WO2023159529A1 - 一种地图数据处理方法及装置 - Google Patents

一种地图数据处理方法及装置 Download PDF

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Publication number
WO2023159529A1
WO2023159529A1 PCT/CN2022/078123 CN2022078123W WO2023159529A1 WO 2023159529 A1 WO2023159529 A1 WO 2023159529A1 CN 2022078123 W CN2022078123 W CN 2022078123W WO 2023159529 A1 WO2023159529 A1 WO 2023159529A1
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WO
WIPO (PCT)
Prior art keywords
risk
information
risk event
vehicle
path
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Ceased
Application number
PCT/CN2022/078123
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English (en)
French (fr)
Inventor
费雯凯
杨淼
刘建琴
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Huawei Technologies Co Ltd
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Huawei Technologies Co Ltd
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Filing date
Publication date
Application filed by Huawei Technologies Co Ltd filed Critical Huawei Technologies Co Ltd
Priority to PCT/CN2022/078123 priority Critical patent/WO2023159529A1/zh
Priority to EP22927812.2A priority patent/EP4459231A4/en
Priority to CN202280089756.0A priority patent/CN118575056A/zh
Publication of WO2023159529A1 publication Critical patent/WO2023159529A1/zh
Priority to US18/812,616 priority patent/US20240410708A1/en
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/3453—Special cost functions, i.e. other than distance or default speed limit of road segments
    • G01C21/3461—Preferred or disfavoured areas, e.g. dangerous zones, toll or emission zones, intersections, manoeuvre types or segments such as motorways, toll roads or ferries
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/3407—Route searching; Route guidance specially adapted for specific applications
    • G01C21/3415—Dynamic re-routing, e.g. recalculating the route when the user deviates from calculated route or after detecting real-time traffic data or accidents
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/36—Input/output arrangements for on-board computers
    • G01C21/3667—Display of a road map
    • G01C21/367—Details, e.g. road map scale, orientation, zooming, illumination, level of detail, scrolling of road map or positioning of current position marker
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G01C21/34—Route searching; Route guidance
    • G01C21/36—Input/output arrangements for on-board computers
    • G01C21/3691—Retrieval, searching and output of information related to real-time traffic, weather, or environmental conditions
    • G01C21/3694—Output thereof on a road map
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/38—Electronic maps specially adapted for navigation; Updating thereof
    • G01C21/3885—Transmission of map data to client devices; Reception of map data by client devices
    • G01C21/3889—Transmission of selected map data, e.g. depending on route
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/38—Electronic maps specially adapted for navigation; Updating thereof
    • G01C21/3863—Structures of map data
    • G01C21/387—Organisation of map data, e.g. version management or database structures
    • G01C21/3878—Hierarchical structures, e.g. layering

Definitions

  • the present application relates to the field of maps, in particular to a map data processing method and device.
  • map technology has brought great convenience to people in the field of travel.
  • route planning can be realized through maps to provide navigation information for travel, or accidents or congested road sections can be displayed through maps to facilitate travelers to avoid congested or accident road sections and plan new routes.
  • future intelligent driving and intelligent transportation put forward higher requirements for the richness of map information, and the richness of the existing map content cannot fully meet the needs of future use.
  • the present application discloses a map data processing method and device, which can provide forecast information of risk events that may occur within a certain geographical area, and improve the richness of map information.
  • the present application provides a map data processing method, the method includes: acquiring risk event information, the risk event information includes time information and location information, the time information is used to indicate the predicted time range of the risk event, and the location information is used to Indicates the predicted area range of risk events; stores risk event information as map data.
  • the risk event refers to an event that affects the driving (or driving) safety or travel smoothness of the vehicle, or in other words, the risk event is an event that has a potential impact on the driving (or driving) safety or smooth travel of the vehicle.
  • the forecast time range is the time range in which risk events may occur, not necessarily the time range in which risk events actually occur.
  • the predicted area is the area where the risk event may occur, not necessarily the area where the risk event actually occurs.
  • the network side device may be, for example, a server deployed on the network side (such as an application server or a map server), or a component or a chip in the server.
  • the network side device may be deployed in a cloud environment or an edge environment, which is not specifically limited in this embodiment of the present application.
  • Roadside equipment can be, for example, devices such as Road Side Unit (Road Side Unit, RSU), Multi-Access Edge Computing (Multi-Access Edge Computing, MEC) or sensors, or components or chips inside these devices, or can be composed of A system-level device composed of RSU and MEC, or a system-level device composed of RSU and sensors, or a system-level device composed of RSU, MEC and sensors.
  • the terminal can be any device such as a vehicle, a portable mobile device (such as a mobile phone, a tablet, etc.), or can be a device, a component or a chip in any of the above devices, such as an on-board unit (On Board Unit, OBU),
  • OBU on Board Unit
  • the acquired risk event information can provide forecast information of risk events that may occur within a certain geographical area, for example, the forecast time range of the risk event and the forecast area range of the risk event. Storing risk event information as map data can not only increase the richness of map data, but also enable the map data to meet more abundant use requirements.
  • the method further includes: planning a minimum risk path according to risk event information.
  • the lowest risk path enriches the user's travel strategy, and is also conducive to improving the safety of vehicle travel.
  • the method further includes: recommending the lowest risk path to the user, or controlling the vehicle to travel along the lowest risk path.
  • the lowest risk route provides another option for the user's travel strategy. Control the vehicle to travel along the lowest risk path, so that the vehicle can travel more safely.
  • the method further includes: recommending a plurality of routes to the user, the plurality of routes including the lowest risk route; receiving feedback information from the user, the feedback information is used to indicate the route selected by the user from the plurality of routes; controlling the vehicle along the selected path travel.
  • the multiple paths can also include at least one of the least time-consuming path, the shortest distance path, the least walking path, and the most shady path, which greatly enriches the user's travel strategy. Users can also Freely choose the favorite path according to your actual needs, which improves the user's sense of interactive experience.
  • planning the minimum risk path according to the risk event information includes: planning the minimum risk path of the vehicle according to the risk event information and the perception ability of the vehicle; planning the minimum risk path of the vehicle according to the risk event information and the vehicle type of the vehicle; or According to the risk event information and the level of automatic driving of the vehicle, the minimum risk path of the vehicle is planned.
  • the vehicle can effectively perceive the surrounding environment through its own sensing elements (or called on-board sensors).
  • the perception ability of the vehicle includes the perception of the vehicle and the perception of the environment.
  • the devices used to perceive the vehicle include sensors in power, chassis, body and electronic and electrical systems, etc.
  • the devices used to sense the environment include on-board cameras, millimeter-wave radars, and lidars.
  • the perception components of different vehicle configurations are different, resulting in different perception capabilities of different vehicles, so vehicles with different perception capabilities may have different responses to the same risk event.
  • the sensor installed in a vehicle has a low perception ability in a dark environment, and according to the risk event information, it is predicted that a road segment is more risky to drive at night, then when planning a night travel route for the vehicle, try to avoid the risk event information. of this road section.
  • Vehicle types can be classified in various ways, for example, internal combustion engine vehicles, electric vehicles, jet vehicles, etc., based on the type of power plant. Based on the size of the space, it can be divided into mini cars, small cars, compact cars, medium cars, medium and large cars, etc. In some possible embodiments, other forms of division are also possible, and this application does not specifically limit the manner of vehicle type division. Different types of vehicles may respond differently to the same risk event.
  • a road section has a steep slope and it is expected to snow in a certain period of time, according to the risk event information, it is predicted that the road section will be more risky to drive in this period of time, then when planning a car model with poor climbing ability When traveling on the route, try to avoid the road section indicated by the risk event information.
  • the level of automatic driving can be divided into six levels, the levels from low to high are L0 (no automatic driving), L1 (driving support), L2 (partial automatic driving), L3 (conditional automatic driving), L4 (highly automatic driving ) and L5 (fully autonomous driving).
  • the higher the level the stronger the autonomous driving capability of the vehicle.
  • the vehicle is at different levels of autonomous driving, its ability to respond to the same risk event may be different. For example, according to the risk event information, it is predicted that a certain section of the road in a certain period of time has a high driving risk due to the large flow of people.
  • the risk event information it is predicted that a certain section of the road in a certain period of time has a high driving risk due to the large flow of people.
  • the lowest risk path is: a path without risk events; a path without risk events with a risk level exceeding a risk threshold; or a path without risk events of a specific risk type.
  • the lowest risk path is the path of no risk event, which can be understood as the area that the lowest risk path passes through within a certain time range is the area of no risk event.
  • the lowest risk path is the path with no risk event whose risk level exceeds the risk threshold. It can be understood that there are predicted risk events in the area that the lowest risk path passes within a certain time range, but the risk level of the risk event does not exceed the risk threshold (that is, the risk level is considered The larger the value of , the more dangerous the risk event).
  • the lowest risk path is a path of risk events without a specific risk type. It can be understood that there are predicted risk events in the area that the lowest risk path passes within a certain time range, but the risk type of the risk event is not a specific risk type.
  • planning the lowest risk path according to the risk event information includes: planning multiple paths; determining that the predicted area range includes the location points in the multiple paths; estimating that the vehicle will travel to the location point at the expected time; The time information and position information determine the forecast time range corresponding to the forecast area; determine the forecast time range including the estimated time; determine the driving risk of at least one of the multiple routes according to the risk event; determine the lowest of the multiple routes according to the driving risk risk path.
  • the lowest risk path is the path with the lowest driving risk among the multiple paths.
  • the determination of the lowest risk path needs to combine the two dimensions of space and time, that is, when planning the path, it is necessary to comprehensively consider each location point in the path and the estimated time when the vehicle arrives at each location point, so that the location point corresponding to the estimated time is not a risk as far as possible
  • the estimated time for the vehicle-end equipment to arrive at points A, B, and C is also comprehensively considered. For example, if it is estimated to arrive at point A at time t1, point B at time t2, and point C at time t3, then respectively Use the risk event information near point A at time t1, the risk event information near point B at time t2, and the risk event information near point C at time t3 to plan the route.
  • acquiring risk event information includes: generating risk event information, or receiving risk event information.
  • the execution subject of the above method may be a network-side device, a road-side device or a terminal, such as a map server, a component or chip in a map server, a road-side device, a vehicle or a terminal.
  • a network side device for example
  • roadside equipment that uses risk event information to provide roadside instruction information
  • vehicles that use risk event information for driving portable terminals (such as mobile phones, laptop computers or navigators), and for example Components, chips or applications applicable to the aforementioned devices.
  • the method is used in a vehicle, or the method further includes sending risk event information to the vehicle, and the vehicle meets at least one of the following conditions:
  • the vehicle is within the predicted area
  • the minimum distance between the vehicle and the predicted area range is less than the first threshold
  • the predicted area range intersects with the planned path of the vehicle
  • the minimum distance between the predicted area range and the planned path of the vehicle is less than a second threshold
  • the tile to which the prediction area belongs is the tile where the vehicle is located.
  • the tiles to which the prediction area belongs are the tiles that the planned path of the vehicle passes through.
  • the vehicle can obtain and store information within a certain geographical range or related to the navigation route based on the current location or its own planned route.
  • the map data of the region can be used to obtain and store the risk event information of the local area that is more relevant to itself. In this way, the storage space of the map data in the vehicle can be saved, and the efficiency of the vehicle to use the risk event information can be improved.
  • the vehicle when the risk event information is sent to the vehicle, it means that the vehicle can also be used as the user of the risk event information.
  • the traffic of data transmission can be saved, and the storage space of map data in the vehicle can be saved for vehicles using risk event information, and the use of risk event information by vehicles can be improved. s efficiency.
  • the range of the prediction area is located on a road or a lane in the map.
  • the geometric expression at the road level or the geometric expression at the lane level may be performed on the predicted area range.
  • the risk event information further includes at least one of the following: identification information of the risk event, identification information of the tile to which the risk event belongs, identification information of the road where the risk event is located, risk level information, risk type information, Early warning information, identification information of dynamic elements affecting risk events, and information of map elements affected by risk events; among them, risk level information is used to indicate the degree of danger of risk events, risk type information is used to indicate the type of risk events, early warning information It is used to indicate the content to remind the driver or driving system based on the risk event.
  • the risk level information is an optional information of the risk event information. Stepwise quantification of the risk degree of risk events based on risk level information can effectively distinguish risk events of different risk levels, which is conducive to prioritization.
  • the risk type information is an optional information of the risk event information. Based on the risk type information, the type of risk that may be faced can be quickly identified, so that risk events can be better dealt with. For example, risk type information can classify and describe risk events from the causes of risk events, or classify and describe risk events from the results of risk events. limited.
  • the identification information of the dynamic elements that affect the risk event is an optional information of the risk event information. Based on the identification information of the dynamic elements affecting the risk events, the dynamic elements associated with the risk events can be quickly indexed, and when the associated dynamic elements are detected to be changed, the risk event information can be quickly updated in linkage. For example, a traffic jam event is a dynamic element that is likely to cause a vehicle crash, and the identification of the traffic jam event is used as the content of the risk event information about the vehicle crash.
  • the information of the map elements affected by the risk event is an optional information of the risk event information. Based on the information of the map elements affected by the risk event, the corresponding map elements can be quickly indexed, and when the risk event changes, the linkage update of the information of the map elements affected by the risk event can be realized, which not only improves the accuracy of the map data, but also improves the accuracy of the map data. Improved update efficiency of map data.
  • Early warning information is an optional information of risk event information. Based on the early warning information, the driver can be reminded of the risk events in the front area in time, which is conducive to improving the safety of the vehicle.
  • the identification information of the risk event, the identification information of the tile to which the risk event belongs, and the identification information of the road where the risk event is located are all optional information of the risk event information.
  • the method further includes: determining that elements affecting risk events have changed, and the elements are located on a static layer or a dynamic layer of the map; updating risk event information or eliminating risk event information according to the changed elements.
  • the linked update of the risk event information can be realized, or the risk event information can be eliminated.
  • the elimination of risk event information means that it is predicted that the corresponding risk event will not occur.
  • the method further includes: performing road condition monitoring, traffic dispatching, path planning or vehicle control according to the risk event information.
  • Implementing the above implementation methods can provide a variety of application services based on risk event information, such as macroscopic control services such as road condition monitoring and traffic scheduling, as well as personalized and customized services such as route planning and vehicle control.
  • risk event information such as macroscopic control services such as road condition monitoring and traffic scheduling, as well as personalized and customized services such as route planning and vehicle control.
  • the method further includes: determining that the vehicle is about to pass through the predicted area where the risk event is located; controlling the vehicle to perform at least one of the following operations: changing lanes; adjusting the driving speed; updating the navigation route; Alerting staff of risk events.
  • the method further includes: determining a road area associated with the predicted area range according to road topology information in the map; prompting risk events to vehicles in the road area, or controlling traffic flow in the road area.
  • the association between the road area and the prediction area includes but is not limited to: the distance between the road area and the prediction area is less than or equal to the preset distance threshold; the traffic between the road area and the prediction area is reachable; or the vehicle in the road area is expected to arrive
  • the moment in the forecast area where the risk event is located belongs to the forecast time range corresponding to the risk event.
  • the road area may be one or more lanes, or one or more roads, or a set of at least one lane and at least one road.
  • risk event reminders can be given to vehicles within a certain geographical area in advance, and the traffic flow within the corresponding geographical area can also be controlled to achieve traffic scheduling. Avoid traffic accidents.
  • the method further includes presenting risk event information on a map display interface through at least one of the following manners.
  • the changes of risk events are dynamically played on the interface; implementing this method, users can accurately know the change trend of risk events in the nearby area in advance, so that they can adjust their coping strategies in time and improve their own security.
  • the predicted area range of at least one risk event and the description information of at least one risk event whose risk level exceeds the threshold at the current time implement this method, by limiting the risk level of the marked risk event to filter out the non-compliance Risk events with threshold conditions, so that users can focus on risk events whose risk level exceeds the threshold.
  • the risk events near the position closest to the current position in the navigation route may be preferentially displayed.
  • risk events of different risk levels can be effectively distinguished based on color, and the distribution of risk events of different risk levels in the map is also intuitively displayed.
  • risk events of different risk types can be effectively distinguished based on color, and the distribution of risk events of different risk types in the map is also intuitively displayed.
  • the map data includes first static layer data and first dynamic layer data, and the risk event information is obtained according to the first static layer data and the first dynamic layer data.
  • map data related to risk events may be extracted from the first static layer data and the first dynamic layer data.
  • the first static layer data can indicate the road status that does not change frequently, such as road type, number of lanes, road surface pothole information (for example, depth, location information, area, etc.);
  • the first dynamic layer data for example, can indicate Frequently changing weather conditions such as precipitation, snowfall, visibility, light intensity, wind direction, wind force, lightning index, etc., can also indicate road construction information (for example, whether there is construction, construction location, construction duration, etc.), road covering information (For example, the thickness of ice accumulation, the depth of water accumulation, the amount of fallen leaves) and other road conditions that change frequently.
  • the static layer data and dynamic layer data in the map data provide the possibility to obtain risk event information, and the risk event information increases the richness of the map data.
  • storing the risk event information as map data includes: storing the risk event information as second dynamic layer data of the map data.
  • the risk event information can be expressed in the form of a dynamic layer of the map, and the dynamic layer carrying the risk event information can be displayed separately, or can be combined with at least one other layer in the map (for example, a static layer, Dynamic layers that only carry weather information, etc.) superimposed display.
  • the risk event information is stored in a data structure corresponding to the risk event identifier. Based on the identification of risk events, the corresponding risk events can be quickly indexed in the map.
  • the risk event information is stored in a data structure corresponding to the forecast time range.
  • the risk event information corresponding to any forecast time range can be provided to the user, and the delivery of risk event information based on the forecast time range can improve data transmission efficiency.
  • the present application provides a map data processing device, which includes: an acquisition unit for acquiring risk event information, the risk event information includes time information and location information, and the time information is used to indicate the predicted time range of the risk event , the location information is used to indicate the predicted area range of the risk event; the storage unit is used to store the risk event information as map data.
  • the device further includes: a planning unit, configured to plan the lowest risk path according to the risk event information.
  • a planning unit configured to plan the lowest risk path according to the risk event information.
  • the device further includes: a first processing unit, configured to recommend a path with the lowest risk to the user, or control the vehicle to travel along the path with the lowest risk.
  • a first processing unit configured to recommend a path with the lowest risk to the user, or control the vehicle to travel along the path with the lowest risk.
  • the device further includes: a second processing unit, configured to recommend multiple paths to the user, the multiple paths including the lowest-risk path; a receiving unit, configured to receive feedback information from the user, and the feedback information is used to instruct the user to choose from multiple paths. the path selected from the paths; the second processing unit is also used to control the vehicle to travel along the selected path.
  • a second processing unit configured to recommend multiple paths to the user, the multiple paths including the lowest-risk path
  • a receiving unit configured to receive feedback information from the user, and the feedback information is used to instruct the user to choose from multiple paths. the path selected from the paths
  • the second processing unit is also used to control the vehicle to travel along the selected path.
  • the planning unit is specifically configured to: plan the minimum risk path of the vehicle according to the risk event information and the perception capability of the vehicle; plan the minimum risk path of the vehicle according to the risk event information and the vehicle type of the vehicle; or plan the minimum risk path of the vehicle according to the risk event information and the vehicle type.
  • the lowest risk path is: a path without risk events; a path without risk events with a risk level exceeding a risk threshold; or a path without risk events of a specific risk type.
  • the planning unit is specifically used to: plan multiple paths; determine that the predicted area range includes the location points in the multiple paths; estimate that the vehicle will drive to the location point at the expected time; according to the time information and location information in the risk event information Determine the forecast time range corresponding to the range of the forecast area; determine the forecast time range includes the estimated time; determine the driving risk of at least one of the multiple routes according to the risk event; determine the lowest risk route among the multiple routes according to the driving risk.
  • the acquiring unit is specifically configured to: generate risk event information, or receive risk event information.
  • the device is a vehicle, or, the device further includes a sending unit that sends risk event information to the vehicle, and the vehicle meets at least one of the following conditions:
  • the vehicle is within the predicted area
  • the minimum distance between the vehicle and the predicted area range is less than the first threshold
  • the predicted area range intersects with the planned path of the vehicle
  • the minimum distance between the predicted area range and the planned path of the vehicle is less than a second threshold
  • the tile to which the prediction area belongs is the tile where the vehicle is located.
  • the tiles to which the prediction area belongs are the tiles that the planned path of the vehicle passes through.
  • the range of the prediction area is located on a road or a lane in the map.
  • the risk event information further includes at least one of the following: identification information of the risk event, identification information of the tile to which the risk event belongs, identification information of the road where the risk event is located, risk level information, risk type information, Early warning information, identification information of dynamic elements affecting risk events, and information of map elements affected by risk events; among them, risk level information is used to indicate the degree of danger of risk events, risk type information is used to indicate the type of risk events, early warning information It is used to indicate the content to remind the driver or driving system based on the risk event.
  • the device further includes a third processing unit, configured to: determine that elements affecting risk events have changed, and the elements are located on a static layer or a dynamic layer of the map; update risk event information or eliminate risk events according to changed elements information.
  • a third processing unit configured to: determine that elements affecting risk events have changed, and the elements are located on a static layer or a dynamic layer of the map; update risk event information or eliminate risk events according to changed elements information.
  • the device further includes a fourth processing unit, configured to: perform road condition monitoring, traffic scheduling, route planning, or vehicle control according to risk event information.
  • a fourth processing unit configured to: perform road condition monitoring, traffic scheduling, route planning, or vehicle control according to risk event information.
  • the device further includes a fifth processing unit, configured to: determine that the vehicle is about to pass through the predicted area where the risk event is located; control the vehicle to perform at least one of the following operations: change lanes; adjust driving speed; update the navigation route; Turning on the warning light; and prompting the driver of the risk event.
  • a fifth processing unit configured to: determine that the vehicle is about to pass through the predicted area where the risk event is located; control the vehicle to perform at least one of the following operations: change lanes; adjust driving speed; update the navigation route; Turning on the warning light; and prompting the driver of the risk event.
  • the device further includes a sixth processing unit, configured to: determine the road area associated with the predicted area range according to the road topology information in the map; prompt risk events to vehicles in the road area, or control vehicles in the road area flow.
  • a sixth processing unit configured to: determine the road area associated with the predicted area range according to the road topology information in the map; prompt risk events to vehicles in the road area, or control vehicles in the road area flow.
  • the device further includes a display unit configured to present risk event information on the map display interface in at least one of the following ways:
  • the map data includes first static layer data and first dynamic layer data, and the risk event information is obtained according to the first static layer data and the first dynamic layer data.
  • the storage unit is specifically configured to: store the risk event information as the second dynamic layer data of the map data.
  • the risk event information is stored in a data structure corresponding to the risk event identifier.
  • the risk event information is stored in a data structure corresponding to the forecast time range.
  • the present application provides an electronic map, the electronic map includes risk event information, the risk event information includes time information and location information, the time information is used to indicate the predicted time range of the risk event, and the location information is used to indicate the time range of the risk event Forecast area extent.
  • the electronic map is a map product, specifically, it can be a map data product carrying risk event information, such as a map update data package, or it can be a map application product loaded with risk event information, such as being installed on a vehicle or a portable terminal map applications, or map display products that present risk event information in graphic and/or text form, such as electronic navigators.
  • risk event information such as a map update data package
  • map application product loaded with risk event information such as being installed on a vehicle or a portable terminal map applications
  • map display products that present risk event information in graphic and/or text form, such as electronic navigators.
  • the risk event information is stored in an event data structure in the electronic map.
  • the risk event information is stored as dynamic layer data in the electronic map.
  • the risk event information further includes at least one of the following: identification information of the risk event, identification information of the tile to which the risk event belongs, identification information of the road where the risk event is located, risk level information, and risk type information , early warning information, identification information of dynamic elements affecting risk events, and information of map elements affected by risk events; among them, risk level information is used to indicate the degree of danger of risk events, risk type information is used to indicate the type of risk events, early warning The information is used to indicate the contents to be reminded to the driver or the driving system based on the risk event.
  • the present application provides a map data processing device, which includes at least one processor and a communication interface, and the communication interface is used to provide information input and/or output for the at least one processor.
  • the device is used to implement the first aspect or the method in any possible embodiment of the first aspect.
  • the map data processing device may be a network-side device, a road-side device or a terminal.
  • the network side device may be, for example, a server deployed on the network side (such as an application server or a map server), or a component or a chip in the server.
  • the network side device may be deployed in a cloud environment or an edge environment, which is not specifically limited in this embodiment of the present application.
  • Roadside equipment can be, for example, devices such as Road Side Unit (Road Side Unit, RSU), Multi-Access Edge Computing (Multi-Access Edge Computing, MEC) or sensors, or components or chips inside these devices, or can be composed of A system-level device composed of RSU and MEC, or a system-level device composed of RSU and sensors, or a system-level device composed of RSU, MEC and sensors.
  • the terminal can be any device such as a vehicle, a smart wearable device (such as a sports bracelet, a watch, etc.), a portable mobile device (such as a mobile phone, a tablet, etc.), or it can be a device, a component or a device in any of the above-mentioned devices. Chips, such as On Board Unit (OBU).
  • OBU On Board Unit
  • the map data processing device may be the generating end of the above-mentioned electronic map, or the end of using the above-mentioned electronic map, which is not specifically limited here.
  • the present application provides a computer-readable storage medium, including computer instructions.
  • the computer instructions When the computer instructions are executed by a processor, the above-mentioned first aspect or any possible implementation of the first aspect can be realized. method.
  • the present application provides a computer program product.
  • the computer program product When the computer program product is executed by a processor, the method in the above-mentioned first aspect or any possible embodiment of the first aspect is implemented.
  • the computer program product can be, for example, a software installation package. If the method provided by any possible design of the first aspect above needs to be used, the computer program product can be downloaded and executed on the processor. , so as to implement the first aspect or the method in any possible embodiment of the first aspect.
  • the present application provides electronic information, which carries risk event information.
  • the risk event information includes time information and location information.
  • the time information is used to indicate the predicted time range of the risk event
  • the location information is used to indicate the risk event. range of the forecast area.
  • the electronic information is a collection of electrical, magnetic or electromagnetic signals
  • map information is carried in the form of electrical, magnetic or electromagnetic carriers.
  • a computer-readable storage medium has an information input interface, and the information input interface can receive the electronic information described in the seventh aspect or any possible implementation of the seventh aspect. information, and store the risk event information carried by the electronic information in the computer-readable storage medium.
  • the present application provides a vehicle, the vehicle includes the map data processing device according to the second aspect or any possible implementation manner of the second aspect above, or includes the map data processing device according to the fourth aspect or any possible implementation manner of the fourth aspect above A map data processing device in a possible implementation manner.
  • the present application provides a system, which includes a first map data processing device and a second map data processing device.
  • the first map data processing device is used to execute the map data processing method in the above first aspect or any possible implementation of the first aspect when acquiring risk event information is generating risk event information;
  • the second map data processing device is configured to execute the map data processing method in the above first aspect or any possible implementation of the first aspect when acquiring risk event information is receiving risk event information.
  • FIG. 1 is a schematic diagram of a scene provided by an embodiment of the present application.
  • FIG. 2 is a schematic diagram of a system architecture provided by an embodiment of the present application.
  • Fig. 3 is a flowchart of a method for processing map data provided by an embodiment of the present application
  • Fig. 4 is a schematic representation of the range of prediction regions of some risk events provided by the embodiment of the present application.
  • FIG. 5A is a schematic diagram of an expression of risk event information provided by the embodiment of the present application.
  • FIG. 5B is a schematic diagram of another expression of risk event information provided by the embodiment of the present application.
  • FIG. 6 is a schematic diagram of another expression of risk event information provided by the embodiment of the present application.
  • FIG. 7 is a schematic representation of risk event information in different forecast periods provided by the embodiment of the present application.
  • Fig. 8 is a flow chart of another map data processing method provided by the embodiment of the present application.
  • Fig. 9 is a flow chart of another map data processing method provided by the embodiment of the present application.
  • FIG. 10 is a schematic interface diagram of a route recommendation provided by an embodiment of the present application.
  • Fig. 11 is a schematic diagram of an interface of a display device provided by an embodiment of the present application.
  • FIG. 12 is a schematic diagram of a map display interface provided in this embodiment of the present application.
  • FIG. 13 is a schematic diagram of the functional structure of a map data processing device provided in this embodiment of the present application.
  • FIG. 14 is a schematic diagram of the functional structure of a map data processing device provided in this embodiment of the present application.
  • FIG. 15 is a schematic structural diagram of another map data processing device provided in this embodiment of the present application.
  • the number of described objects is not limited by the prefixes, and may be one or more. Taking “the first device” as an example, the number of “device” may be one or more.
  • the objects modified by different prefixes can be the same or different, for example, if the described object is "equipment”, then “first equipment” and “second equipment” can be the same equipment, the same type of equipment or different types of equipment ; For another example, if the described object is "information”, then “first information” and “second information” may be information of the same content or information of different content.
  • the use of prefixes used to distinguish the described objects in the embodiments of the present application does not constitute a restriction on the described objects. For the description of the described objects, please refer to the claims or the description of the context in the embodiments. It should not be because of the use of such prefixes constitute redundant restrictions.
  • a description such as "at least one (or at least one) of a1, a2, ... and an” is used, including any one of a1, a2, ... and an.
  • the case of being alone also includes the case of any combination of any number of a1, a2, ... and an, and each case can exist alone.
  • the description of "at least one of a, b, and c" includes a alone, b alone, c alone, a combination of a and b, a combination of a and c, a combination of b and c, or a combination of abc Condition.
  • Maps are carriers of geographic information.
  • a map includes multiple layers (Layers), and a layer may be understood as a map data set, and data in the map data set is organized in a set data structure.
  • Data in layers can describe map features from a variety of sources.
  • map elements can be divided into two types: elements and events: elements are map elements that are relatively fixed, change little, or have a long update cycle, such as road topology, building location, lane line, lane direction or traffic Infrastructure layout, etc.; events are map elements with strong time-varying characteristics, such as traffic accidents, weather changes, icy roads, road construction, or traffic congestion.
  • elements and events can be recorded in different layers, for example, information about elements is carried by a static layer in the map, and information about events is carried by a dynamic layer in the map.
  • the map may include one or more static layers, and may further include one or more dynamic layers.
  • a map includes a static layer and multiple dynamic layers.
  • the static layer records the geographical distribution of buildings, roads, trees, traffic lights and road signs
  • the dynamic layer 1 records the real-time speed limit of the lane.
  • the dynamic layer 2 records the weather conditions, such as sunny, rainy, snowy, windy, temperature or humidity.
  • it may have both time-varying map elements and time-invariant map elements.
  • the non-time-varying map elements refer to map elements that are relatively fixed, change little, or have a long update cycle, that is, This description object is related to both elements in the map and events in the map.
  • This description object is related to both elements in the map and events in the map.
  • the geographic location of the lane is an element in the map
  • the traffic flow of the lane is an event in the map
  • the speed limit of the lane is an event in the map
  • the allowed time period of the lane is an event in the map event.
  • the position of the traffic light in the intersection is an element in the map
  • the lighting change of the traffic light is an event in the map.
  • Data in a map's static layers can be called elements or static features
  • data in a map's dynamic layers can be called events or dynamic features.
  • a risk event refers to an event that affects the driving (or driving) safety or smooth travel of the vehicle, or in other words, a risk event has a potential impact on the driving (or driving) safety or smooth travel of the vehicle event.
  • Each risk event has corresponding time information and location information, the time information is used to indicate the predicted time range of the risk event, and the location information is used to indicate the predicted area range of the risk event.
  • the forecast time range is the time range in which the predicted risk event may occur, not necessarily the time range in which the risk event actually occurs.
  • the predicted area is the area where the predicted risk event may occur, not necessarily the area where the risk event actually occurs.
  • the risk event is a risk event that is predicted to have the possibility of occurrence, and may or may not actually occur; in addition, the forecast time range can be a future time range, which can provide travelers with advance risk event information and use for better travel planning; or the forecast time horizon can include the current point in time and a time horizon in the future, and can even include past time points.
  • risk event 1 indicates that area A in the map is at risk during the time period 9:00-10:00, where 9:00-10:00 is the forecast time range of risk event 1, and area A is risk event 1 range of the forecast area.
  • risk events are of different types, for example, lane change collision, skidding collision, reduced visibility, reduced braking distance, and the like. It can be understood that different types of risk events correspond to different risks.
  • the road network structure data in the map can be divided into tile level, road level and lane level.
  • Each tile in the map has a unique tile identification (Identification, ID), and each tile includes multiple roads.
  • Each road has a unique road ID, and each road includes multiple lanes, and each lane has a unique lane ID.
  • a tile can be understood as a rectangular grid image that cuts a map within a certain range into several rows and columns according to a certain size and format, and different map resolutions.
  • the sliced rectangular grid image is called Tile.
  • FIG. 1 is a schematic diagram of an application scenario of an embodiment of the present application.
  • the on-board map prompts that there is an accident-prone area 1 in road section BC, please detour as much as possible, so the vehicle determines
  • the driving route is A ⁇ B ⁇ E ⁇ F ⁇ C ⁇ D, which takes half an hour longer than the route A ⁇ B ⁇ C ⁇ D.
  • the probability of accidents in area 1 is relatively high only during a specific period of the day from 9:30 to 11:00, while area 1 is safe during other periods of the day, that is to say, at the current moment it is 14:00
  • the optimal path planning of the vehicle should be path A ⁇ B ⁇ C ⁇ D. It can be seen from this that due to the lack or imperfection of map information, the vehicle cannot make optimal driving decisions, which reduces the accuracy of driving decisions and leads to a decrease in vehicle travel efficiency.
  • the embodiment of the present application proposes a map data processing method, which can provide vehicles with forecast information of risk events that may occur within a certain geographical area, so that vehicles can obtain the corresponding forecast time range and forecast area range of risk events , which is conducive to improving the safety of driving and the accuracy of driving decisions, thereby improving the efficiency of vehicle travel.
  • FIG. 2 exemplarily shows a system architecture diagram of an embodiment of the present application.
  • the system is used for generating or using an electronic map, and the electronic map includes risk event information, and the risk event information includes risk event location information and risk event time information.
  • the system includes at least one of network side equipment, roadside equipment and terminals.
  • the terminal can communicate with the network-side device and the roadside device respectively in a wireless manner, and the network-side device and the roadside device can communicate with the roadside device in a wireless or wired manner.
  • the electronic map can be generated by any one of network side equipment, roadside equipment or terminals.
  • the network side device may be a device with a computing function, for example, it may be a server (such as an application server or a map server) deployed on the network side, or a component or chip in the server.
  • Network-side devices can be deployed in the cloud environment, that is, cloud computing servers, or network-side devices can also be deployed in edge environments, that is, edge computing servers.
  • the network side device may be one integrated device, or multiple distributed devices, which is not specifically limited in this embodiment of the present application.
  • Roadside equipment includes devices such as Road Side Unit (Road Side Unit, RSU), Multi-Access Edge Computing (Multi-Access Edge Computing, MEC) or sensors, for example, it can be RSU, MEC or sensors, or it can be composed of RSU and A system composed of MEC, or a system composed of RSU and sensors, or a system composed of RSU, MEC and sensors.
  • RSU Road Side Unit
  • MEC Multi-Access Edge Computing
  • a terminal can be any device such as a vehicle, a smart wearable device (such as a sports bracelet, a watch, etc.), a portable mobile device (such as a mobile phone, a tablet, etc.), or it can be a device or a component in any of the above-mentioned devices Or a chip, such as an on-board unit (On Board Unit, OBU), which is not specifically limited in this embodiment of the present application.
  • OBU On Board Unit
  • the network-side device can obtain risk event information based on the basic map (including static layers and dynamic layers) and artificial intelligence models.
  • the artificial intelligence model is obtained by training based on historical accident data.
  • Historical accident data includes historical accident occurrence time, accident area, risk level, environmental information of the accident area during the occurrence time period, and road state information of the accident area.
  • the network-side device may also Update the risk event information according to the changed elements.
  • the base map may be a high-definition map, a standard precision map, or other types of maps, which are not specifically limited in this embodiment of the present application.
  • the data source device may be, for example, a device provided by a traffic management department to provide traffic road condition data.
  • the network-side device can serve as the publisher of the electronic map, and the terminal or roadside device can serve as the receiver and user of the electronic map.
  • terminals and roadside devices also have information acquisition capabilities and computing capabilities
  • terminals or roadside devices can not only serve as receivers and users of electronic maps, but also as producers or updaters of electronic maps to generate this risk locally. Event information, for its own use or to send to other devices.
  • the terminal can be equipment or devices such as vehicles and on-board units (On Board Unit, OBU).
  • OBU On Board Unit
  • the above-mentioned basic map is stored in the terminal.
  • the terminal Based on its own current location information and/or its own planned trajectory information, the terminal can obtain map data related to risk events at its location or within a certain geographical area from the above basic map, such as environmental prediction information or road status information, combined with manual
  • the smart model generates an electronic map containing risk event information within a certain geographical area for its own use or for other devices to use.
  • the network side device can publish the electronic map to the terminal through a wireless network, such as a cellular communication network; or, the network side device can publish the electronic map to other devices, and the other device forwards it to the terminal. Carried out through V2X (Vehicle to Everything, Internet of Vehicles).
  • V2X Vehicle to Everything, Internet of Vehicles
  • the map server in the cloud publishes electronic maps to portable terminals or vehicles held by pedestrians. It can be released through cellular communication networks including base stations, or forwarded by roadside equipment to portable terminals or vehicles through V2X communication.
  • the producer of the electronic map is a roadside device or terminal, and the roadside device or terminal can publish it through V2X.
  • the risk event is related to the dynamic elements in the surrounding environment, when the state of the associated dynamic element changes, it may trigger the update of the corresponding risk event information.
  • the communication between the network side equipment and the terminal, between the terminal and the roadside equipment, and between the network side equipment and the roadside equipment can use cellular communication technology, such as 2G cellular communication, such as the Global System for Mobile Communications (global system for mobile communication (GSM), general packet radio service (GPRS); or 3G cellular communication, such as wideband code division multiple access (WCDMA), time division synchronous code division multiple access (time division-synchronous code division multiple access, TS-SCDMA), code division multiple access (code division multiple access, CDMA), or 4G cellular communication, such as long term evolution (long term evolution, LTE). Or 5G cellular communication, or other evolved cellular communication technologies.
  • 2G cellular communication such as the Global System for Mobile Communications (global system for mobile communication (GSM), general packet radio service (GPRS); or 3G cellular communication, such as wideband code division multiple access (WCDMA), time division synchronous code division multiple access (time division-synchronous code division multiple access, TS-SCDMA), code division multiple access (code division multiple access
  • the wireless communication system may also utilize non-cellular communication technologies, such as Wi-Fi and wireless local area network (wireless local area network, WLAN) communication.
  • the communication between the above devices can also use infrared link, bluetooth or ZigBee for direct communication.
  • other wireless protocols may be used for communication between the above devices, such as various vehicle communication systems, for example, the system may include one or more dedicated short range communications (DSRC) devices, these devices It may include public and/or private data communication between vehicles and/or roadside stations, which is not specifically limited in this application.
  • DSRC dedicated short range communications
  • FIG. 2 is only an exemplary architecture diagram, but does not limit the number of network elements included in the system shown in FIG. 2 .
  • FIG. 2 may also include other functional entities.
  • the method provided in the embodiment of the present application can be applied to the communication system shown in FIG. 2 , and of course the method provided in the embodiment of the present application can also be applied to other communication systems, which is not limited in the embodiment of the present application.
  • FIG. 3 is a flow chart of a method for processing map data provided by an embodiment of the present application, which can be applied to the system architecture described above.
  • the method includes but is not limited to the following steps:
  • the risk event information includes time information and location information, wherein the time information is used to indicate the predicted time range of the risk event, and the location information is used to indicate the predicted area range of the risk event.
  • acquiring risk event information specifically includes: generating risk event information.
  • the method described in the embodiment of FIG. 3 can be used to generate a map including risk event information, and the method includes but is not limited to a device on the network side (for example, server side), roadside device or terminal side , component, chip, software module or hardware module, and the device on the terminal side includes but not limited to a vehicle or a portable terminal.
  • acquiring risk event information specifically includes: receiving risk event information.
  • the method described in the embodiment of FIG. 3 can be used for the use or storage of maps including risk event information, and the method includes but is not limited to devices, components, chips, A software module or a hardware module is executed, and the device on the terminal side includes but not limited to a vehicle or a portable terminal.
  • the predicted time range of a risk event refers to the time range in which the predicted risk event may occur, not necessarily the time range in which the risk event actually occurs.
  • the forecast time range of a risk event can be a time interval, which can be expressed based on the start time and end time of the risk event, or based on the start time and duration of the risk event, No specific limitation is made here.
  • the forecast time range of a risk event can be within 1 to 2 hours from the current moment, or it can be a specific time period, such as 9:00-10:00 AM.
  • the embodiment of the present application Not specifically limited.
  • the predicted area of a risk event refers to the area where the predicted risk event may occur, not necessarily the area where the risk event actually occurs.
  • the range of the prediction area can be the coordinate value obtained based on any coordinate system, for example, the corresponding three-dimensional coordinates composed of longitude, latitude and altitude in the world geodetic coordinate system (Word Geodetic System 1984, WGS84), or it can be in the natural coordinate system
  • the three-dimensional coordinates composed of X coordinates, Y coordinates and Z coordinates may also be three-dimensional coordinates composed of S coordinates, D coordinates and H coordinates in the road coordinate system, or coordinates in other coordinate systems.
  • the prediction area range of a risk event has the following multiple representations: in a specific implementation, when the prediction area range is a regular shape, one or more parameters relative to the reference point (for example, the starting point of the lane or road) can be passed , such as distance, coordinates, etc.
  • the road-level representation can be performed on the predicted area range, that is, the predicted area range is located on the road in the map, with the reference point as the starting point of the road, (a, b) is the interval expression of a section of the road where the predicted area range is located, Alternatively, represented by the geographic coordinates of the two endpoints of the road segment.
  • the predicted area range may also be represented at the lane level, that is, the predicted area range is located in a lane in the map.
  • the range of the prediction area when the range of the prediction area is an irregular shape, it can be represented by the geographical coordinates of multiple corner points of the irregular shape, or by the smallest circumscribed rectangle or the smallest circumscribed circle of the range of the predicted area Express geometric positions.
  • a schematic diagram of a road-level geometric representation of the predicted area range of a risk event is provided.
  • the starting point of the road can be used as a reference point, and (20m, 50m) means starting from 20 meters away from the reference point to 50 meters away from the reference point cut off at meters.
  • a schematic diagram of a lane-level geometric expression of a predicted area range of a risk event is provided.
  • the starting point of the lane can be used as the reference point, (20m, 60m) means starting from 20 meters away from the reference point to 60 meters away from the reference point cut off at meters.
  • the risk event information further includes at least one of the following: identification information of the risk event, identification information of the tile to which the risk event belongs, identification information of the road where the risk event is located, and risk level information , risk type information, early warning information, identification information of dynamic elements affecting risk events, and information of map elements affected by risk events.
  • the risk event information further includes risk level information, and the risk level information is used to indicate the degree of danger of the risk event.
  • the risk level information is an optional information of the risk event information. The step-by-step quantification of the risk degree of risk events based on risk level information can effectively distinguish risk events with different risk levels, which is conducive to distinguishing priorities and improving the response efficiency of risk events.
  • the division of risk level may be three levels of division, ie high risk, medium risk and low risk.
  • the risk degree classification may also be four-level classification, that is, high risk, medium risk, low risk, and no risk, or other classification methods, which are not specifically limited in this embodiment of the present application.
  • the risk level information may use bit mapping, binary value or other methods to indicate the degree of danger of the risk event. For example, when the risk level information takes the first risk value, it indicates that the risk level of the risk event is high risk; when the risk level information takes the second risk value, it indicates that the risk level of the risk event is medium risk; the risk level information takes the third risk value When , the risk level of the indicated risk event is low risk.
  • Table 1 exemplarily provides a mapping table between a risk value and a risk degree of a risk event. It can be seen from Table 1 that when the risk value is 1, it means that the risk level of the risk event is low risk; when the risk value is 2, it means that the risk level of the risk event is medium risk; The hazard level is high risk. Based on Table 1, it can be seen that the higher the risk value, the more dangerous the risk event is.
  • risk value Dangerous degree of risk event 1 low risk 2 medium risk 3 high risk
  • the risk event information further includes risk type information, and the risk type information is used to indicate the type of the risk event. It should be noted that the risk type information is an optional information of the risk event information. Based on the risk type information, the type of risk that may be faced can be quickly identified, so that risk events can be better dealt with.
  • the types of risk events include but are not limited to lane-changing collisions, turning collisions, skidding collisions, reduced visibility, reduced braking distances, and the like.
  • risk type information can classify and describe risk events from the causes of risk events, such as low visibility risk events, road slippery risk events, or road narrowing risk events; risk events can also be classified from the results of risk events.
  • Categorical descriptions such as congestion risk events, scratch risk events, rear-end collision risk events, cliff fall risk events, or climbing difficulty risk events. There are many ways of specific classification, which are not limited in this embodiment of the present application.
  • the risk event information further includes identification information of dynamic elements that affect the risk event.
  • identification information of the dynamic elements affecting the risk event is an optional information of the risk event information.
  • the dynamic elements associated with risk events can be quickly indexed.
  • the risk event information can be updated in time based on the changed dynamic elements, which is conducive to improving the risk event information. accuracy.
  • the impact of a risk event by a dynamic element includes but is not limited to: the dynamic element causes the generation of the risk event, or the dynamic element will aggravate or reduce the degree of danger of the risk event. It should be noted that since the risk event has a corresponding forecast time range, it means that the risk event is affected by the dynamic element within the forecast time range, and whether the risk event is associated with the dynamic element outside the forecast time range, this The application examples are not specifically limited.
  • the dynamic elements that affect risk events may be road icing, road construction, heavy fog, snowstorm, rainstorm, road congestion, a certain road is forbidden to pass, road collapse, landslide, road maintenance and other dynamic elements. At least one, which is not specifically limited here.
  • the identification information of the dynamic element is used to uniquely identify a dynamic element in the map.
  • the identification information of a dynamic element can be a combination of one or more characters, where the characters can be one or more of numbers, letters and other symbols, such as a combination of one or more numbers, or one or more data and A combination of letters.
  • the risk event information further includes information on map elements affected by the risk event.
  • the information of the map elements affected by the risk event is an optional information of the risk event information.
  • the map element can be quickly indexed, and when the risk event changes, the information of the map element affected by the risk event can be updated jointly, which not only improves the accuracy of the map data, but also improves the accuracy of the map data. The update efficiency of map data is also improved.
  • map features can be elements in static layers such as roads, lanes, intersections, etc., or can be speed limit values of roads, time periods of road speed limits, confidence levels of time periods, traffic time periods of roads, time periods of road construction, lanes Events in dynamic layers such as line spanability.
  • risk event 1 causes the speed limit value of road 1 to change
  • risk event 1 causes the duration of the speed limit period of road 2 to be extended
  • risk event 1 causes a lane to add speed limit information.
  • the risk event information also includes early warning information.
  • the early warning information is used to indicate the contents to be reminded to the driver or the driving system based on the risk event. It should be noted that early warning information is an optional information of risk event information. Based on the early warning information, the driver or the driving system can be reminded to pay attention to and avoid the predicted area corresponding to the risk event ahead as much as possible, which is conducive to improving the safety of the vehicle.
  • the early warning information can be a risk warning message like "area a is dangerous in the forecast period 1, please drive carefully", or it can be a detour like "area a is dangerous in the forecast period 1, please try to avoid this area”
  • the suggestion information may also be other information that can serve as a warning of risk events, which is not specifically limited in this embodiment of the present application.
  • the risk event information further includes: at least one of identification information of the risk event, identification information of a tile to which the risk event belongs, and identification information of a road where the risk event is located. It should be noted that the identification information of the risk event, the identification information of the tile to which the risk event belongs, and the identification information of the road where the risk event is located are all optional information of the risk event information.
  • the identification information of the risk event is used to identify the risk event in the map; the identification information of the tile is used to identify the tile in the map, and the identification information of the road is used to identify the road in the map.
  • the identification information of the risk event, the identification information of the tile or the identification information of the road may be a combination of one or more characters, where the characters may be one or more of numbers, letters and other symbols, For example, a combination of one or more numbers, or a combination of one or more numbers and letters.
  • the first type uses event as a unit, and stores risk event information in the data structure corresponding to the risk event identifier; the second type uses time period as a unit, and stores risk event information in a data structure corresponding to the forecast time range.
  • the following expressions shown in FIG. 5A , FIG. 5B and FIG. 6 are for reference only, and do not limit that risk event information can only be stored in the illustrated data structure. These two expressions are described in detail below.
  • the first type in units of events
  • FIG. 5A is a schematic diagram of an expression manner of risk event information provided by the embodiment of the present application.
  • risk event A is taken as an example to illustrate the representation of risk event information of risk event A.
  • the risk event information includes time information and location information of risk event A.
  • the risk event information also includes risk level information of wind risk event A, risk type information, dynamic elements affecting risk event A, information of map elements affected by risk event A, early warning information, risk event At least one of the identification information of the tile to which A belongs, the identification information of the road where the risk event A is located, and the identification information of the risk event A.
  • FIG. 5A For details of each information shown in FIG. 5A , reference may be made to the description of relevant content in the foregoing embodiments, and for the sake of brevity of the description, details are not repeated here.
  • the expression manner of the risk event information shown in FIG. 5A is merely an example, and the embodiment of the present application does not limit the composition and data structure of the risk event information.
  • Each of the eight types of content in the above-exemplified risk event information except time information and location information is not necessarily included in the risk event information, that is, can be selectively included in the risk event information according to actual application requirements.
  • the risk event may also change dynamically over time.
  • the risk event information may also include specific information of the risk event in different time periods.
  • FIG. 5B it provides a schematic diagram of another expression manner of risk event information, which is also another example of specific content of risk event information.
  • risk event A is still taken as an example to illustrate the risk event information of risk event A.
  • risk event information includes risk event A in at least one period (for example, prediction period 1, prediction period 2, etc.) Information, taking forecast period 1 as an example, the specific information of risk event A in forecast period 1 includes the location information of risk event A in forecast period 1, risk level information, risk type information, dynamic factors affecting risk event A, affected Information on map elements affected by risk event A and early warning information, etc.
  • the risk event information also includes the identification information of the risk event A, the identification information of the tile to which the risk event A belongs, and the identification information of the road where the risk event A is located.
  • the ID of the same risk event remains unchanged in different time periods, therefore, the ID of the risk event remains unchanged.
  • the location information, risk level information, risk type information, identification information of dynamic elements that affect risk events, and map elements affected by risk event A in different time periods correspond to risk events. At least one of information and warnings may be different.
  • the location information is the prediction area range 1, and the risk level information indicates high risk and affects risk event A
  • the dynamic elements include road icing element 1 and road construction element 2, where the icing thickness of road icing element 1 in the prediction period 1 is 5cm; assuming that risk event A is in the prediction period 2 (that is, the next 1 hour to 2 hours Inside): The location information is still the prediction area range 1, but the risk level information indicates low risk and the dynamic element affecting risk event A is road icing element 1, and the icing thickness of road icing element 1 in the prediction period 2 It is 1cm.
  • the dynamic elements associated in the prediction period 2 have changed compared with the dynamic elements associated in the prediction period 1. Specifically, the attribute status of the road icing element 1 has changed and the road construction element has changed after the prediction period 1 ends. 2 disappears. In addition, the risk level information at the forecast period 2 is also different from the risk level information at the forecast period 1.
  • the second type by time period
  • the risk event information may also be expressed in units of a forecast period range.
  • FIG. 6 is another schematic representation of risk event information provided by the embodiment of the present application.
  • the risk event information is stored in units of forecast period, for example, the risk event information of risk event A is stored in the data structure corresponding to forecast period 1, wherein the risk event information of risk event A includes the identification of risk event A Information, location information corresponding to risk event A in prediction period 1, risk level information, risk type information, dynamic elements that affect risk event A, information on map elements affected by risk event A, early warning information, and tiles to which risk event A belongs The identification information of the slice, etc.
  • the event-related information of the risk event A in different time periods may be stored in a data structure corresponding to the time period.
  • the event-related information of risk event A in the prediction period 2 may be stored in the storage structure corresponding to the prediction period 2 .
  • risk event information of at least one risk event may be stored in a data structure corresponding to each forecast period.
  • risk event information of other risk events can also be stored, for example, risk event information of risk event B, where, The detailed content of the risk event information of risk event B can refer to the risk event information of risk event A, and for the sake of brevity, details are not repeated here.
  • the risk event information in different time periods can be represented in the form of map layers, for example, the risk event information in different time periods can be expressed in The map is represented in the form of different dynamic layers.
  • FIG. 7 is a schematic diagram of a dynamic layer carrying risk event information in different time periods provided by an embodiment of the present application.
  • the map data shown in Figure 7 includes static layers and dynamic layers corresponding to each forecast period, where the static layers include information such as road topology, building locations, lane lines, lane directions, or traffic infrastructure layout ;
  • the dynamic layers corresponding to each period include the dynamic layer 1 corresponding to the prediction period 1, the dynamic layer 2 corresponding to the prediction period 2, ..., the dynamic layer n corresponding to the prediction period n.
  • the dynamic layer 1 includes risk event 1 and risk event 2, where the forecast area of risk event 1 is indicated by risk area 1
  • the area range of risk event 2 is the area range indicated by risk area 2.
  • dynamic layer 1 may also include dynamic elements associated with risk event 1, such as snowstorm elements, and dynamic elements associated with risk event 2, such as road construction elements.
  • the dynamic layer corresponding to the forecast period shown in FIG. 7 not only carries the location information of the risk event but also carries the dynamic elements affecting the risk event.
  • part of the information of the risk event may also be located in different dynamic layers, which is not specifically limited in this embodiment of the present application.
  • the dynamic layer corresponding to each prediction period shown in FIG. 7 is just an example.
  • the dynamic layer corresponding to each forecast period can be displayed separately, or the dynamic layers corresponding to multiple forecast periods can be displayed simultaneously as shown in Figure 7, or it can be a dynamic layer that carries risk event information.
  • Layers are superimposed with other layers of the map, for example, the dynamic layer 1 in Figure 7 is superimposed with the static layer, or the dynamic layer that carries risk event information is superimposed with the dynamic layer that only carries weather information display, or, the dynamic layer carrying risk event information, the dynamic layer carrying road environment information, the dynamic layer carrying only weather information, and the static layer are superimposed and displayed together.
  • the embodiment of the present application does not limit the The number of map layers to be superimposed, and the types of layers that can be superimposed are not limited.
  • map data related to risk events can be extracted from static layer data and dynamic layer data, for example, environmental information and/or road state information for a certain period of time, wherein the environmental information includes but not limited to precipitation , snowfall, visibility, light intensity, wind direction, wind force, lightning index and other weather parameters, road status information includes but not limited to road type, number of lanes, road construction information (for example, whether there is construction, construction location, construction time, etc.), Pothole information on the road surface (for example, depth, location information, area, etc.), road surface covering information (for example, thickness of icing, depth of accumulated water, amount of fallen leaves) and other information indicating the state of the road.
  • environmental information includes but not limited to precipitation , snowfall, visibility, light intensity, wind direction, wind force, lightning index and other weather parameters
  • road status information includes but not limited to road type, number of lanes, road construction information (for example, whether there is construction, construction location, construction time, etc.), Pothole information on the road surface (for example, depth, location information,
  • the risk event information is obtained by forecasting.
  • road types For example, based on road administrative levels, they can be divided into national roads, provincial roads, county roads, and township roads. Class-1 roads, class-2 roads, and class-3 roads can also be divided into other ways, which are not specifically limited here.
  • the static layer data and dynamic layer data used to obtain risk event information are also different.
  • the types of risk events include but are not limited to lane change collisions, turning collisions, skid collisions, reduced visibility, reduced braking distances, and the like.
  • the static layer data specifically includes the real and virtual information of lane lines, the location information of dashed lane lines, the location information of intersections, and the location information of road obstacles.
  • Layer data includes road construction information, traffic accident information, severe weather information (such as heavy rain, snowstorm) and so on.
  • the map data used to obtain risk event information is mainly dynamic layer data, wherein the dynamic layer data specifically includes road surface icing information, road surface water information, road surface leaf fall information, etc. At least one of information describing road coverage such as thickness and severe weather information such as heavy rain and snowstorm.
  • the process of obtaining risk event information may specifically be: obtaining the environmental information and/or road state information of the target geographical area during the prediction period from the static layer data and/or dynamic layer data of the map, wherein the target The geographical area includes the above-mentioned forecast area range, and the forecast time period includes the above-mentioned forecast time range.
  • the time information of the risk event is based on the input parameters (for example, environmental information and/or road state information) predicts the possible occurrence time range
  • the location information of the risk event is the possible occurrence area range predicted according to the input parameters.
  • the artificial intelligence model may also output at least one of risk level information and risk type information of the risk event. Furthermore, combined with the time information, location information, risk level information, risk type information and the description information of the map elements in the risk event, the dynamic elements affecting the risk event can be determined and the information of the map elements affected by the risk event can be obtained.
  • the source data used to generate risk event information is not limited to the data in the map, and can also be information received from roadside equipment, or information sensed by the vehicle itself (including environmental information and/or vehicle status information). The embodiment of the present application does not limit the data type and data source for generating risk event information.
  • the aforementioned artificial intelligence models are pre-trained and can be continuously optimized during subsequent use.
  • the artificial intelligence model may be a random forest (Random Forest, RF), a support vector machine (Support Vector Machine, SVM) model, a neural network model or other prediction algorithms, which are not specifically limited in this application.
  • the above-mentioned artificial intelligence model is obtained based on training of historical accident data, wherein the historical accident data includes historical accident occurrence period, historical accident occurrence area, risk level, risk type, historical environmental information of historical accident occurrence period and Historical road state information.
  • the historical accident data may be provided by the traffic management department.
  • the risk level can be assessed according to the degree of collision of historical accidents, casualties, etc.
  • the training process of the artificial intelligence model can specifically be: taking accident 1 as an example, assuming that the occurrence period of accident 1 is period 1 and the occurrence area is area 1, and the risk level of accident 1 is taken as the true value of the risk level of accident 1 , take the risk type of accident 1 as the true value of the risk type of accident 1, input the historical environmental information and historical road state information corresponding to accident 1 into the artificial intelligence model for prediction, and the artificial intelligence model outputs the prediction time range as time period 2, prediction area
  • the scope is area 2, predicted risk level and predicted risk type. According to the location information of area 1 and area 2, the location prediction error of accident 1 is obtained, and the period prediction error of accident 1 is obtained according to time period 1 and time period 2.
  • the risk level prediction error of accident 1 is obtained from the risk level true value corresponding to the level and accident 1, and the risk type prediction error of accident 1 is obtained according to the predicted risk type and the true value of the risk type of accident 1, based on the location prediction error, period prediction error, risk At least one of the level prediction error and risk type prediction error adjusts the parameters of the artificial intelligence model until the prediction error of the artificial intelligence model is less than or equal to the preset error threshold, so that the trained artificial intelligence model can Environmental information and road state information accurately predict risk events.
  • the vehicle when acquiring risk event information is generating risk event information, and the generator of risk event information is a vehicle, in this case, the vehicle must meet at least one of the following conditions:
  • the vehicle is within the predicted area
  • the minimum distance between the vehicle and the predicted area range is less than the first threshold
  • the predicted area range intersects with the planned path of the vehicle
  • the minimum distance between the predicted area range and the planned path of the vehicle is less than a second threshold
  • the tile to which the prediction area belongs is the tile where the vehicle is located.
  • the tiles to which the prediction area belongs are the tiles that the planned path of the vehicle passes through.
  • the vehicle can generate risk event information near its own location based on the current location, or generate risk event information near the driving route based on the planned route.
  • the above is only an example of the restriction conditions when the generation end of the risk event information is a vehicle, and the above restriction conditions are also applicable to other devices whose generation end of the risk event information is the terminal side.
  • risk event information is stored as map data.
  • map data For example, store risk event information as dynamic layer data for a map.
  • storing the risk event information specifically includes: storing the risk event information in a data structure corresponding to the risk event identifier.
  • storing the risk event information specifically includes: storing the risk event information in a data structure corresponding to the forecast time range.
  • S103 Determine that factors affecting risk events have changed, and update risk event information or eliminate risk event information according to the changed factors.
  • the elements are located in the static layer or dynamic layer in the map. Since the risk event information includes the identification information of the dynamic elements affecting the risk event A, based on the identification of the dynamic elements in the risk event information, the impact risk can be quickly indexed in the map For the dynamic elements of event A, when it is detected that the dynamic elements affecting the risk event change, correspondingly, the information of the risk event will also change.
  • the change of dynamic elements affecting risk events can be determined based on detection information sent by roadside equipment or terminals, or can be determined based on real-time dynamically updated map data.
  • updating risk event information based on changed elements refers to: modifying time information, location information, risk level information, risk type information, early warning information, and factors affecting risk events in risk event information based on changed elements. At least one of the identification information of the dynamic element and the information of the map element affected by the risk event.
  • the elimination of risk event information according to the changed elements may be: according to the changed elements, it is determined that the risk level information of the updated risk event satisfies the first risk condition, and the risk event information is deleted, wherein the first The risk condition may be that the risk level of the risk event is less than the first risk threshold, or that the risk event has no risk. It should be noted that the elimination of risk event information means that the corresponding risk event is predicted not to occur, and the risk event in the map is also deleted.
  • the risk level of risk event 1 in the forecast period 1 is medium risk
  • the factor affecting risk event 1 is road icing and the thickness of ice in the forecast period 1 is 3mm
  • the weather changes from cloudy to sunny it is easy to accelerate the icing process. If it is obtained from the map that the road icing element associated with risk event 1 disappears in the forecast period 1, resulting in the risk level of risk event 1 being updated to no risk, then delete the risk event information.
  • the dynamic elements that affect risk events also belong to the dynamic layer data of the map that obtains risk event information.
  • the risk can also be updated based on the above-mentioned artificial intelligence model. The event information will not be repeated here.
  • the method further includes: performing road condition monitoring, traffic dispatching, route planning or vehicle control according to the risk event information.
  • the method further includes sending the risk event information, so that the receiver of the risk event information performs road condition monitoring, traffic scheduling, route planning or vehicle control according to the risk event information.
  • the risk event information For the specific process, reference may be made to the relevant descriptions of the embodiments in FIG. 8 and FIG. 9 below.
  • the updated risk event information may also be sent.
  • a map display interface may also be generated according to risk event information.
  • risk event information For the specific process, reference may be made to the related description of S304 in the following embodiment in FIG. 9 , which will not be repeated here.
  • the implementation of the embodiment of the present application can provide risk event information of risk events that may occur within a certain geographical area.
  • the risk event information is of reference significance and helps to increase the richness of the map.
  • it also provides a data structure for storing risk event information, which can realize clear and intuitive expression of risk event information.
  • the acquisition of risk event information comprehensively considers factors such as surrounding environment information and road state information, which can effectively improve the accuracy of risk event information.
  • FIG. 8 is a flowchart of another method for processing map data provided by an embodiment of the present application.
  • the execution subject of the method shown in FIG. 8 may be a network side device, a roadside device or a terminal.
  • the method includes but is not limited to the following steps:
  • S201 Generate risk event information.
  • the risk event information includes time information and location information.
  • the time information is used to indicate the predicted time range of the risk event
  • the location information is used to indicate the predicted area range of the risk event.
  • S202 According to the risk event information, execute road condition monitoring, traffic scheduling, route planning operations or vehicle control.
  • performing road condition monitoring according to the risk event information may be: performing road condition monitoring on the predicted area in the map according to the risk event information.
  • road condition monitoring may also be performed on the predicted area range exceeding the risk threshold in the map according to the risk event information.
  • the execution of traffic scheduling may be: according to the risk event information and the road topology information in the map, determine the road area associated with the predicted area range, and prompt the corresponding risk event to the vehicles in the road area , or control the traffic flow in the road area. It can be seen that the execution of traffic dispatch based on risk event information can effectively improve the safety of vehicle travel.
  • the above-mentioned road area includes at least one section of road or includes at least one section of lanes, and multiple sections of roads in the road area may correspond to the same road sign (that is, belong to the same road), or may correspond to multiple road signs (that is, belong to different roads).
  • multiple lanes in the road area may correspond to the same lane marking or may correspond to multiple lane markings.
  • the road area when the road area satisfies at least one of the following conditions, the road area is associated with the predicted area range:
  • the distance between the road area and the predicted area range is less than or equal to the preset distance threshold
  • the risk event information indicates "area 1, period 1, high risk”
  • the network side device such as a cloud device, determines based on the risk event information that the road area associated with area 1 where risk event 1 is located includes area 2 and area 3, then the network The side device prompts the risk event to the vehicles in area 2 and area 3 before period 1, for example, sends "area 1 is a high-risk area in period 1, please detour".
  • the network-side device can also control the traffic flow in area 2 and area 3, for example, selectively setting certain road sections in area 2 or area 3 to be prohibited from entering within a preset period of time, or, for area 2 and area 3 Or, guide vehicles in area 2 or area 3 to change the guide line to avoid area 1, so that the traffic flow in area 1 in time period 1 is reduced, traffic scheduling is realized, and the travel risk of vehicles is greatly reduced .
  • controlling the vehicle according to the risk event information may be: when it is determined that the vehicle is about to pass through the predicted area where the risk event is located, controlling the vehicle to perform at least one of the following operations: changing lanes; adjusting the driving speed; Updating the navigation route; turning on the warning light; and prompting the driver of the risk event.
  • Vehicles can pre-subscribe risk event services to network-side devices, such as the cloud, so that vehicles can take targeted measures in advance to deal with risk events, improving the safety of vehicle operation.
  • executing route planning according to risk event information may be: in response to a route planning request from a terminal (for example, a vehicle, a mobile phone, etc.), execute according to information such as the risk event information and the destination of the terminal carried in the route planning request Path planning to obtain the lowest risk path.
  • path planning may also be performed based on risk event information in combination with at least one of the vehicle's perception capability, vehicle type, and automatic driving level to obtain the lowest risk path.
  • the lowest risk path may also be recommended to the terminal, or multiple paths including the lowest risk path may be recommended to the terminal for selection by the terminal.
  • the path planning operation refer to the related description of S303 in the embodiment of FIG.
  • multiple operations in road condition monitoring, traffic dispatching, route planning, and vehicle control can also be performed based on risk event information.
  • risk event information please refer to the above-mentioned relevant descriptions, and details will not be repeated here.
  • the network side device or the roadside device, etc. generate risk event information of risk events that may occur within a certain geographical area and have reference significance.
  • Network-side devices or road-side devices can also use the risk event information to implement functions such as traffic dispatching, road condition monitoring, route planning, and vehicle control, effectively responding to risk events in the map from a macro perspective, and helping to reduce traffic accidents.
  • Fig. 9 is a flow chart of another map data processing method provided by the embodiment of the present application, which is applied between the network side device and the terminal.
  • the cloud device and the car end device are taken as examples, that is, it is applied to the cloud equipment and vehicle-end equipment, but this application does not limit the method described in Figure 9 to only be applied between cloud equipment and vehicle-end equipment, for example, it can also be applied between roadside equipment and terminals, or between terminals and terminals between.
  • the method includes but is not limited to the following steps:
  • the cloud device generates risk event information, the risk event information includes time information and location information, the time information is used to indicate the predicted time range of the risk event, and the location information is used to indicate the predicted area range of the risk event.
  • the risk event information includes time information and location information
  • the time information is used to indicate the predicted time range of the risk event
  • the location information is used to indicate the predicted area range of the risk event.
  • S302 The cloud device sends risk event information to the vehicle device.
  • the cloud device sends the risk event information to the vehicle end device, which may be: the cloud device sends the risk event information in a broadcast manner. That is to say, risk event information can be carried in broadcast information.
  • the broadcasting of the risk event information by the cloud device may be: the cloud device broadcasts the risk event information based on the identifier of the tile to which the predicted area belongs, or the cloud device broadcasts the risk event information based on the identifier of the road to which the predicted area belongs.
  • the cloud device may also deliver risk event information in combination with time period and tile identification, or in combination with time period and road identification, which is not specifically limited here.
  • the cloud device may also directly send risk event information to the car-end device, wherein the car-end device meets at least one of the following conditions:
  • the vehicle-end equipment is within the predicted area
  • the tile to which the predicted area belongs is the tile where the vehicle end equipment is located;
  • the tiles to which the predicted area belongs are the tiles passed by the planned path of the vehicle-end equipment.
  • the road to which the predicted area belongs is the road where the vehicle-end equipment is located.
  • the car-end device has customized the risk warning service on the cloud device in advance, so that the car-end device can obtain risk event information from the cloud in time, so that it can respond to risk events early , improving the safety of the own vehicle.
  • the cloud device may also issue risk event information based on the actual needs of the vehicle-end device. For example, send risk event information in the area where the planned route of the vehicle-end device is located; send risk event information in at least one tile where the vehicle-end device is located; send risk event information in a specific area within a specific period of time, etc., No specific limitation is made here.
  • the cloud device sends the risk event information in advance, that is, the time when the cloud device sends the risk event information is earlier than the start time of the predicted time range in the risk event information. In some possible embodiments, when the time delay between the time when the cloud device sends the risk event information and the time when the car end device receives the risk event information is ignored, the time when the cloud device sends the risk event information is the latest It can also be equal to the starting moment of the earliest prediction time range in the risk event information.
  • the vehicle end device executes path planning and/or controls its own vehicle according to the risk event information.
  • the car-end device executes path planning according to the risk event information, specifically: the car-end device plans the lowest risk path according to the risk event information.
  • the lowest risk path is: a path without a risk event; a path without a risk event whose risk level exceeds a risk threshold; or a path without a risk event of a specific risk type.
  • the lowest risk path is the path of no risk event, which can be understood as the area that the lowest risk path passes through within a certain time range is the area of no risk event.
  • the lowest risk path is the path with no risk event whose risk level exceeds the risk threshold. It can be understood that there are predicted risk events in the area that the lowest risk path passes within a certain time range, but the risk level of the risk event does not exceed the risk threshold (that is, the risk level is considered The larger the value of , the more dangerous the risk event).
  • the lowest risk path is the path of risk events without a specific risk type. It can be understood that there are predicted risk events in the area that the lowest risk path passes within a certain time range, but the risk type of the risk event is not a specific risk type.
  • planning the minimum risk path according to the risk event information includes: planning the minimum risk path of the vehicle end device according to the risk event information and the perception capability of the vehicle end device.
  • the vehicle can effectively perceive the surrounding environment through its own sensing elements (or called on-board sensors).
  • the perception ability of the vehicle includes the perception of the vehicle and the perception of the environment.
  • the devices used to perceive the vehicle include sensors in power, chassis, body and electronic and electrical systems, etc.
  • the devices used to sense the environment include on-board cameras, millimeter-wave radars, and lidars.
  • the risk event information when planning the lowest risk path of the vehicle-end device, combined with the perception capability of the vehicle-end device, not only can the risk event information be corroborated, but also can be recalculated based on the information related to the risk event perceived by the vehicle at the current location, to obtain More accurate time information and location information of risk events make the planning of the lowest risk path more accurate.
  • planning the minimum risk path according to the risk event information includes: planning the minimum risk path of the vehicle end device according to the risk event information and the vehicle type of the vehicle end device.
  • transport vehicles can be divided into cars, passenger cars and trucks, and special-purpose vehicles can be divided into transport types (including refrigerated vehicles). vehicles, sand dump trucks, etc.) and operational types (including fire trucks, ambulances, etc.), special-purpose vehicles can be divided into recreational vehicles, competition vehicles, etc.
  • transport vehicles can be divided into cars, passenger cars and trucks, and special-purpose vehicles can be divided into transport types (including refrigerated vehicles). vehicles, sand dump trucks, etc.) and operational types (including fire trucks, ambulances, etc.), special-purpose vehicles can be divided into recreational vehicles, competition vehicles, etc.
  • special-purpose vehicles can be divided into recreational vehicles, competition vehicles, etc.
  • internal combustion engine vehicles electric vehicles, jet vehicles, and the like.
  • they can be divided into miniature vehicles, small vehicles, compact vehicles, medium-sized vehicles, and medium-to-large vehicles.
  • the embodiment of the present application does not specifically limit the division of vehicle types.
  • different types of vehicles have different levels of difficulty in driving and manipulation, and are good at different driving environments.
  • the regulation of small vehicles is more flexible than that of medium and large vehicles, so the ability of small vehicles to respond to certain types of risk events is stronger than that of medium and large vehicles. Therefore, the inclusiveness of the lowest risk path for small vehicles may be stronger than that for medium and large vehicles. Requirements for anti-risk ability. Smaller vehicles may be less able to respond to other kinds of risk events than larger vehicles.
  • the minimum risk path of the vehicle end equipment is planned in combination with the vehicle type, so that the minimum risk path matches the risk response capability of the vehicle, so that different types of vehicles have suitable minimum risk paths.
  • the minimum risk route should include at least one charging pile site that can replenish the power for the vehicle in time, so as to ensure that the vehicle can travel along the minimum risk route. The path travels smoothly to the destination.
  • planning the minimum risk path according to the risk event information includes: planning the minimum risk path of the vehicle end device according to the risk event information and the automatic driving level of the vehicle end device.
  • Autonomous driving can also be called intelligent driving or assisted driving. It is an important direction for the development of intelligent vehicles. Achieve different levels of driving experience.
  • the Society of Automotive Engineers (SAE) provides a classification standard for driving automation, including driving levels L0 to L5, where L0 is no automation, the human driver has full authority to operate the vehicle, and can be driven during driving.
  • System warning or assistance such as automatic emergency braking (autonomous emergency braking, AEB), blind spot detection (blind spot monitoring, BSM) or lane departure warning (lane departure warning, LDW), etc.
  • L1 level is driving support.
  • the driving operation is completed by the human driver and the driving system.
  • the driving system can provide driving support for the steering wheel or acceleration and deceleration operations through the driving environment.
  • L2 level is partially automated, providing driving support for steering wheel and acceleration and deceleration through the driving environment, and other driving Actions are performed by human drivers, such as car-following functions that combine adaptive cruise control (ACC) and lane keep assistance (LKA);
  • L3 is conditional automation, which can be done by the driving system The driving operation, but the human driver needs to respond to the request of the driving system at an appropriate time, that is, the human driver needs to be ready to take over the driving system;
  • L4 level is highly automated, and all driving operations can be completed by the driving system. The driver does not necessarily need to respond to the request of the driving system.
  • L5 level driving operations can be performed autonomously by the driving system under various road and environmental conditions that human drivers can cope with. It can be seen that at the level of L0 to L2, the driving system mainly provides support for the driver, and the driver still needs to do a good job of driving supervision, and steer, brake or accelerate as needed to ensure safety. From L3 to L5, the driving system can replace the driver to complete all driving operations. At the L3 level, the driver must be ready to take over the driving. At the L4 and L5 levels, the driving system can realize complete driving under some conditions and all conditions. Members can choose whether to take over.
  • the above grading is an example. With the evolution of technology or different regulations in different countries or regions, the above grading can change.
  • the vehicle automation grading proposed by the Ministry of Industry and Information Technology of China includes 6 levels of vehicle driving automation, Among them, level 0-2 is driving assistance, the system assists humans to perform dynamic driving tasks, and the driving subject is still the driver; level 3-5 is automatic driving, and the system replaces humans to perform dynamic driving tasks under the designed operating conditions. When the function is activated, The driving subject is the system.
  • level 0 driving automation (emergency assistance, emergency assistance) system cannot continuously perform vehicle lateral or longitudinal motion control in dynamic driving tasks, but can continuously perform some target and event detection and response in dynamic driving tasks Ability.
  • Level 1 driving automation (partial driver assistance) system continuously executes vehicle lateral or longitudinal motion control in dynamic driving tasks under its design operating conditions (or called design operating range ODD), and has the same Vehicle lateral or longitudinal motion control adaptive part target and event detection and response capabilities.
  • Level 2 driving automation (combined driver assistance) system continuously performs vehicle lateral and longitudinal motion control in dynamic driving tasks under its design operating conditions, and has the ability to adapt to the executed vehicle lateral and longitudinal motion control Some target and incident detection and response capabilities.
  • Level 3 driving automation (conditionally automated driving) systems continuously perform all dynamic driving tasks under the operating conditions for which they are designed.
  • Level 4 driving automation (highly automated driving) system continuously performs all dynamic driving tasks under its design operating conditions and automatically executes the minimum risk strategy.
  • the level 5 driving automation (fully automated driving) system continuously performs all dynamic driving tasks and automatically executes the minimum risk strategy under any drivable conditions.
  • lateral control is mainly used to control the steering of the vehicle, for example, controlling the torque or angle of the steering wheel to control the direction of the vehicle; longitudinal control is mainly used for speed control of the vehicle, such as controlling the brake pedal, accelerator pedal, or gear to control Vehicle acceleration/deceleration, braking, etc.
  • Operational Domain Design refers to the conditions under which the automatic driving system can operate safely.
  • the conditions set can include geographical location, road type, speed range, weather, time, national and local traffic laws and regulations, etc.
  • HWP Highway Cruise Control system
  • the system recognizes that the vehicle is already within the ODD range (for example, the vehicle is currently driving on the highway, the weather is sunny, the speed is appropriate, the lighting conditions are good, the global positioning navigation system (Global Positioning Navigation System) Positioning System, GPS) signal stability, etc.), after the driver confirms to activate the system, the HWP system will continue to perform all dynamic driving tasks.
  • the vehicle is mainly controlled by the automatic driving system.
  • the level of automatic driving of the vehicle it is possible to make the areas passed by the planned minimum risk path meet the requirements of ODD , that is to say, the lowest risk path needs to avoid areas with poor road conditions and bad weather conditions as much as possible.
  • the driver may also be requested to take over, which is not specifically limited here.
  • the vehicle itself has better risk response capabilities.
  • the planned minimum risk path is also more inclusive.
  • a vehicle at the L0, L1 or L2 level mainly relies on the driver to perform driving operations, and the risk response capability of the vehicle mainly depends on the driver's driving experience.
  • the driving level can also be combined with the driver's driving habits and / or driving ability to plan the lowest risk path.
  • the minimum for the planning of the risk path when planning the lowest-risk path according to the risk event information, the minimum For the planning of the risk path, reference may be made to relevant descriptions in the foregoing embodiments for details, and details are not repeated here.
  • the process of obtaining the lowest risk route may specifically be: obtaining multiple routes, determining that the predicted area range includes the position points in the multiple routes; predicting the estimated time when the vehicle-end device travels to the position point; The time information and location information in determine the forecast time range corresponding to the forecast area range; determine the forecast time range including the above-mentioned expected time; determine the driving risk of at least one of the multiple routes according to the risk event; determine the multiple routes according to the driving risk of the route The lowest risk path among the paths.
  • the vehicle-end device when planning the lowest-risk path, the vehicle-end device needs to estimate the estimated time of its actual arrival at each location point of the route based on its own driving speed, so that the position of the vehicle-end device at each estimated time is not a risk event as much as possible The range of the forecast area where it is located, or as much as possible so that each estimated time does not belong to the forecast time range of the risk event.
  • the car-end device passes from point A to point B to point C, and there are risk events near points A, B, and C at the current moment, but when planning the lowest risk path of the car-end device, not only according to the current time
  • the risk events at the three location points are the route planning for the vehicle, and the estimated time when the vehicle-end equipment arrives at points A, B, and C is also considered comprehensively. For example, it is expected to arrive at point A at time t1, point B at time t2, and point B at time t3 At point C, the risk event information near point A at time t1, the risk event information near point B at time t2, and the risk event information near point C at time t3 are used to plan the path of the vehicle-end device.
  • path 1 For example, in Figure 1, assuming that the current time is 9:00, and the vehicle wants to go from position A to position D, there are two possible driving paths, namely path 1 and path 2, where path 1 is A ⁇ B ⁇ C ⁇ D, path 2 is A ⁇ B ⁇ E ⁇ F ⁇ C ⁇ D, and the length of path 1 is less than the length of path 2.
  • path 1 is A ⁇ B ⁇ C ⁇ D
  • path 2 is A ⁇ B ⁇ E ⁇ F ⁇ C ⁇ D
  • the length of path 1 is less than the length of path 2.
  • the vehicle receives broadcast risk event information at position A, and obtains information indicating that area 1 in road segment BC is a high-risk area within the predicted time range of 9:30-11:00 according to the risk event information.
  • the following two examples illustrate The process of the vehicle performing path planning operations based on risk event information:
  • Example 1 The vehicle first selects path 1: A ⁇ B ⁇ C ⁇ D. Based on its current driving speed, the vehicle estimates that the time it will arrive at position B, position C and position D is 9:05, 9:20 and 9: 25. It can be seen that the vehicle successfully crossed the area 1 in the road segment BC before 9:20, which is earlier than the start time 9:30 of the predicted time range of the risk event 9:30-11:00, so the route 1 is determined : A ⁇ B ⁇ C ⁇ D is a risk-free path, and the length of path 1 is less than the length of path 2, so path 1 is the optimal navigation path.
  • Example 2 The vehicle first selects route 1: A ⁇ B ⁇ C ⁇ D, assuming that the vehicle estimates the time when it arrives at position B, position C and position D based on its current driving speed at 9:30, 10:30 and 11, respectively. :00, it can be seen that the moment when the vehicle arrives at area 1 in the road segment BC belongs to the predicted event range of the risk event 9:30-11:00, that is, if the vehicle travels according to the path A ⁇ B ⁇ C ⁇ D, Then the vehicle will pass through high-risk area 1 within the predicted time range of 9:30-11:00, that is, there are risk events in the area passed by route 1, and the safety of route 1 is low.
  • the vehicle analyzes path 2, and determines that path 2: A ⁇ B ⁇ E ⁇ F ⁇ C ⁇ D is a risk-free path, so that path 2 is taken as the lowest risk path, so as to realize the prediction period 9:30-11:00 Avoiding area 1 improves driving safety and accuracy of driving decisions.
  • the vehicle-end device can also recommend the lowest-risk route to the user, or the vehicle-end device controls the own vehicle to drive along the lowest-risk route.
  • the vehicle-end device can also recommend multiple paths to the user, wherein the multiple paths include the lowest-risk path, and receive feedback information from the user, and the feedback information is used to instruct the user A path selected from multiple paths, and the ego vehicle is controlled to travel along the path selected by the user.
  • the navigation application can recommend travel routes to the user based on travel strategies, such as the shortest distance, the shortest time, the fewest red lights, the most fuel-efficient, or the most economical tolls.
  • Vehicles, portable terminals, and devices installed on the roadside or in the cloud can use risk event information.
  • the installed navigation application combined with the user's travel needs (including but not limited to the starting point, destination, travel time or travel mode), provide users with Formulate the lowest risk path or recommend multiple paths including the lowest risk path for the user.
  • the multiple paths may also include the path with the least time, the path with the shortest distance, and the like.
  • the path with the lowest risk may also take the least time or the shortest distance at the same time, which is not specifically limited here.
  • FIG. 10 is a schematic interface diagram of a route recommendation provided by an embodiment of the present application.
  • the navigation display interface in Figure 10 recommends multiple travel routes under different strategies to the user, and the rectangular box in the lower right corner explains each route in text form: route 1 via A-B-F-D-E is the route with the shortest travel distance, and the total distance is 3.6 kilometers; route 2 via A-B-C-D-E is the route with the lowest risk, and the number of risk events is 0; route 3 via A-G-H-E is the route with the least time, with a total duration of 15 minutes.
  • the circular areas on each route in Figure 10 represent the prediction range area where the risk event is located.
  • route 1 has two circular areas, so the number of risk events in route 1 is 2; route 3 has 1 circular area , so the number of risk events in route 3 is 1; and the number of risk events in route 2 is 0, therefore, route 2 is the lowest risk path.
  • the user can select a route from the three routes shown in FIG. 10 , and in response to the user's selection operation, the vehicle is controlled to travel along the route selected by the user.
  • performing control on the own vehicle includes: within the scope of the prediction area where the vehicle is determined to pass through the risk event, controlling the vehicle to perform at least one of the following operations: changing lanes; adjusting driving speed; update navigation routes; turn on warning lights; and alert the driver or driving system of risky events. Therefore, during the driving process, the vehicle-end equipment facilitates real-time decision-making based on risk event information to improve its own safety.
  • the vehicle end device generates a map display interface according to the risk event information.
  • the display device of the vehicle end device may present a map display interface.
  • the display device may be a vehicle-mounted panel of a vehicle-end device, a vehicle-mounted display, or a head-up display (HUD) system, etc., which are not specifically limited herein.
  • HUD head-up display
  • risk event information can be presented on the map display interface in at least one of the following ways:
  • the changes of risk events can be dynamically played on the map display interface, so that the driver can know the change trend of risk events in the front area in advance, and can adjust his own driving strategy in time to improve his own safety.
  • the marked risk events related to the navigation path satisfy two dimensions of time and space.
  • the navigation route indicates from point A to point C via point B, and there are risk events near points A, B, and C at the current moment, but the estimated time for the vehicle to reach points A, B, and C is t1, t2, At t3, it is only necessary to mark the relevant information of risk events near point A at time t1, the relevant information of risk events near point B at time t2, and the relevant information of risk events near point C at time t3.
  • at least one risk event related to the navigation path within a certain range from the current position may also be marked first.
  • the user can also select a time period of interest on the map display interface, and view the relevant information of at least one risk event under this time period.
  • the time period information is conducive to improving the accuracy of driving decisions.
  • the user's selection operation may be generated based on any form such as touch, drag, slide, or voice.
  • FIG. 11 is an interface of a display device provided by an embodiment of the present application.
  • the interface of the display device mainly includes two parts, one part is a risk event selection interface, and the other part is a map display interface.
  • the risk event selection interface there are a "local” option key and a "global” option key, wherein at least one road option key is listed under the "local” option key, for example, road A, road B, road C, etc.
  • a list of risk events on road B is listed on the right side. It can be seen that the list of risk events on road B includes event 1, event 2, event 3 and event 4.
  • the map display interface when the user selects an option button on the risk event selection interface on the left, in response to the operation, relevant information of the risk event is presented in the display area of the map display interface on the right.
  • a "period" option key can also be set on the risk event selection interface shown in FIG. 11 , so that the user can view the relevant information of at least one risk event under the interested time period.
  • the time period may be generated according to default settings, so as to be directly selected by the user.
  • the time period may also be set online by the user himself, which is not specifically limited here.
  • At least one of the option keys such as "risk level”, “risk type”, and “geographical area range” can also be set on the risk event selection interface shown in Figure 11, which can be used as a map display
  • the interface displays the filter conditions of risk events. For example, only mark the relevant information of risk events whose risk level is above medium risk, mark the relevant information of risk events whose risk type is collision risk, mark the relevant information of risk events within a geographical area within 100m from the current location, and so on.
  • FIG. 11 is only an example of an interface of a display device, and this embodiment of the present application does not limit the interface of the display device to only the form shown in FIG. 11 .
  • the identification of map tiles can also be added under the "local" option key of the risk event selection interface shown in FIG. 11 to realize the classification of each road.
  • FIG. 12 is a schematic diagram of a map display interface provided by an embodiment of the present application.
  • a risk event is displayed.
  • the quadrilateral area in the road ahead indicates the forecast area where the risk event is located, and a bullet box next to it shows the detailed information of the risk event, including "forecast period 9:20-10:30, the location information is the coordinates of area 1, and the risk level is high risk", based on this bullet box, it can be known that there is a high risk in area 1 at 9:20-10:30.
  • the display area of the map display interface in Figure 11 can display the screen.
  • dynamic elements that affect the risk event can also be included in the risk event information on the map display interface.
  • the road surface icing elements that affect the risk event are also marked, wherein the oval area within the quadrilateral area is the area where the road surface icing elements are located. In this way, the user can be reminded to pay attention to the road environment around the risk area and drive carefully.
  • different colors can be used to mark the prediction area ranges where risk events of different risk levels are located, or different colors can be used to mark risks of different risk types The extent of the forecast region where the event is located. In this way, the distribution of risk events of different risk levels or different risk types in the map is intuitively and clearly displayed to the user.
  • the car-end device when it detects that it is close to the predicted area within the predicted time range, it can also give a voice prompt or warn the user that it is about to enter an area with a risk event; At the same time, it can also continue to broadcast voice information such as "drive carefully” and "request to take over driving” to remind the user.
  • the cloud device can provide the car-end device with risk event information of risk events that may occur within a certain geographical area with reference significance, and the car-end device can perform path planning and The control of the self-vehicle can better deal with the risk events in the map and improve the driving safety of the vehicle-end equipment.
  • the map display interface is obtained based on the risk event information, which intuitively and clearly shows the distribution of risk events in the map to the user.
  • the embodiment of the present application also provides an electronic map or an electronic map data structure, the electronic map or the electronic map data structure includes risk event information, the risk event information includes time information and location information, and the time information is used to indicate the predicted time of the risk event Range, the location information is used to indicate the range of the predicted area of the risk event.
  • the electronic map or electronic map data is used in the first device, and the first device sends the electronic map data including risk event information to the second device, and the second device performs road condition monitoring, traffic scheduling, routing, etc. based on the electronic map. Operations such as planning or control of the vehicle.
  • the first device is, for example, a network-side device, such as a cloud device; a road-side device, or a terminal; the second device is, for example, a terminal.
  • the risk event information is stored in an event data structure in the electronic map.
  • the risk event information is stored as dynamic layer data in the electronic map.
  • the risk event information also includes at least one of the following: identification information of the risk event, identification information of the tile to which the risk event belongs, identification information of the road where the risk event is located, risk level information, and risk type information , early warning information, identification information of dynamic elements affecting risk events, and information of map elements affected by risk events; among them, risk level information is used to indicate the degree of danger of risk events, risk type information is used to indicate the type of risk events, early warning The information is used to indicate the contents to be reminded to the driver or the driving system based on the risk event.
  • FIG. 13 is a schematic functional structure diagram of a map data processing device provided by an embodiment of the present application.
  • the map data processing device 30 includes an acquisition unit 310 and a storage unit 312 .
  • the map data processing device 30 can be realized by hardware, software or a combination of software and hardware.
  • the acquisition unit 310 is used to generate risk event information
  • the risk event information includes time information and location information
  • the time information is used to indicate the predicted time range of the risk event
  • the location information is used to indicate the predicted area range of the risk event
  • the storage unit 312 used to store risk event information as map data.
  • the map data processing device 30 further includes a sending unit 314, and the sending unit 314 is configured to send risk event information to the vehicle.
  • the map data processing device 30 further includes a processing unit 316, and the processing unit 316 is configured to determine that the elements affecting the risk event have changed, and update the risk event information or eliminate the risk event information according to the changed elements, The processing unit 316 is also configured to perform road condition monitoring, traffic scheduling, route planning or vehicle control according to risk event information.
  • the map data processing device 30 can be used to implement the method described in the embodiment of FIG. 3 .
  • the acquisition unit 310 may be used to execute S101
  • the storage unit 312 may be used to execute S102
  • the processing unit 316 may be used to execute S103 .
  • the sending unit 314 may be configured to execute S302 in the embodiment of FIG. 9 .
  • the map data processing apparatus 30 can also be used to implement the method described in the embodiment of FIG. 8 and the method at the cloud device side described in the embodiment of FIG. 9 , which are not repeated here for the sake of brevity.
  • each unit in the above embodiment shown in FIG. 13 may be realized by software, hardware, firmware or a combination thereof.
  • the software or firmware includes but is not limited to computer program instructions or codes, and can be executed by a hardware processor.
  • the hardware includes but not limited to various integrated circuits, such as central processing unit (central processing unit, CPU), digital signal processor (digital signal processor, DSP), field-programmable gate array (field-programmable gate array, FPGA) Or application-specific integrated circuit (ASIC).
  • FIG. 14 is a schematic functional structure diagram of a map data processing device provided by an embodiment of the present application.
  • the map data processing device 40 includes a receiving unit 410 and a processing unit 412 .
  • the map data processing device 40 can be realized by hardware, software or a combination of software and hardware.
  • the receiving unit 410 is used to receive risk event information, the risk event information includes time information and location information, the time information is used to indicate the predicted time range of the risk event, and the location information is used to indicate the predicted area range of the risk event; the processing unit 412 , for executing path planning or controlling the self-vehicle according to the risk event information.
  • the map data processing apparatus 40 further includes a display unit 414, and the display unit 414 is configured to generate a map display interface according to risk event information.
  • the map data processing apparatus 40 can be used to implement the method on the vehicle end device side described in the embodiment of FIG. 9 .
  • the receiving unit 410 and the processing unit 412 may be used to perform S303, and the display unit 414 may be used to perform S304.
  • each unit in the above embodiment shown in FIG. 14 may be realized by software, hardware, firmware or a combination thereof.
  • the software or firmware includes but is not limited to computer program instructions or codes, and can be executed by a hardware processor.
  • the hardware includes but not limited to various integrated circuits, such as central processing unit (central processing unit, CPU), digital signal processor (digital signal processor, DSP), field-programmable gate array (field-programmable gate array, FPGA) Or application-specific integrated circuit (ASIC).
  • the present application also provides a map data processing device.
  • the map data processing device 50 includes: a processor 501 , a communication interface 502 , a memory 503 and a bus 504 .
  • the processor 501 , the memory 503 and the communication interface 502 communicate through a bus 504 . It should be understood that the present application does not limit the number of processors and memories in the map data processing device 50 .
  • the map data processing device 50 may be the generator of the above-mentioned electronic map or electronic map data including risk event information.
  • the map data processing device 50 may be a network-side device, a road-side device or a terminal.
  • the network side device may be, for example, a server deployed on the network side (such as an application server or a map server), or a component or a chip in the server.
  • the network side device may be deployed in a cloud environment or an edge environment, which is not specifically limited in this embodiment of the present application.
  • Roadside equipment can be, for example, devices such as Road Side Unit (Road Side Unit, RSU), Multi-Access Edge Computing (Multi-Access Edge Computing, MEC) or sensors, or components or chips inside these devices, or can be composed of A system-level device composed of RSU and MEC, or a system-level device composed of RSU and sensors, or a system-level device composed of RSU, MEC and sensors.
  • the terminal can be any device such as a vehicle, a portable mobile device (such as a mobile phone, a tablet, etc.), or can be a device, a component or a chip in any of the above devices, such as an on-board unit OBU, which is not described in detail in this embodiment limited.
  • the map data processing device 50 may be a user end of the aforementioned electronic map including risk event information.
  • the map data processing device 50 may be a network-side device, a road-side device or a terminal.
  • the network side device may be, for example, a server deployed on the network side (such as an application server or a map server), or a component or a chip in the server.
  • the network side device may be deployed in a cloud environment or an edge environment, which is not specifically limited in this embodiment of the present application.
  • Roadside equipment can be, for example, devices such as Road Side Unit (Road Side Unit, RSU), Multi-Access Edge Computing (Multi-Access Edge Computing, MEC) or sensors, or components or chips inside these devices, or can be composed of A system-level device composed of RSU and MEC, or a system-level device composed of RSU and sensors, or a system-level device composed of RSU, MEC and sensors.
  • the terminal can be any device such as a vehicle, a smart wearable device (such as a sports bracelet, a watch, etc.), a portable mobile device (such as a mobile phone, a tablet, etc.), or it can be a device, a component or a device in any of the above-mentioned devices.
  • a chip, such as an OBU, is not specifically limited in this embodiment of the present application.
  • the bus 504 may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus or an extended industry standard architecture (extended industry standard architecture, EISA) bus or the like.
  • the bus can be divided into address bus, data bus, control bus and so on. For ease of representation, only one line is used in FIG. 15 , but it does not mean that there is only one bus or one type of bus.
  • the bus 504 may include a path for transmitting information between various components of the map data processing device 50 (eg, memory 503 , processor 501 , communication interface 502 ).
  • the processor 501 may include any one or more of processors such as a central processing unit (central processing unit, CPU), a microprocessor (micro processor, MP), or a digital signal processor (digital signal processor, DSP).
  • processors such as a central processing unit (central processing unit, CPU), a microprocessor (micro processor, MP), or a digital signal processor (digital signal processor, DSP).
  • the memory 503 is used to provide a storage space, in which data such as operating systems and computer programs can be stored.
  • Memory 503 can be random access memory (random access memory, RAM), erasable programmable read only memory (erasable programmable read only memory, EPROM), read-only memory (read-only memory, ROM), or portable read-only memory One or more combinations of memory (compact disc read memory, CD-ROM), etc.
  • the memory 503 may exist independently, or may be integrated inside the processor 501 .
  • Communication interface 502 may be used to provide information input or output to processor 501 .
  • the communication interface 502 can be used to receive data sent from the outside and/or send data to the outside, and can be a wired link interface such as an Ethernet cable, or a wireless link (such as Wi-Fi, Bluetooth, general wireless transmission, etc.) interface.
  • the communication interface 502 may further include a transmitter (such as a radio frequency transmitter, an antenna, etc.) or a receiver coupled with the interface.
  • the map data processing device 50 when the map data processing device 50 can be the user end of the above-mentioned map including risk event information, the map data processing device 50 further includes a display 505, and the display 505 is connected or coupled to the processor 501 through the bus 504 .
  • the display 505 is used to generate a map display interface according to the risk event information.
  • the display 505 can be a display screen, and the display screen can be a liquid crystal display (Liquid Crystal Display, LCD), an organic or inorganic light-emitting diode (Organic Light-Emitting Diode, OLED), an active matrix organic light-emitting diode panel (Active Matrix/Organic Light Emitting Diode, AMOLED), etc.
  • the display 505 may also be a vehicle-mounted panel, a vehicle-mounted display, or a head-up display (HUD) system or the like.
  • the processor 501 in the map data processing device 50 is used to read the computer program stored in the memory 503 to execute the aforementioned method, such as the method described in FIG. 3 , FIG. 8 or FIG. 9 .
  • the map data processing device 50 can be one or more modules in the execution subject of the method shown in FIG. 3 , FIG. 8 or FIG. 9 (cloud device side), and the processor 501 can be used for Read one or more computer programs stored in memory for:
  • the risk event information includes time information and location information, the time information is used to indicate the predicted time range of the risk event, and the location information is used to indicate the predicted area range of the risk event;
  • the risk event information is stored as map data through the storage unit 312;
  • the map data processing device 50 can be one or more modules in the execution subject of the method shown in FIG. 8 or FIG. 9, and the processor 501 can be used to read a or more computer programs for:
  • the risk event information is received by the receiving unit 410, the risk event information includes time information and location information, the time information is used to indicate the predicted time range of the risk event, and the location information is used to indicate the predicted area range of the risk event;
  • the display unit 414 generates a map display interface according to the risk event information.
  • the embodiment of the present application also provides a communication system, the communication system includes a first map data processing device and a second map data processing device, wherein the first map data processing device can be, for example, the map data processing device shown in Figure 13 30, may also be the map data processing device 50 described in Figure 15 as the map generation end; the second map data processing device may be, for example, the map data processing device 40 shown in Figure 14, or the map data processing device 50 as described in Figure 15.
  • the first map data processing device can be used to execute the method described in the embodiment of FIG. 3 and FIG. 8 and the method on the cloud device side described in the embodiment of FIG. 9
  • the second map data processing device can be used to execute the method described in the embodiment of FIG. 8 above.
  • storage medium includes read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), programmable read-only memory (Programmable Read-only Memory, PROM), erasable programmable read-only memory ( Erasable Programmable Read Only Memory, EPROM), One-time Programmable Read-Only Memory (OTPROM), Electrically-Erasable Programmable Read-Only Memory, EEPROM, Compact Disc Read-Only Memory (CD-ROM) or other optical disk storage, magnetic disk storage, tape storage, or any other computer-readable medium that can be used to carry or store data.
  • Read-Only Memory Read-Only Memory
  • RAM Random Access Memory
  • PROM Programmable Read-only Memory
  • PROM Programmable Read-only Memory
  • EPROM Erasable Programmable Read Only Memory
  • OTPROM One-time Programmable Read-Only Memory
  • EEPROM Electrically-Erasable Programmable Read-Only Memory
  • CD-ROM Compact Disc Read-Only Memory
  • the essence of the technical solution of the present application or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of software products.
  • the computer program product is stored in a storage medium, including several instructions. So that a device (which may be a personal computer, a server, or a network device, a robot, a single-chip microcomputer, a chip, a robot, etc.) executes all or part of the steps of the methods described in the various embodiments of the present application.

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Abstract

本申请公开了一种地图数据处理方法及装置，该方法包括：获取包括时间信息和位置信息的风险事件信息，其中，时间信息用于指示风险事件的预测时间范围，位置信息用于指示风险事件的预测区域范围。实施本申请，能够提供在一定地理区域范围内可能发生的风险事件的预测，提高了地图信息的丰富程度。另外，还可以根据风险事件信息规划最低风险路径，不仅丰富了车辆的出行策略，还有利于提高车辆出行的安全性。

Description

一种地图数据处理方法及装置 技术领域
本申请涉及地图领域，尤其涉及一种地图数据处理方法及装置。
背景技术
地图技术的发展为人们在出行领域带来了极大的便利。例如，通过地图实现路径规划，为出行提供导航信息，或者，通过地图显示事故或拥堵路段，以方便出行者规避拥堵或事故路段，进行新的路径规划。但是，未来的智能驾驶和智能交通对地图信息的丰富程度提出了更高的要求，现有的地图内容丰富程度还不能充分满足未来使用的需求。
发明内容
本申请公开了一种地图数据处理方法及装置，能够提供在一定地理区域范围内有可能发生的风险事件的预测信息，提高了地图信息的丰富程度。
第一方面，本申请提供了一种地图数据处理方法，该方法包括：获取风险事件信息，风险事件信息包括时间信息和位置信息，时间信息用于指示风险事件的预测时间范围，位置信息用于指示风险事件的预测区域范围；将风险事件信息作为地图数据存储。
其中，风险事件是指影响车辆的行驶(或驾驶)安全性或出行顺畅性的事件，或者说，风险事件是对车辆的行驶(或驾驶)安全或出行顺畅存在潜在影响的事件。
预测时间范围是风险事件可能发生的时间范围，并不一定是风险事件实际发生的时间范围。预测区域范围是风险事件可能发生的区域范围，并不一定是风险事件实际发生的区域范围。
该方法可用于网络侧设备、路侧设备或终端。其中，网络侧设备例如可以是部署在网络侧的服务器(例如应用服务器或地图服务器)，或者为该服务器中的组件或者芯片。网络侧设备可以部署在云环境或者边缘环境中，本申请实施例不做具体限定。路侧设备例如可以是路侧单元(Road Side Unit，RSU)、多接入边缘计算(Multi-Access Edge Computing，MEC)或者传感器等装置，或者是这些装置内部的组件或者芯片，也可以是由RSU和MEC组成的系统级设备，或者是由RSU和传感器组成的系统级设备，还可以是由RSU、MEC和传感器组成的系统级设备。终端例如可以是车辆、便携移动设备(例如，手机、平板等)等任一种设备、或可以是上述任一种设备内的装置、部件或芯片，例如车载单元(On Board Unit,OBU)，本申请实施例不做具体限定。
上述方法中，获取的风险事件信息，能够提供一定地理区域范围内有可能发生的风险事件的预测信息，例如，风险事件的预测时间范围和风险事件的预测区域范围。将风险事件信息作为地图数据存储，不仅可以提高地图数据的丰富程度，也使得该地图数据可以满足更加丰富的使用需求。
可选地，该方法还包括：根据风险事件信息规划最低风险路径。
实施上述实现方式，最低风险路径使得用户的出行策略更加丰富，还有利于提高车辆出 行的安全性。
可选地，该方法还包括：向用户推荐最低风险路径，或者控制车辆沿最低风险路径行驶。
实施上述实现方式，最低风险路径为用户的出行策略提供了另一种选择。控制车辆沿最低风险路径行驶，使得车辆能较为安全地行驶。
可选地，该方法还包括：向用户推荐多条路径，多条路径包括最低风险路径；从用户接收反馈信息，反馈信息用于指示用户从多条路径中选择的路径；控制车辆沿选择的路径行驶。
实施上述实现方式，多条路径除了包括最低风险路径外，还可以包括用时最少路径、距离最短路径、步行最少路径、最阴凉路径等中的至少一条，大大丰富了用户的出行策略，用户也可以结合自身实际需求自由选择中意的路径，提高了用户的交互体验感。
可选地，根据风险事件信息规划最低风险路径，包括：根据风险事件信息和车辆的感知能力，规划车辆的最低风险路径；根据风险事件信息和车辆的车辆类型，规划车辆的最低风险路径；或者根据风险事件信息和车辆处于的自动驾驶级别，规划车辆的最低风险路径。
车辆通过自身的感知元件(或称为车载传感器)可以有效感知周围环境。车辆的感知能力包括对车辆的感知和对环境的感知。其中，用于感知车辆的器件包括动力、底盘、车身及电子电气系统中的传感器等，用于感知环境的器件包括车载摄像头、毫米波雷达、激光雷达等。不同车辆配置的感知元件不同，导致不同车辆的感知能力不同，从而具有不同感知能力的车辆对相同风险事件的应对能力可能也不同。例如，某辆车装载的传感器在黑暗环境中感知能力较低，根据风险事件信息预测某路段在夜间时段行驶风险较大，则在为该车辆规划夜间出行路线时，尽量避免风险事件信息所指示的该路段。
车辆类型可以有多种划分，例如，基于动力装置类型可分为内燃机车辆、电动车辆、喷气车辆等。基于空间大小可分为微型车、小型车、紧凑型车、中型车、中大型车等。在一些可能的实施例中，还可以是其他形式的划分，本申请对车辆类型的划分方式不作具体限定。不同类型的车辆对相同风险事件的应对能力可能也不同。例如，某路段坡度较大，且预计某时间段将下雪，根据风险事件信息预测该路段在该时间段行驶风险较大，则在为某爬坡能力较差的车型规划涉及该时间段的出行路线时，尽量避免风险事件信息所指示的该路段。
自动驾驶级别可分为六个级别，级别从低至高依次为L0(无自动驾驶)、L1(驾驶支援)、L2(部分自动驾驶)、L3(有条件的自动驾驶)、L4(高度自动驾驶)和L5(完全自动驾驶)。级别越高，表示车辆的自动驾驶能力越强。车辆处于不同的自动驾驶级别时，对相同风险事件的应对能力可能也不同。例如，根据风险事件信息预测某时间段内某路段因人流量较大导致行驶风险较大，则针对选择高级别自动驾驶状态的车辆规划涉及该时间段的出行路线时，尽量避免风险事件信息所指示的该路段。
实施上述实现方式，结合车辆的感知能力、车辆类型或车辆所处的自动驾驶级别规划最低风险路径，可使得最低风险路径与车辆的风险应对能力匹配，还提高了最低风险路径的精准性。
可选地，最低风险路径为：无风险事件的路径；没有风险等级超于风险阈值的风险事件的路径；或者没有特定风险类型的风险事件的路径。
最低风险路径为无风险事件的路径可以理解为在一定时间范围内最低风险路径经过的区域都是无风险事件的区域。最低风险路径为没有风险等级超过风险阈值的风险事件的路径可以理解为在一定时间范围内最低风险路径经过的区域存在预测的风险事件，但风险事件的风 险等级不超过风险阈值(即认为风险等级的取值越大，风险事件越危险)。最低风险路径为没有特定风险类型的风险事件的路径可以理解为在一定时间范围内最低风险路径经过的区域存在预测的风险事件，但风险事件的风险类型并不是特定风险类型。
可选地，根据风险事件信息规划最低风险路径，包括：规划多条路径；确定预测区域范围包括多条路径中的位置点；预估车辆在预计时间行驶至位置点；根据风险事件信息中的时间信息和位置信息确定与预测区域范围相对应的预测时间范围；确定预测时间范围包括预计时间；根据风险事件确定多条路径中至少一条路径的行驶风险；根据行驶风险确定多条路径中的最低风险路径。
实施上述实现方式，最低风险路径为多条路径中行驶风险最低的路径。最低风险路径的确定需结合空间和时间两个维度，即规划路径时需综合考虑路径中的各个位置点以及车辆到达各个位置点的预计时间，尽可能使得预计时时间对应的位置点不属于风险事件所在的预测区域范围或者尽可能使得各个预计时间不属于风险事件的预测时间范围。
例如，假设某车辆从A点经过B点到达C点，且A、B、C三点附近当前时刻均存在风险事件，但规划最低风险路径时，不仅根据当前时刻这三个位置点的风险事件为该车辆规划路径，还综合考虑车端设备分别到达A、B、C三点的预计时间，示例性地，预计t1时刻到达A点、t2时刻到达B点以及t3时刻到达C点，则分别使用t1时刻A点附近的风险事件信息、t2时刻B点附近的风险事件信息以及t3时刻C点附近的风险事件信息来规划路径。
可选地，获取风险事件信息，包括：生成风险事件信息，或者，接收风险事件信息。
其中，在获取风险事件信息为生成风险事件信息时，上述方法的执行主体可以是网络侧设备、路侧设备或者终端，例如为地图服务器、地图服务器中的部件或者芯片、路侧设备、车辆或者移动终端，或者为路侧设备、车辆或者移动终端内的部件或者芯片；在获取风险事件信息为接收风险事件信息时，上述方法的执行主体也可以为网络侧设备、路侧设备或者终端，例如为使用风险事件信息提供服务的应用服务器、使用风险事件信息提供路侧指示信息的路侧设备、使用风险事件信息进行驾驶的车辆、便携终端(如手机、便携电脑或导航仪)，还例如为可应用于上述设备的部件、芯片或应用程序。
可选地，该方法用于车辆，或者，该方法还包括向车辆发送风险事件信息，车辆满足以下条件中的至少一项：
车辆在预测区域范围内；
车辆距离预测区域范围的最小距离小于第一阈值；
预测区域范围与车辆的规划路径有交集；
预测区域范围距离车辆的规划路径的最小距离小于第二阈值；
预测区域范围所属的瓦片为车辆所在的瓦片；和
预测区域范围所属的瓦片为车辆的规划路径所经过的瓦片。
当该方法用于车辆，即由车辆获取风险事件信息并将风险事件信息作为地图数据存储时，车辆可以基于当前所在位置或者自身的规划路径，获取并存储一定地理范围区域内或者与导航路径相关的区域的地图数据，从而获取并存储与自身关联性较大的局部区域的风险事件信息，如此，可以节省地图数据在车内的存储空间，并提高车辆利用风险事件信息的效率。
另外，当向车辆发送风险事件信息，即说明车辆也可以作为风险事件信息的使用者，在此情况下，作为使用者的车辆需满足上述条件对车辆所在位置的限定，使作为风险事件信息 使用者的车辆获取与其关联性较大的局部区域的风险事件信息，如此，可以节省数据传输流量，也为使用风险事件信息的车辆节省地图数据在车内的存储空间，并提高车辆利用风险事件信息的效率。
可选地，预测区域范围位于地图中的道路或者车道。
实施上述实现方式，可以对预测区域范围进行道路级的几何表达或者进行车道级的几何表达。
可选地，风险事件信息还包括以下内容中的至少一项：风险事件的标识信息、风险事件所属的瓦片的标识信息、风险事件所在的道路的标识信息、风险等级信息、风险类型信息、预警信息、影响风险事件的动态要素的标识信息和受风险事件影响的地图要素的信息；其中，风险等级信息用于指示风险事件的危险程度，风险类型信息用于指示风险事件的类型，预警信息用于指示基于该风险事件向驾驶员或驾驶系统提醒的内容。
风险等级信息是风险事件信息的一种可选信息。基于风险等级信息对风险事件的风险程度进行阶梯量化，能有效区分不同危险程度的风险事件，有利于分清轻重缓急。
风险类型信息是风险事件信息的一种可选信息。基于风险类型信息可以快速明确可能面临的是何种类型的风险，从而可以更好地应对风险事件。例如，风险类型信息可以从风险事件产生的原因对风险事件进行分类描述，也可以从风险事件导致的结果对风险事件进行分类描述，具体分类的方式还有多种可能，本申请在此不做限定。
影响风险事件的动态要素的标识信息是风险事件信息的一种可选信息。基于影响风险事件的动态要素的标识信息可以快速索引风险事件关联的动态要素，在检测到关联的动态要素发生变化时，可以快速实现风险事件信息的联动更新。例如，交通拥堵事件是一种动态要素，很可能导致车辆剐蹭，则将交通拥堵事件的标识作为关于车辆剐蹭的风险事件信息的内容。
受风险事件影响的地图要素的信息是风险事件信息的一种可选信息。基于受风险事件影响的地图要素的信息可以快速索引相应的地图要素，且当风险事件变化时，可以实现受风险事件影响的地图要素的信息的联动更新，不仅提高了地图数据的准确率，还提高了地图数据的更新效率。
预警信息是风险事件信息的一种可选信息。基于预警信息可及时提醒驾驶员前方区域的风险事件，有利于提高车辆行驶的安全性。
风险事件的标识信息、风险事件所属的瓦片的标识信息以及风险事件所在的道路的标识信息均是风险事件信息的可选信息。将风险事件与地图瓦片的标识关联，可以基于地图瓦片的标识实现风险事件的快速索引。将风险事件与道路的标识关联，可以基于道路的标识实现风险事件的快速索引。由此，有效提高了风险事件的搜索效率以及风险事件信息的发布效率。
可选地，该方法还包括：确定影响风险事件的要素发生变化，要素位于地图的静态图层或者动态图层；根据变化后的要素更新风险事件信息或者消除风险事件信息。
实施上述实现方式，当确定影响风险事件的要素发生变化时，基于要素的标识与风险事件的标识之间的映射关系，可以实现风险事件信息的联动更新，或者，消除风险事件信息。风险事件信息的消除，意味着预测到对应的风险事件将不发生。
可选地，该方法还包括：根据风险事件信息，执行路况监控、交通调度、路径规划或对车辆的控制。
实施上述实现方式，可以基于风险事件信息提供多种应用服务，例如，路况监控、交通 调度等宏观上的调控服务，以及路径规划、对车辆的控制等个性化的、定制性地服务。
可选地，该方法还包括：确定车辆即将经过风险事件所在的预测区域范围；控制车辆执行以下操作中的至少一项：变换车道；调整行驶速度；更新导航路线；开启警示灯；和向驾驶员提示风险事件。
实施上述实现方式，在车辆即将经过风险事件所在的预测区域范围时，提供了多种应对策略，使得车辆能及时准确地应对风险事件，提高车辆出行的安全性。
可选地，该方法还包括：根据地图中的道路拓扑信息确定与预测区域范围相关联的道路区域；向道路区域内的车辆提示风险事件，或者管控道路区域的车流量。
道路区域与预测范围区域相关联包括但不限于：道路区域与预测区域范围之间的距离小于等于预设距离阈值；道路区域与预测区域范围之间交通可达；或者道路区域内的车辆预计到达风险事件所在的预测区域范围的时刻属于风险事件对应的预测时间范围。道路区域可以是一条或多条车道，也可以是一条或多条道路，还可以是至少一条车道和至少一条道路构成的集合。
实施上述实现方式，基于风险事件信息和地图中的道路拓扑信息可以预先对一定地理区域范围内的车辆进行风险事件提示，还能管控相应地理区域范围内的车流量，实现交通调度，以尽可能避免交通事故的发生。
可选地，该方法还包括通过以下方式中的至少一种在地图显示界面上呈现风险事件信息。
根据时间信息和位置信息在界面上动态播放风险事件的变化；实施此方式，用户预先可以准确知晓附近区域内的风险事件的变化趋势，方便及时调整自身的应对策略，提高了自身的安全性。
标记风险事件中在当前时间的风险等级超过阈值的至少一个风险事件的预测区域范围和至少一个风险事件的描述信息；实施此方式，通过对标记的风险事件的风险等级进行限制，以过滤不符合阈值条件的风险事件，从而用户可以重点关注风险等级超过阈值的风险事件。
标记风险事件中与导航路径相关的至少一个风险事件的预测区域范围和至少一个风险事件的描述信息；进一步地，与导航路径相关的风险事件需满足空间和时间两个维度，例如，导航路径指示从A点经过B点到达C点，预计车辆到达A、B、C三点的时间分别为t1、t2、t3，则与导航路径相关的风险事件包括：t1时刻A点附近的风险事件、t2时刻B点附近的风险事件和t3时刻C点附近的风险事件。在一些可能的实施例中，考虑到导航路径中各位置的远近，可以优先显示导航路径中距离当前位置距离最近的位置点附近的风险事件。
标记用户选择的时间下的至少一个风险事件的预测区域范围和至少一个风险事件的描述信息；实施此方式，用户可以选择感兴趣的时段并基于选中的时段显示对应风险事件的相关信息。
标记符合用户选择的风险类型的至少一个风险事件的预测区域范围和至少一个风险事件的描述信息；实施此方式，用户可以选择感兴趣的风险类型并基于选中的风险类型显示对应风险事件的相关信息。
用不同颜色标记不同风险等级的风险事件所对应的预测区域范围；和
用不同颜色标记不同风险类型的风险事件所对应的预测区域范围。
通过不同颜色标记不同风险等级的风险事件对应的预测区域范围，可以基于颜色有效区分不同风险等级的风险事件，还直观地展示了地图中不同风险等级的风险事件的分布。
通过不同颜色标记不同风险类型的风险事件对应的预测区域范围，可以基于颜色有效区分不同风险类型的风险事件，还直观地展示了地图中不同风险类型的风险事件的分布。
可选地，地图数据包括第一静态图层数据和第一动态图层数据，风险事件信息为根据第一静态图层数据和第一动态图层数据得到。
进一步地，可以从第一静态图层数据和第一动态图层数据中提取与风险事件相关的地图数据。第一静态图层数据例如可以指示道路类型、车道数量、路面坑洼信息(例如，深度、位置信息、面积等)等不频繁发生变化的道路状态；第一动态图层数据例如可以指示随时间变化较频繁的降水量、降雪量、能见度、光照强度、风向、风力、雷电指数等天气情况，还可以指示路面施工信息(例如，是否有施工、施工位置、施工时长等)、路面覆盖物信息(例如，积冰厚度、积水深度、落叶量)等较频繁发生变化的道路状态。
实施上述实现方式，地图数据中的静态图层数据以及动态图层数据为风险事件信息的获取提供了可能，而风险事件信息增加了地图数据的丰富程度。
可选地，将风险事件信息作为地图数据存储，包括：将风险事件信息作为地图数据的第二动态图层数据存储。
实施上述实现方式，风险事件信息可以以地图的动态图层的形式表达，承载了风险事件信息的动态图层可以单独显示，也可以与地图中的其他至少一个图层(例如，静态图层、仅承载了天气信息的动态图层等)叠加显示。
可选地，风险事件信息存储于风险事件的标识对应的数据结构中。基于风险事件的标识可以在地图中快速索引到对应的风险事件。
可选地，风险事件信息存储于预测时间范围对应的数据结构中。如此，可以为用户提供任一预测时间范围对应的风险事件信息，基于预测时间范围下发风险事件信息可以提高数据传输效率。
第二方面，本申请提供了一种地图数据处理装置，该装置包括：获取单元，用于获取风险事件信息，风险事件信息包括时间信息和位置信息，时间信息用于指示风险事件的预测时间范围，位置信息用于指示风险事件的预测区域范围；存储单元，用于将风险事件信息作为地图数据存储。
可选地，该装置还包括：规划单元，用于根据风险事件信息规划最低风险路径。
可选地，该装置还包括：第一处理单元，用于向用户推荐最低风险路径，或者控制车辆沿最低风险路径行驶。
可选地，该装置还包括：第二处理单元，用于向用户推荐多条路径，多条路径包括最低风险路径；接收单元，用于从用户接收反馈信息，反馈信息用于指示用户从多条路径中选择的路径；第二处理单元，还用于控制车辆沿选择的路径行驶。
可选地，规划单元具体用于：根据风险事件信息和车辆的感知能力，规划车辆的最低风险路径；根据风险事件信息和车辆的车辆类型，规划车辆的最低风险路径；或者根据风险事件信息和车辆处于的自动驾驶级别，规划车辆的最低风险路径。
可选地，最低风险路径为：无风险事件的路径；没有风险等级超于风险阈值的风险事件的路径；或者没有特定风险类型的风险事件的路径。
可选地，规划单元具体用于：规划多条路径；确定预测区域范围包括多条路径中的位置点；预估车辆在预计时间行驶至位置点；根据风险事件信息中的时间信息和位置信息确定与 预测区域范围相对应的预测时间范围；确定预测时间范围包括预计时间；根据风险事件确定多条路径中至少一条路径的行驶风险；根据行驶风险确定多条路径中的最低风险路径。
可选地，获取单元具体用于：生成风险事件信息，或者，接收风险事件信息。
可选地，该装置为车辆，或者，该装置还包括向车辆发送风险事件信息的发送单元，车辆满足以下条件中的至少一项：
车辆在预测区域范围内；
车辆距离预测区域范围的最小距离小于第一阈值；
预测区域范围与车辆的规划路径有交集；
预测区域范围距离车辆的规划路径的最小距离小于第二阈值；
预测区域范围所属的瓦片为车辆所在的瓦片；和
预测区域范围所属的瓦片为车辆的规划路径所经过的瓦片。
可选地，预测区域范围位于地图中的道路或者车道。
可选地，风险事件信息还包括以下内容中的至少一项：风险事件的标识信息、风险事件所属的瓦片的标识信息、风险事件所在的道路的标识信息、风险等级信息、风险类型信息、预警信息、影响风险事件的动态要素的标识信息和受风险事件影响的地图要素的信息；其中，风险等级信息用于指示风险事件的危险程度，风险类型信息用于指示风险事件的类型，预警信息用于指示基于该风险事件向驾驶员或驾驶系统提醒的内容。
可选地，该装置还包括第三处理单元，用于：确定影响风险事件的要素发生变化，要素位于地图的静态图层或者动态图层；根据变化后的要素更新风险事件信息或者消除风险事件信息。
可选地，该装置还包括第四处理单元，用于：根据风险事件信息，执行路况监控、交通调度、路径规划或对车辆的控制。
可选地，该装置还包括第五处理单元，用于：确定车辆即将经过风险事件所在的预测区域范围；控制车辆执行以下操作中的至少一项：变换车道；调整行驶速度；更新导航路线；开启警示灯；和向驾驶员提示风险事件。
可选地，该装置还包括第六处理单元，用于：根据地图中的道路拓扑信息确定与预测区域范围相关联的道路区域；向道路区域内的车辆提示风险事件，或者管控道路区域的车流量。
可选地，该装置还包括显示单元，显示单元用于通过以下方式中的至少一种在地图显示界面上呈现风险事件信息：
根据时间信息和位置信息在界面上动态播放风险事件的变化；
标记风险事件中在当前时间的风险等级超过阈值的至少一个风险事件的预测区域范围和至少一个风险事件的描述信息；
标记风险事件中与导航路径相关的至少一个风险事件的预测区域范围和至少一个风险事件的描述信息；
标记用户选择的时间下的至少一个风险事件的预测区域范围和至少一个风险事件的描述信息；
标记符合用户选择的风险类型的至少一个风险事件的预测区域范围和至少一个风险事件的描述信息；
用不同颜色标记不同风险等级的风险事件所对应的预测区域范围；和
用不同颜色标记不同风险类型的风险事件所对应的预测区域范围。
可选地，地图数据包括第一静态图层数据和第一动态图层数据，风险事件信息为根据第一静态图层数据和第一动态图层数据得到。
可选地，存储单元，具体用于：将风险事件信息作为地图数据的第二动态图层数据存储。
可选地，风险事件信息存储于风险事件的标识对应的数据结构中。
可选地，风险事件信息存储于预测时间范围对应的数据结构中。
第三方面，本申请提供了一种电子地图，电子地图包括风险事件信息，风险事件信息包括时间信息和位置信息，时间信息用于指示风险事件的预测时间范围，位置信息用于指示风险事件的预测区域范围。
所述电子地图为地图产品，具体来说，可以是承载风险事件信息的地图数据产品，如地图更新数据包，或者可以为加载风险事件信息的地图应用产品，如可安装于车辆或便携终端上的地图应用程序，或者还可以为以图形和/或文字形式等呈现风险事件信息的地图展示产品，如电子导航仪。
可选地，该风险事件信息在电子地图中以事件的数据结构进行存储。
可选地，该风险事件信息在电子地图中作为动态图层数据存储。
可选地，该风险事件信息还包括以下内容中的至少一项：风险事件的标识信息、风险事件所属的瓦片的标识信息、风险事件所在的道路的标识信息、风险等级信息、风险类型信息、预警信息、影响风险事件的动态要素的标识信息和受风险事件影响的地图要素的信息；其中，风险等级信息用于指示风险事件的危险程度，风险类型信息用于指示风险事件的类型，预警信息用于指示基于该风险事件向驾驶员或驾驶系统提醒的内容。
第四方面，本申请提供了一种地图数据处理装置，该装置包含至少一个处理器以及通信接口，所述通信接口用于为所述至少一个处理器提供信息输入和/或输出。该装置用于实现第一方面或者第一方面任一可能的实施例中的所述方法。
该地图数据处理装置可以是网络侧设备、路侧设备或终端。
其中，网络侧设备例如可以是部署在网络侧的服务器(例如应用服务器或地图服务器)，或者为该服务器中的组件或者芯片。网络侧设备可以部署在云环境或者边缘环境中，本申请实施例不做具体限定。路侧设备例如可以是路侧单元(Road Side Unit，RSU)、多接入边缘计算(Multi-Access Edge Computing，MEC)或者传感器等装置，或者是这些装置内部的组件或者芯片，也可以是由RSU和MEC组成的系统级设备，或者是由RSU和传感器组成的系统级设备，还可以是由RSU、MEC和传感器组成的系统级设备。终端可以是车辆、智能穿戴设备(例如，运动手环、手表等)、便携移动设备(例如，手机、平板等)等任一种设备、或可以是上述任一种设备内的装置、部件或芯片，例如车载单元(On Board Unit，OBU)。本申请实施例不做具体限定。需要说明的是，地图数据处理装置可以是上述电子地图的生成端，也可以是上述电子地图的使用端，在此不作具体限定。
第五方面，本申请提供了一种计算机可读存储介质，包括计算机指令，当所述计算机指令在被处理器运行时，实现上述第一方面或者第一方面的任一可能的实现方式中的方法。
第六方面，本申请提供了一种计算机程序产品，当该计算机程序产品被处理器执行时，实现上述第一方面或者第一方面的任一可能的实施例中的所述方法。该计算机程序产品，例如可以为一个软件安装包，在需要使用上述第一方面的任一种可能的设计提供的方法的情况 下，可以下载该计算机程序产品并在处理器上执行该计算机程序产品，以实现第一方面或者第一方面的任一可能的实施例中的所述方法。
第七方面，本申请提供了一种电子信息，该电子信息承载风险事件信息，风险事件信息包括时间信息和位置信息，时间信息用于指示风险事件的预测时间范围，位置信息用于指示风险事件的预测区域范围。
可选地，该电子信息是电、磁或电磁信号的集合，通过电、磁或电磁这种载体的形式承载地图信息。
第八方面，提供一种计算机可读存储介质，该计算机可读存储介质具备信息输入接口，该信息输入接口能够接收上述第七方面或第七方面的任意一种可能的实现方式所描述的电子信息，并将该电子信息承载的风险事件信息存储于该计算机可读存储介质中。
第九方面，本申请提供了一种车辆，该车辆包括如上述第二方面或第二方面的任一可能的实现方式的地图数据处理装置，或者包括如上述第四方面或第四方面的任一可能的实现方式的地图数据处理装置。
第十方面，本申请提供了一种系统，该系统该包括第一地图数据处理装置和第二地图数据处理装置。其中，该第一地图数据处理装置用于执行，当该获取风险事件信息为生成风险事件信息时的上述第一方面或第一方面的任意一种可能的实现方式中的地图数据处理方法；该第二地图数据处理装置用于执行，当该获取风险事件信息为接收风险事件信息时的上述第一方面或第一方面的任意一种可能的实现方式中的地图数据处理方法。
上述第二方面至第十方面的技术效果与上述第一方面相同，在此不再赘述。
附图说明
图1是本申请实施例提供的一种场景示意图；
图2是本申请实施例提供的一种系统架构的示意图；
图3是本申请实施例提供的一种地图数据处理方法的流程图；
图4是本申请实施例提供的一些风险事件的预测区域范围的表达示意图；
图5A是本申请实施例提供的风险事件信息的一种表达方式的示意图；
图5B是本申请实施例提供的风险事件信息的又一种表达方式的示意图；
图6是本申请实施例提供的风险事件信息的又一种表达方式的示意图；
图7是本申请实施例提供的一种不同预测时段的风险事件信息的表达示意图；
图8是本申请实施例提供的又一种地图数据处理方法的流程图；
图9是本申请实施例提供的又一种地图数据处理方法的流程图；
图10是本申请实施例提供的一种路径推荐的界面示意图；
图11是本申请实施例提供的一种显示装置的界面示意图；
图12是本申请本实施例提供的一种地图显示界面的示意图；
图13是本申请本实施例提供的一种地图数据处理装置的功能结构示意图；
图14是本申请本实施例提供的一种地图数据处理装置的功能结构示意图；
图15是本申请本实施例提供的又一种地图数据处理装置的结构示意图。
具体实施方式
需要说明的是，本申请实施例中采用诸如“第一”、“第二”的前缀词，仅仅为了区分不同的描述对象，对被描述对象的位置、顺序、优先级、数量或内容等没有任何限定作用。例如，被描述对象为“字段”，则“第一字段”和“第二字段”中“字段”之前的序数词并不限制“字段”之间的位置或顺序，“第一”和“第二”并不限制其修饰的“字段”是否在同一个消息中，也不限制“第一字段”和“第二字段”的先后顺序。再如，被描述对象为“等级”，则“第一等级”和“第二等级”中“等级”之前的序数词并不限制“等级”之间的优先级。再如，被描述对象的数量并不受前缀词的限制，可以是一个或者多个，以“第一设备”为例，其中“设备”的数量可以是一个或者多个。此外，不同前缀词修饰的对象可以相同或不同，例如，被描述对象为“设备”，则“第一设备”和“第二设备”可以是同一个设备、相同类型的设备或者不同类型的设备；再如，被描述对象为“信息”，则“第一信息”和“第二信息”可以是相同内容的信息或者不同内容的信息。总之，本申请实施例中对用于区分描述对象的前缀词的使用不构成对所描述对象的限制，对所描述对象的陈述参见权利要求或实施例中上下文的描述，不应因为使用这种前缀词而构成多余的限制。
需要说明的是，本申请实施例中采用诸如“a1、a2、……和an中的至少一项(或至少一个)”等的描述方式，包括了a1、a2、……和an中任意一个单独存在的情况，也包括了a1、a2、……和an中任意多个的任意组合情况，每种情况可以单独存在。例如，“a、b和c中的至少一项”的描述方式，包括了单独a、单独b、单独c、a和b组合、a和c组合、b和c组合，或abc三者组合的情况。
为了便于理解，下面先对本申请实施例可能涉及的相关术语等进行介绍。
地图是地理信息的载体。在一种地图结构中，地图包括多个图层(Layer)，图层可以理解为地图数据集，地图数据集中的数据以设定的数据结构进行组织。图层中的数据能够描述多种来源的地图要素。根据地图要素的时变性，地图要素可分为元素和事件两种类型：元素是比较固定、变化小或者更新周期较长的地图要素，例如道路拓扑、建筑物位置、车道线、车道方向或交通基础设施布局等；事件是具有较强时变特性的地图要素，例如，交通事故、天气变化、路面结冰、道路施工或交通拥堵情况等。
在地图中，元素和事件可以被记录于不同的图层，例如，关于元素的信息由地图中的静态图层承载，关于事件的信息由地图中的动态图层承载。地图中可以包括一个或多个静态图层，还可以进一步包括一个或多个动态图层。例如，某地图包括一个静态图层和多个动态图层，静态图层中记录了建筑物、道路、树木、交通灯和道路指示牌的地理分布，动态图层1记录了车道的实时限速情况、交通施工情况和人流车流情况，动态图层2记录了天气情况，例如晴天、下雨、下雪、刮风、温度或湿度等。对于某个地图描述对象而言，其可能兼具时变性的地图要素和非时变性的地图要素，非时变性的地图要素是指比较固定、变化较小或者更新周期较长的地图要素，即该描述对象既与地图中的元素相关，也与地图中的事件相关。例如，针对某一个车道，该车道的地理位置为地图中的元素，该车道的交通流量为地图中的事件，该车道的限速为地图中的事件，该车道的允许通行时段为地图中的事件。针对某一个交通灯，该交通灯在路口中的位置为地图中的元素，该交通灯的亮灯变化为地图中的事件。
地图的静态图层中的数据可以称为元素或静态要素，地图的动态图层中的数据可以称为事件或动态要素。
在本申请实施例中，风险事件是指影响车辆的行驶(或驾驶)安全性或出行顺畅性的事 件，或者说，风险事件是对车辆的行驶(或驾驶)安全或出行顺畅存在潜在影响的事件。每个风险事件有对应的时间信息和位置信息，时间信息用于指示风险事件的预测时间范围，位置信息用于指示风险事件的预测区域范围。预测时间范围是预测到的风险事件有可能发生的时间范围，并不一定是风险事件实际发生的时间范围。同理，预测区域范围是预测到的风险事件有可能发生的区域范围，并不一定是风险事件实际发生的区域范围。该风险事件是一种预测有发生可能性的风险事件，可能真实发生，也可能未真实发生；另外，预测时间范围可以是未来的时间范围，这样可以为出行者提供提前的风险事件信息，用于更好的出行规划；或者预测时间范围可以包括当前时间点和未来时间的时间范围，甚至可以包括过去时间点。
例如，风险事件1指示地图中的区域范围A在时间段9:00-10:00有风险，其中，9:00-10:00为风险事件1的预测时间范围，区域范围A为风险事件1的预测区域范围。在一些可能的实施例中，风险事件有不同的类型，例如，变道碰撞、打滑碰撞、可见度降低、刹车距离减小等。可以理解，类型不同，则风险事件对应的风险不同。
地图中的路网结构数据可分为瓦片级、道路级和车道级，地图中的每个瓦片有唯一的瓦片标识(Identification，ID)，每个瓦片内包括多条道路，每条道路有唯一的道路ID，每条道路又包括多条车道，每条车道有唯一的车道ID。其中，瓦片可以理解为：将一定范围内的地图按照一定的尺寸和格式，以及不同的地图分辨率，切成若干行和列的矩形栅格图片，对切片后的矩形栅格图片称为瓦片(Tile)。
参见图1，图1是本申请实施例的一种应用场景示意图。在图1中，假设当前时刻为14:00，车辆欲从位置A到达位置D，在进行路径规划时，车载地图提示路段BC中存在易出现事故的区域1请尽可能绕行，于是车辆确定行驶路径为A→B→E→F→C→D，该路径相较于路径A→B→C→D需要多花费半小时。但实际上，区域1仅在当天的特定时段9:30-11:00发生事故的概率较大，而区域1在当天的其他时段都是安全的，也就是说，在当前时刻为14:00时，区域1是安全的，则车辆的最优路径规划应该为路径A→B→C→D。由此可以看出，由于地图信息的缺失或者不完善，导致车辆无法作出最优的驾驶决策，降低了驾驶决策的准确率，导致车辆出行效率下降。
针对上述问题，本申请实施例提出一种地图数据处理方法，能够为车辆提供一定地理区域范围内可能发生的风险事件的预测信息，以使车辆可获取风险事件对应的预测时间范围和预测区域范围，有利于提高驾驶的安全性以及驾驶决策的准确率，从而提高车辆出行效率。
下面将结合附图，对本申请中的技术方案进行描述。
参见图2，图2示例性地给出了本申请实施例的一种系统架构图。该系统用于电子地图的生成或使用，该电子地图包括风险事件信息，风险事件信息包括风险事件的位置信息和风险事件的时间信息。如图2所示，该系统包括网络侧设备、路侧设备和终端中的至少一项。其中，终端可以分别与网络侧设备、路侧设备以无线的方式进行通信，网络侧设备与路侧设备可以通过无线或者有线的方式进行通信。
电子地图可以由网络侧设备、路侧设备或终端中的任意一者生成。
其中，网络侧设备可以是具有计算功能的设备，例如：例如可以是部署在网络侧的服务器(例如应用服务器或地图服务器)，或者为该服务器中的组件或者芯片。网络侧设备可以部署在云环境，即云计算服务器，或者网络侧设备也可以部署在边缘环境中，即边缘计算服务 器。网络侧设备可以是集成的一个设备，也可以是分布式的多个设备，本申请实施例不做具体限定。
路侧设备包括路侧单元(Road Side Unit，RSU)、多接入边缘计算(Multi-Access Edge Computing，MEC)或者传感器等装置，例如，可以是RSU、MEC或者传感器，也可以是由RSU和MEC组成的系统，或者是由RSU和传感器组成的系统，还可以是由RSU、MEC和传感器组成的系统。
终端例如可以是车辆、智能穿戴设备(例如，运动手环、手表等)、便携移动设备(例如，手机、平板等)等任一种设备、或可以是上述任一种设备内的装置，部件或芯片，例如车载单元(On Board Unit，OBU)，本申请实施例不做具体限定。
在一种实施方式中，在电子地图由网络侧设备生成时，网络侧设备可以基于基础地图(包括静态图层和动态图层)和人工智能模型获得风险事件信息。其中，人工智能模型是根据历史事故数据进行训练获得，历史事故数据包括历史事故的发生时段、事故区域、风险等级、事故区域在发生时段的环境信息和事故区域的道路状态信息等。在一些可能的实施例中，网络侧设备基于数据源设备(例如，路面监测装置、路侧设备、终端等中的至少一种)检测到影响风险事件的要素发生变化时，网络侧设备还可以根据变化后的要素更新风险事件信息。基础地图可以是高精地图、标精地图或者其他类型的地图，本申请实施例在此不作具体限定。在一些可能的实施例中，数据源设备例如可以是交通管理部门提供交通路况数据的设备等。在此情况下，网络侧设备可以作为电子地图的发布者，终端或路侧设备可以作为电子地图的接收者和使用者。
由于终端和路侧设备也具备信息获取能力和计算能力，因此，终端或路侧设备除了可以作为电子地图的接收者和使用者，也可以作为电子地图的生产者或更新者在本地生成该风险事件信息，供自身使用或者发送给其他的设备。
以终端作为电子地图的生产者为例，终端可以是车辆、车载单元(On Board Unit，OBU)等设备或装置。在此情况下，终端中存储有上述基础地图。终端可以基于自身当前的位置信息和/或自身的规划轨迹信息，从上述基础地图获取所在位置处或者一定地理区域范围内与风险事件相关的地图数据，例如环境预测信息或道路状态信息，结合人工智能模型生成包含一定地理区域范围内的风险事件信息的电子地图，以供自身使用，或者供其他设备使用。
发布电子地图时，可以由网络侧设备通过无线网络，例如蜂窝通信网络，将电子地图发布到终端；或者，可以由网络侧设备将电子地图发布到其它设备，由其它设备转发给终端，转发可以通过V2X(Vehicle to Everything，车联网)进行。例如，云端的地图服务器向行人手持的便携终端或车辆发布电子地图，既可以通过包括基站在内的蜂窝通信网络进行发布，又可以通过V2X通信由路侧设备向便携终端或车辆转发。或者，电子地图的生产者为路侧设备或终端，可以由路侧设备或终端通过V2X进行发布。
由于风险事件与周围环境中动态要素相关，在关联的动态要素的状态发生变化时，可能会触发相应地风险事件信息的更新。
上述各系统中，网络侧设备与终端之间，终端与路侧设备之间，网络侧设备与路侧设备之间的通信可使用蜂窝通信技术，例如2G蜂窝通信，例如全球移动通信系统(global system for mobile communication，GSM)、通用分组无线业务(general packet radio service，GPRS)；或者3G蜂窝通信，例如宽带码分多址(wideband code division multiple access，WCDMA)、 时分同步码分多址接入(time division-synchronous code division multiple access，TS-SCDMA)、码分多址接入(code division multiple access，CDMA)，或者4G蜂窝通信，例如长期演进(long term evolution，LTE)。或者5G蜂窝通信，或者其他演进的蜂窝通信技术。无线通信系统也可利用非蜂窝通信技术，如Wi-Fi与无线局域网(wireless local area network，WLAN)通信。在一些实施例中，上述设备之间通信还可利用红外链路、蓝牙或ZigBee进行直接通信。在一些实施例中，上述设备之间通信还可以采用其他无线协议，例如各种车辆通信系统，例如，系统中可包括一个或多个专用短程通信(dedicated short range communications，DSRC)设备，这些设备可包括车辆和/或路边台站之间的公共和/或私有数据通信，本申请不做具体限定。
需要说明的是，图2仅为示例性架构图，但不限定图2所示系统包括的网元的数量。虽然图2未示出，但除图2所示的功能实体外，图2还可以包括其他功能实体。另外，本申请实施例提供的方法可以应用于图2所示的通信系统，当然本申请实施例提供的方法也可以适用其他通信系统，本申请实施例对此不予限制。
参见图3，图3是本申请实施例提供的一种地图数据处理方法的流程图，可以应用于上述描述的系统架构。该方法包括但不限于以下步骤：
S101：获取风险事件信息，风险事件信息包括时间信息和位置信息，其中，时间信息用于指示风险事件的预测时间范围，位置信息用于指示风险事件的预测区域范围。
在本申请实施例中，获取风险事件信息，具体为：生成风险事件信息。在此情况下，图3实施例所描述的方法可以用于包括风险事件信息的地图的生成，该方法包括但不限于在网络侧设备(例如，服务器侧)、路侧设备或者终端侧的设备、部件、芯片、软件模块或者硬件模块处执行，所述终端侧的设备包括但不限于车辆或者便携终端。
或者，在本申请实施例中，获取风险事件信息，具体为：接收风险事件信息。在此情况下，图3实施例所描述的方法可以用于包括风险事件信息的地图的使用或存储，该方法包括但不限于在服务器侧、路侧设备或者终端侧的设备、部件、芯片、软件模块或者硬件模块处执行，所述终端侧的设备包括但不限于车辆或者便携终端。
在本申请实施例中，风险事件的预测时间范围是指预测到的风险事件可能发生的时间范围，并不一定是风险事件实际发生的时间范围。风险事件的预测时间范围可以是一个时间区段，该时间区段可以基于该风险事件的起始时刻和该风险事件的结束时刻表示，也可以基于该风险事件的起始时刻和持续时长表示，在此不作具体限定。
例如，风险事件的预测时间范围可以是自当前时刻起1至2小时内，也可以是的某一具体时间段，例如9:00-10:00AM，关于预测时间范围的时长，本申请实施例不作具体限定。
在本申请实施例中，风险事件的预测区域范围是指预测到的风险事件可能发生的区域范围，并不一定是风险事件实际发生的区域范围。预测区域范围可以是基于任意坐标系获得的坐标值，例如，世界大地坐标系(Word Geodetic System 1984，WGS84)中对应的由经度、纬度和海拔组成的三维坐标，也可以是自然坐标系下的X坐标、Y坐标和Z坐标组成的三维坐标，还可以是道路坐标系下的S坐标、D坐标和H坐标组成的三维坐标，或者其他坐标系下的坐标。
风险事件的预测区域范围具体有以下多种表示方式：一具体实施中，该预测区域范围为一个规则形状时，可以通过一个或多个相对于基准点(例如，车道或道路的起点)的参数， 如距离，坐标等表示。例如，可以对预测区域范围进行道路级表示，即该预测区域范围位于地图中的道路，以基准点为道路的起点，(a，b)是对该预测区域范围所在的一段道路的区间表达，或者，通过这段道路两个端点的地理坐标表示。在一些可能的实施例中，也可以对预测区域范围进行车道级表示，即该预测区域范围位于地图中的车道。另一具体实施中，该预测区域范围为一个不规则形状时，可以通过该不规则形状的多个角点的地理坐标来表示，或者，以该预测区域范围的最小外接矩形或最小外接圆形进行几何位置表达。
参见图4的(1)，提供了一种风险事件的预测区域范围的道路级几何表达示意图。在图4的(1)中，该预测区域范围位于地图中的道路时，可以以该条道路的起点作为基准点，(20m，50m)表示从距离基准点20米处开始至距离基准点50米处截止。
参见图4的(2)，提供了一种风险事件的预测区域范围的车道级几何表达示意图。在图4的(2)中，该预测区域范围位于地图中的车道时，可以以该条车道的起点作为基准点，(20m，60m)表示从距离基准点20米处开始至距离基准点60米处截止。
在一些可能的实施例中，风险事件信息还包括下述内容中的至少一项：风险事件的标识信息、风险事件所属的瓦片的标识信息、风险事件所在的道路的标识信息、风险等级信息、风险类型信息、预警信息、影响风险事件的动态要素的标识信息和受风险事件影响的地图要素的信息。
一具体实施中，风险事件信息还包括风险等级信息，风险等级信息用于指示风险事件的危险程度。需要说明的是，风险等级信息是风险事件信息的一种可选信息。基于风险等级信息对风险事件的风险程度进行阶梯量化，可以有效区分不同危险程度的风险事件，有利于分清轻重缓急，提高风险事件的应对效率。
示例性地，危险程度的划分可以是三级划分，即高风险、中风险和低风险。在一些可能的实施例中，危险程度的划分也可以是四级划分，即高风险、中风险、低风险和无风险，还可以是其他划分方式，本申请实施例不作具体限定。
风险等级信息可以采用比特映射、二进制取值或其他方式来指示风险事件的危险程度。例如，风险等级信息取第一风险值时，指示风险事件的危险程度为高风险；风险等级信息取第二风险值时，指示风险事件的危险程度为中风险；风险等级信息取第三风险值时，指示风险事件的危险程度为低风险。
参见表1，表1示例性地提供了一种风险值与风险事件的危险程度之间的映射表。由表1可知，当风险值为1时，表示风险事件的危险程度为低风险；当风险值为2时，表示风险事件的危险程度为中风险；当风险值为3时，表示风险事件的危险程度为高风险。基于表1可知，风险值越高，则风险事件就越危险。
表1
风险值 风险事件的危险程度
1 低风险
2 中风险
3 高风险
可以理解，上述表1仅作为一个示例，以体现风险值与风险事件的危险程度之间的对应关系，在实际应用中，该对应关系的文字内容和存储方式还可以是其他形式，在此不作具体限定。
另一具体实施中，风险事件信息还包括风险类型信息，风险类型信息用于指示风险事件的类型。需要说明的是，风险类型信息是风险事件信息的一种可选信息。基于风险类型信息可以快速明确可能面临的是何种类型的风险，从而可以更好地应对风险事件。
示例性地，风险事件的类型包括但不限于变道碰撞、转弯碰撞、打滑碰撞、可见度降低、刹车距离减小等。
例如，风险类型信息可以从风险事件产生的原因对风险事件进行分类描述，例如，能见度低的风险事件、路面打滑风险事件或道路变窄风险事件；也可以从风险事件导致的结果对风险事件进行分类描述，例如拥堵风险事件、剐蹭风险事件、追尾风险事件、坠崖风险事件或爬坡困难风险事件。具体分类的方式还有多种可能，本申请实施例在此不做限定。
另一具体实施中，风险事件信息还包括影响风险事件的动态要素的标识信息。需要说明的是，影响风险事件的动态要素的标识信息是风险事件信息的一种可选信息。基于影响风险事件的动态要素的标识信息可以快速索引风险事件关联的动态要素，在检测到关联的动态要素发生变化时，基于变化后的动态要素可以及时更新风险事件信息，有利于提高风险事件信息的准确性。
其中，风险事件受动态要素的影响包括但不限于：动态要素导致该风险事件的生成，或者，动态要素会加重或者减轻风险事件的危险程度。需要说明的是，由于风险事件有对应的预测时间范围，即说明在预测时间范围内风险事件受该动态要素的影响，而对于该预测时间范围之外，风险事件是否与该动态元素关联，本申请实施例不作具体限定。
示例性地，影响风险事件的动态要素可以是路面结冰、道路施工、大雾天气、暴雪天气、暴雨天气、道路拥堵、某道路禁止通行、路面坍塌、山体滑坡、路面检修等动态要素中的至少一个，在此不作具体限定。
另外，动态要素的标识信息用于在地图中唯一标识一个动态要素。动态要素的标识信息可以是一个或多个字符的组合，其中，字符可以是数字，字母以及其他符号中的一种或多种，例如一个或多个数字的组合，或者一个或多个数据和字母的组合。
另一具体实施中，风险事件信息还包括受风险事件影响的地图要素的信息。需要说明的是，受风险事件影响的地图要素的信息是风险事件信息的一种可选信息。基于受风险事件影响的地图要素的信息可以实现该地图要素的快速索引，且当风险事件变化时，可以实现受风险事件影响的地图要素的信息的联动更新，不仅提高了地图数据的准确率，还提高了地图数据的更新效率。
例如，地图要素可以是道路、车道、路口等静态图层中的元素，也可以是道路的限速值、道路限速的时段、时段的置信度、道路的通行时段、道路施工的时段、车道线的可跨越性等动态图层中的事件。地图要素受风险事件影响是指风险事件会使得某些地图要素产生临时、动态的改变。例如，风险事件1使得道路1的限速值改变，风险事件1使得道路2的限速时段的时长延长，或者，风险事件1使得某车道新增了限速信息。
另一具体实施中，风险事件信息还包括预警信息。预警信息用于指示基于该风险事件向驾驶员或驾驶系统提醒的内容。需要说明的是，预警信息是风险事件信息的一种可选信息。基于预警信息，可以提醒驾驶员或驾驶系统注意以及尽可能避开前方风险事件对应的预测区域范围，有利于提高车辆行驶的安全性。
例如，预警信息可以是类似“区域a在预测时段1内存在风险，请小心驾驶”的风险提示 信息，也可以是类似“区域a在预测时段1内危险，请尽量避开该区域”绕路建议信息，还可以是其他可以起到警示风险事件作用的信息，本申请实施例在此不作具体限定。
在一些可能的实施例中，风险事件信息还包括：风险事件的标识信息、风险事件所属的瓦片的标识信息和风险事件所在的道路的标识信息中的至少一项。需要说明的是，风险事件的标识信息、风险事件所属的瓦片的标识信息以及风险事件所在的道路的标识信息均是风险事件信息的可选信息。
其中，风险事件的标识信息用于标识地图中的风险事件；瓦片的标识信息用于标识地图中的瓦片，道路的标识信息用于标识地图中的道路。
另外，将风险事件与地图瓦片的标识关联，可以基于地图瓦片的标识实现风险事件的快速索引。将风险事件与道路的标识关联，可以基于道路的标识实现风险事件的快速索引。由此，有效提高了风险事件的搜索效率以及风险事件信息的发布效率。
需要说明的是，风险事件的标识信息、瓦片的标识信息或道路的标识信息可以是一个或多个字符的组合，其中，字符可以是数字，字母以及其他符号中的一种或多种，例如一个或多个数字的组合，或者一个或多个数据和字母的组合。
在本申请实施例中，示例性地提供了风险事件信息的两种表达方式。第一种以事件为单位，将风险事件信息存储至风险事件的标识对应的数据结构中；第二种以时段为单元，将风险事件信息存储至预测时间范围对应的数据结构中。需要说明的是，下述图5A、图5B和图6所示的表达方式仅供参考，并不限定风险事件信息仅能存储至图示的数据结构中。下面具体介绍这两种表达方式。
第一种：以事件为单位
参见图5A，图5A是本申请实施例提供的风险事件信息的一种表达方式的示意图。在图5A中，以风险事件A为例说明风险事件A的风险事件信息的表示，可以看出，风险事件信息包括风险事件A的时间信息和位置信息。在一些可能的实施例中，风险事件信息还包括风风险事件A的风险等级信息、风险类型信息、影响风险事件A的动态要素、受风险事件A影响的地图要素的信息、预警信息、风险事件A所属的瓦片的标识信息、风险事件A所在的道路的标识信息和风险事件A的标识信息中的至少一项。图5A所示的各个信息具体可参考上述实施例中的相关内容的叙述，为了说明书的简洁，在此不再赘述。
需要说明的是，上述图5A所示风险事件信息的表达方式仅仅作为一种示例，本申请实施例对风险事件信息的组成内容和数据结构不做限定。以上举例的风险事件信息中除了时间信息和位置信息以外的八种内容中的每一种都不是必然包括于风险事件信息中，即可以根据实际应用需求可选择性的包括于风险事件信息中。
在本申请实施例中，风险事件也可以随着时间动态变化，在此情况下，风险事件信息也可以包括风险事件在不同时段的具体信息。参见图5B，提供了风险事件信息的又一种表达方式的示意图，也是风险事件信息的具体内容的又一种示例。在图5B中，仍以风险事件A为例说明风险事件A的风险事件信息，可以看出，风险事件信息包括风险事件A在至少一个时段(例如，预测时段1、预测时段2等)的具体信息，以预测时段1为例，风险事件A在预测时段1的具体信息包括风险事件A在该预测时段1时的位置信息、风险等级信息、风险类型信息、影响风险事件A的动态要素、受风险事件A影响的地图要素的信息以及预警信息等。除此之外，风险事件信息还包括风险事件A的标识信息、风险事件A所属的瓦片的标识信息 和风险事件A所在的道路的标识信息。有关图5B所示的各个信息具体可参考上述实施例中的相关叙述，在此不再赘述。
可以理解，同一风险事件在不同时段下的ID不变，因此，风险事件的ID是保持不变的。但由于风险事件的状态可以随时间变化，因此，风险事件在不同时段对应的位置信息、风险等级信息、风险类型信息、影响风险事件的动态要素的标识信息、受风险事件A影响的地图要素的信息和预警信息中的至少一项可能会不同。
以一个例子具体说明风险事件在不同时段的预测状态：假设风险事件A在预测时段1(即未来1小时内)内：位置信息为预测区域范围1，风险等级信息指示高风险以及影响风险事件A的动态要素包括路面结冰要素1和道路施工要素2，其中，路面结冰要素1在预测时段1内的结冰厚度为5cm；假设风险事件A在预测时段2(即未来1小时至2小时内)内：位置信息仍为预测区域范围1，但风险等级信息指示低风险以及影响风险事件A的动态要素为路面结冰要素1，且路面结冰要素1在预测时段2内的结冰厚度为1cm。由此可以看出，预测时段2时关联的动态要素相较于预测时段1时关联的动态要素有变化，具体地，路面结冰要素1的属性状态有变化且预测时段1结束后道路施工要素2消失，另外，预测时段2时的风险等级信息与预测时段1时的风险等级信息也不同。
第二种：以时段为单位
在本申请实施例中，风险事件信息还可以预测时段范围为单位进行表示。
参见图6，图6是本申请实施例提供的风险事件信息的又一种表达示意图。在图6中，以预测时段为单位存储风险事件信息，例如，风险事件A的风险事件信息存储至预测时段1对应的数据结构中，其中，风险事件A的风险事件信息包括风险事件A的标识信息、风险事件A在预测时段1对应的位置信息、风险等级信息、风险类型信息、影响风险事件A的动态要素、受风险事件A影响的地图要素的信息、预警信息以及风险事件A所属的瓦片的标识信息等。在一些可能的实施例中，若风险事件A的状态随时间变化，风险事件A在不同时段的事件相关信息可以存储至对应时段的数据结构中。例如，在图6中，风险事件A在预测时段2的事件相关信息可以存储至预测时段2对应的存储结构中。
在一些可能的实施例中，每个预测时段对应的数据结构中可以存储至少一个风险事件的风险事件信息。例如，在图6中预测时段1对应的数据结构中，除了可以存储风险事件A的风险事件信息外，还可以存储其他风险事件的风险事件信息，例如，风险事件B的风险事件信息，其中，风险事件B的风险事件信息的详细内容具体可参考风险事件A的风险事件信息，为了说明书的简洁，在此不再赘述。
在一些可能的实施例中，为了更直观、清楚地显示不同时段内的风险事件信息，不同时段内的风险事件信息可以以地图图层的形式进行表示，例如，不同时段的风险事件信息可以以地图的不同动态图层的形式进行表示。
参见图7，图7是本申请实施例提供的一种承载了不同时段的风险事件信息的动态图层的示意图。可以看出，图7所示的地图数据包括静态图层和各个预测时段对应的动态图层，其中，静态图层包括道路拓扑、建筑物位置、车道线、车道方向或交通基础设施布局等信息；各个时段对应的动态图层包括预测时段1对应的动态图1层、预测时段2对应的动态图层2、…、预测时段n对应的动态图层n。以预测时段1对应的动态图层1为例具体说明动态图层1中的具体内容，动态图层1包括风险事件1和风险事件2，其中，风险事件1的预测区域范围 为风险区域1指示的区域范围，风险事件2的预测区域范围为风险区域2指示的区域范围。基于图7，还可以看出动态图层1还可以包括风险事件1关联的动态要素，例如暴雪天气要素，以及风险事件2关联的动态要素，例如道路施工要素。
需要说明的是，图7所示的预测时段对应的动态图层既承载了风险事件的位置信息又承载了影响风险事件的动态要素，在一些可能的实施例中，风险事件的部分信息(包括位置信息、风险等级信息和风险类型信息等)与风险事件关联的动态要素也可以位于不同的动态图层，本申请实施例不作具体限定。
另外，图7所示的各个预测时段对应的动态图层只是一种示例。在实际应用时，每个预测时段对应的动态图层既可以单独分开显示，也可以是多个预测时段对应的动态图层如图7所示同时显示，还可以是承载了风险事件信息的动态图层与地图的其他图层进行叠加显示，例如，图7中的动态图层1与静态图层叠加显示，或者，承载了风险事件信息的动态图层与仅承载天气信息的动态图层叠加显示，或者，承载了风险事件信息的动态图层与承载了路面环境信息的动态图层、仅承载天气信息的动态图层、静态图层四者一起叠加显示，本申请实施例并不限定可叠加的地图图层的数量，也不限定可叠加的图层的类型。
在申请实施例中，风险事件信息为根据地图数据中的静态图层数据和地图数据中的动态图层数据获得的。一具体实施中，可以从静态图层数据和动态图层数据中提取与风险事件相关的地图数据，例如，某时段的环境信息和/或道路状态信息，其中，环境信息包括但不限于降水量、降雪量、能见度、光照强度、风向、风力、雷电指数等天气参数，道路状态信息包括但不限于道路类型、车道数量、路面施工信息(例如，是否有施工、施工位置、施工时长等)、路面坑洼信息(例如，深度、位置信息、面积等)、路面覆盖物信息(例如，结冰厚度、积水深度、落叶量)等指示道路状态的信息。根据风险事件相关的地图数据进行预测获得风险事件信息。需要说明的是，道路类型的分类有多种，例如，基于道路行政等级可分为国道、省道、县道、乡道等，基于道路使用任务、功能、和交通量可分为高速公路、一级公路、二级公路、三级公路等，还可以是其他划分方式，在此不作具体限定。
在本申请实施例中，基于风险事件的类型的不同，用于获取风险事件信息的静态图层数据和动态图层数据也不同。其中，风险事件的类型包括但不限于变道碰撞、转弯碰撞、打滑碰撞、可见度降低、刹车距离减小等。
例如，风险事件的类型为变道碰撞时，静态图层数据具体包括车道线的实虚信息、虚线类型的车道线的位置信息、十字路口的位置信息、路面障碍物的位置信息等，动态图层数据包括道路施工信息、交通事故信息、恶劣天气信息(例如暴雨、暴雪)等。
又例如，风险事件的类型为刹车距离减小时，用于获取风险事件信息的地图数据主要为动态图层数据，其中，该动态图层数据具体包括路面结冰信息、路面积水信息、路面落叶厚度等描述路面覆盖情况的信息以及暴雨、暴雪天气等恶劣天气信息中的至少一项。
示例性地，风险事件信息的获取过程，具体可以是：从地图的静态图层数据和/或动态图层数据中获取目标地理区域在预测时段的环境信息和/或道路状态信息，其中，目标地理区域包括上述预测区域范围，预测时段包括上述预测时间范围。将目标地理区域在预测时段的环境信息和道路状态信息输入人工智能模型进行预测，输出风险事件的时间信息和位置信息，需要说明的是，风险事件的时间信息是根据入参(例如，环境信息和/或道路状态信息)预测到的可能发生的时间范围，风险事件的位置信息是根据入参预测到的可能发生的区域范围。 在一些可能的实施例中，人工智能模型还可以输出风险事件的风险等级信息和风险类型信息中的至少一项。进一步地，结合风险事件的时间信息、位置信息、风险等级信息、风险类型信息以及地图中地图要素的描述信息，可以确定影响风险事件的动态要素以及获得受风险事件影响的地图要素的信息。用于生成风险事件信息的源数据也不限于地图中的数据，还可以为从路侧设备接收到的信息，或者车辆自身感知到的信息(包括环境信息和/或车辆状态信息)。本申请实施例对生成风险事件信息的数据类型和数据来源不做限定。
上述人工智能模型是预先训练好的，在后续使用过程中可以不断进行优化。例如，人工智能模型可以是随机森林(Random Forest，RF)、支持向量机(Support Vector Machine，SVM)模型、神经网络模型或者其他预测算法，本申请不做具体限定。
示例性地，上述人工智能模型是基于历史事故数据训练获得，其中，历史事故数据包括历史事故的发生时段、历史事故的发生区域、风险等级、风险类型、历史事故在发生时段的历史环境信息和历史道路状态信息。历史事故数据可以是交通管理部门提供的。例如，风险等级可以根据历史事故的碰撞致程度、人员伤亡情况等进行评定。
示例性地，人工智能模型的训练过程具体可以是：以事故1为例，假设事故1的发生时段为时段1以及发生区域为区域1，将事故1的风险等级作为事故1的风险等级真值，将事故1的风险类型作为事故1的风险类型真值，将事故1对应的历史环境信息和历史道路状态信息输入至人工智能模型进行预测，人工智能模型输出预测时间范围为时段2、预测区域范围为区域2、预测风险等级和预测风险类型，根据区域1的位置信息与区域2的位置信息获得事故1的位置预测误差，根据时段1与时段2获得事故1的时段预测误差，根据预测风险等级和事故1对应的风险等级真值获得事故1的风险等级预测误差，根据预测风险类型和事故1的风险类型真值获得事故1的风险类型预测误差，基于位置预测误差、时段预测误差、风险等级预测误差和风险类型预测误差中的至少一项调整人工智能模型的参数，直至人工智能模型的预测误差小于等于预设误差阈值，使得训练好的人工智能模型能够基于目标地理区域在预测时段的环境信息和道路状态信息准确地进行风险事件的预测。
可以看到，基于人工智能模型实现对一定地理区域范围内可能发生的风险事件的预测，且在预测过程中结合了周围环境信息以及道路状态信息等因素，有效提高了预测的风险事件的精准性。
在本申请实施例中，当获取风险事件信息为生成风险事件信息，且风险事件信息的生成端为车辆时，在此情况下，车辆需满足下述条件中的至少一项：
车辆在预测区域范围内；
车辆距离预测区域范围的最小距离小于第一阈值；
预测区域范围与车辆的规划路径有交集；
预测区域范围距离车辆的规划路径的最小距离小于第二阈值；
预测区域范围所属的瓦片为车辆所在的瓦片；和
预测区域范围所属的瓦片为车辆的规划路径所经过的瓦片。
也就是说，车辆可以基于当前所在位置，生成自身位置附近的风险事件信息，或者基于规划的路径，生成行驶路径附近的风险事件信息。需要说明的是，上述只是对风险事件信息的生成端为车辆时的限制条件的示例，上述限制条件同样适用于风险事件信息的生成端为终端侧的其他设备。
S102：存储风险事件信息。
在本申请实施例中，将风险事件信息作为地图数据进行存储。例如，将风险事件信息作为地图的动态图层数据进行存储。
在本申请实施例中，存储风险事件信息，具体为：将风险事件信息存储至风险事件的标识对应的数据结构中。此实施例具体可参考上述图5A和图5B的相关叙述，在此不再赘述。
在本申请实施例中，存储风险事件信息，具体为：将风险事件信息存储至预测时间范围对应的数据结构中。此实施例具体可参考上述图6的相关叙述，在此不再赘述。
可选地，在一些可能的实施例中，还可以执行：
S103：确定影响风险事件的要素发生变化，根据变化后的要素更新风险事件信息或者消除风险事件信息。
其中，要素位于地图中的静态图层或者动态图层，由于风险事件信息中包括影响风险事件A的动态要素的标识信息，基于风险事件信息中动态要素的标识，可以在地图中快速索引影响风险事件A的动态要素，当检测到影响风险事件的动态要素发生变化时，相应地，也会导致风险事件信息发生变化。
需要说明的是，可以基于路侧设备或者终端发送的检测信息确定影响风险事件的动态要素发生变化，也可以基于实时动态更新的地图数据确定影响风险事件的动态要素发生变化。
一具体实施中，根据变化后的要素更新风险事件信息是指：根据变化后的要素，修改风险事件信息中的时间信息、位置信息、风险等级信息、风险类型信息、预警信息、影响风险事件的动态要素的标识信息、受风险事件影响的地图要素的信息中的至少一项。
另一具体实施中，根据变化后的要素消除风险事件信息，可以是：根据变化后的要素确定更新后的风险事件的风险等级信息满足第一风险条件，则删除风险事件信息，其中，第一风险条件可以是风险事件的风险等级小于第一风险阈值，或者，风险事件无风险。需要说明的是，风险事件信息的消除，意味着预测到对应的风险事件将不发生，地图中的该风险事件也被删除。
例如，假设风险事件1在预测时段1的风险等级为中风险，且影响风险事件1的要素为路面结冰且预测时段1内结冰厚度为3mm，由于天气由阴转为大太阳易加速冰的融化，从地图中获取到预测时段1内风险事件1关联的道路结冰要素消失，导致风险事件1的风险等级更新为无风险，则删除该风险事件信息。
在一些可能的实施例中，若从风险事件的预测时间范围的开始时刻起直到预测时间范围的结束时刻为止，一直未检测到影响风险事件的动态要素发生变化，则当到达预测时间范围的结束时刻时，删除风险事件信息。
在一些可能的实施例中，影响风险事件的动态要素也属于获取风险事件信息的地图的动态图层数据，当检测到影响风险事件的动态要素发生变化时，也可以基于上述人工智能模型更新风险事件信息，在此不再赘述。
在一些可能的实施例中，该方法还包括：根据风险事件信息执行路况监控、交通调度、路径规划或对车辆的控制。在一些可能的实施例中，该方法还包括发送风险事件信息，以使风险事件信息的接收端根据风险事件信息，执行路况监控、交通调度、路径规划或者对车辆的控制。具体过程可参考下述图8和图9实施例的相关叙述。另外，在对风险事件信息更新后，还可以发送更新后的风险事件信息。
在一些可能的实施例中，还可以根据风险事件信息生成地图显示界面。具体过程可参考下述图9实施例中S304的相关叙述，在此不再赘述。
可以看到，实施本申请实施例，能够提供一定地理区域范围内可能发生的风险事件的风险事件信息，该风险事件信息是具有参考意义的，有利于增加地图的丰富程度。另外，还提供了用于存储风险事件信息的数据结构，能够实现对风险事件信息清晰、直观地表达。风险事件信息的获取综合考虑了周围环境信息和道路状态信息等因素，能有效提高风险事件信息的精准性。
参见图8，图8是本申请实施例提供的又一种地图数据处理方法的流程图。图8所示方法的执行主体可以是网络侧设备、路侧设备或终端。该方法包括但不限于以下步骤：
S201：生成风险事件信息，风险事件信息包括时间信息和位置信息，时间信息用于指示风险事件的预测时间范围，位置信息用于指示风险事件的预测区域范围。本步骤具体可参考图3实施例中S101的叙述，在此不再赘述。
S202：根据风险事件信息，执行路况监控、交通调度、路径规划操作或对车辆的控制。
一具体实施中，根据风险事件信息，执行路况监控可以是：根据风险事件信息对地图中的预测区域范围进行路况监控。在一些可能的实施例中，还可以根据风险事件信息对地图中超过风险阈值的预测区域范围进行路况监控。
一具体实施中，根据风险事件信息，执行交通调度可以是：根据风险事件信息和地图中的道路拓扑信息确定与预测区域范围相关联的道路区域，向该道路区域内的车辆提示相应地风险事件，或者管控该道路区域的车流量。可以看出，基于风险事件信息执行交通调度，能够有效提升车辆出行的安全性。
其中，上述道路区域包括至少一段道路或者包括至少一段车道，道路区域中的多段道路可以对应同一个道路标识(即属于同一条道路)，也可以对应多个道路标识(即属于不同的道路)。同理，道路区域中的多段车道可以对应同一个车道标识也可以对应多个车道标识。
其中，道路区域满足下述至少一个条件时，道路区域与预测区域范围相关联：
(1)道路区域与预测区域范围之间的距离小于等于预设距离阈值；
(2)道路区域与预测区域范围之间交通可达；或者
(3)道路区域内的车辆预计到达风险事件所在的预测区域范围的时刻属于风险事件对应的预测时间范围。
例如，风险事件信息指示“区域1，时段1，高风险”，网络侧设备，例如云端设备，基于风险事件信息确定风险事件1所在的区域1关联的道路区域包括区域2和区域3，则网络侧设备在时段1之前向区域2和区域3内的车辆提示该风险事件，例如，发送“区域1在时段1为高风险区域，请绕行”。或者，网络侧设备还可以管控区域2和区域3内的车流量，例如，选择性地设置区域2或区域3内的某些路段在预设时段内禁止进入，或者，对区域2和区域3内的道路限速，或者，引导区域2或区域3内的车辆更换导路线以避开区域1，从而使得时段1内区域1的车流量减少，实现了交通调度，大大降低了车辆的出行风险。
一具体实施中，根据风险事件信息，执行对车辆的控制可以是：在确定车辆即将经过风险事件所在的预测区域范围时，控制车辆执行以下操作中的至少一项：变换车道；调整行驶速度；更新导航路线；开启警示灯；和向驾驶员提示所述风险事件。车辆可以预先向网络侧 设备，例如云端，订阅风险事件服务，使得车辆可以提前针对性的采取措施应对风险事件，提高了车辆运行的安全性。
一具体实施中，根据风险事件信息，执行路径规划可以是：响应于来自终端(例如、车辆、手机等)的路径规划请求，根据风险事件信息和路径规划请求携带的终端的目的地等信息执行路径规划，获得最低风险路径。另一具体实施中，也可以根据风险事件信息，结合车辆的感知能力、车辆类型、自动驾驶级别中的至少一项执行路径规划，获得最低风险路径。在一些可能的实施例中，还可以向终端推荐最低风险路径，或者，向终端推荐包括最低风险路径的多条路径以供终端选择。有关路径规划操作具体可参考图9实施例中S303的相关叙述，为了说明书的简洁，在此不再赘述。
在一些可能的实施例中，也可以基于风险事件信息执行路况监控、交通调度、路径规划和对车辆的控制中的多项操作，具体过程可参考上述相关叙述，在此不再赘述。
可以看到，实施本申请实施例，网络侧设备或路侧设备等生成具有参考意义的、一定地理区域范围内可能发生的风险事件的风险事件信息。网络侧设备或路侧设备还可以使用该风险事件信息，实现交通调度、路况监控、路径规划以及车辆控制等功能，从宏观上有效应对了地图中的风险事件，有利于减少交通事故的发生。
参见图9，图9是本申请实施例提供的又一种地图数据处理方法的流程图，应用于网络侧设备和终端之间，在此以云端设备和车端设备为例，即应用于云端设备和车端设备之间，但本申请并不限定图9所描述的方法仅应用于云端设备和车端设备之间，例如，还可以应用于路侧设备和终端之间，或者终端与终端之间。该方法包括但不限于以下步骤：
S301：云端设备生成风险事件信息，风险事件信息包括时间信息和位置信息，时间信息用于指示风险事件的预测时间范围，位置信息用于指示风险事件的预测区域范围。本步骤具体可参考图3实施例中S101的叙述，在此不再赘述。
S302：云端设备向车端设备发送风险事件信息。
在本申请实施例中，云端设备向车端设备发送风险事件信息，可以是：云端设备以广播的方式发送风险事件信息。也就是说，风险事件信息可以承载于广播信息中。
进一步地，云端设备广播风险事件信息可以是：云端设备基于预测区域范围所属的瓦片的标识广播风险事件信息，或者，云端设备基于预测区域范围所属的道路的标识广播风险事件信息。
在一些可能的实施例中，云端设备也可以结合时段与瓦片的标识，或者，结合时段与道路的标识下发风险事件信息，在此不作具体限定。
在本申请实施例中，云端设备也可以直接向车端设备发送风险事件信息，其中，车端设备满足下述条件中的至少一项：
(1)车端设备在预测区域范围内；
(2)车端设备距离预测区域范围的最小距离小于第一阈值；
(3)预测区域范围与车端设备的规划路径有交集；
(4)预测区域范围距离车端设备的规划路径的最小距离小于第二阈值；
(5)预测区域范围所属的瓦片为车端设备所在的瓦片；
(6)预测区域范围所属的瓦片为车端设备的规划路径所经过的瓦片；和
(7)预测区域范围所属的道路为车端设备所在的道路。
可以看出，若云端设备直接向车端设备发送风险事件信息，则车端设备预先在云端设备定制了风险提示服务，使得车端设备可以及时从云端获取风险事件信息，从而能提早应对风险事件，提高了自身车辆行驶的安全性。
在一些可能的实施例中，云端设备也可以基于车端设备的实际需求下发风险事件信息。例如，下发车端设备的规划路径所在区域的风险事件信息；下发车端设备所在位置处至少一个瓦片内的风险事件信息；下发某一具体区域在某具体时段内的风险事件信息等，在此不做具体限定。
在一些可能的实施例中，云端设备是提前发送风险事件信息的，也就是说，云端设备发送风险事件信息的时刻早于风险事件信息中预测时间范围的起始时刻。在一些可能的实施例中，在忽略云端设备发送风险事件信息的发送时刻与车端设备接收到风险事件信息的接收时刻之间的时延的情况下，云端设备发送风险事件信息的时刻最晚也可以等于风险事件信息中最早的一个预测时间范围的起始时刻。
S303：车端设备根据风险事件信息，执行路径规划和/或对自身车辆的控制。
在本申请实施例中，车端设备根据风险事件信息，执行路径规划，具体为：车端设备根据风险事件信息规划最低风险路径。
其中，最低风险路径为：无风险事件的路径；没有风险等级超过风险阈值的风险事件的路径；或者，没有特定风险类型的风险事件的路径。
最低风险路径为无风险事件的路径可以理解为在一定时间范围内最低风险路径经过的区域都是无风险事件的区域。最低风险路径为没有风险等级超过风险阈值的风险事件的路径可以理解为在一定时间范围内最低风险路径经过的区域存在预测的风险事件，但风险事件的风险等级不超过风险阈值(即认为风险等级的取值越大，风险事件越危险)。最低风险路径为没有特定风险类型的风险事件的路径可以理解为在一定时间范围内最低风险路径经过的区域有存在预测的风险事件，但风险事件的风险类型并不是特定风险类型。
一具体实施中，根据风险事件信息规划最低风险路径，包括：根据风险事件信息和车端设备的感知能力，规划车端设备的最低风险路径。
车辆通过自身的感知元件(或称为车载传感器)可以有效感知周围环境。车辆的感知能力包括对车辆的感知和对环境的感知。其中，用于感知车辆的器件包括动力、底盘、车身及电子电气系统中的传感器等，用于感知环境的器件包括车载摄像头、毫米波雷达、激光雷达等。
示例性地，在规划车端设备的最低风险路径时，结合车端设备的感知能力，不仅可以佐证风险事件信息，还可以基于车辆在当前位置感知到的与风险事件相关的信息重新计算，获得更为精准的风险事件的时间信息和位置信息，使得规划的最低风险路径的精准性更高。
可以理解，车端设备的感知能力越好，应对风险事件的能力就越强，车端设备的最低风险路径的包容性越好，换句话说，最低风险路径对车端设备的抗风险能力的要求就越高。例如，若车端设备的感知能力较好，则车端设备应对风险事件的能力较强，则最低风险路径可以是无风险事件的路径、没有风险等级超过风险阈值的风险事件的路径和没有特定风险类型的风险事件的路径中的任意一种；若车端设备的感知能力较差，则车端设备应对风险事件的能力较差，则最低风险路径应尽可能为无风险事件的路径。
一具体实施中，根据风险事件信息规划最低风险路径，包括：根据风险事件信息和车端设备的车辆类型，规划车端设备的最低风险路径。
车辆类型有多种分类方式，例如，基于用途可分为运输车辆、专用车辆以及特殊用途车辆，其中，运输车辆又可分为轿车、客车和货车，专用车辆又可分为运输型(包括冷藏车、沙土自卸车等)和作业型(包括消防车、救护车等)，特殊用途车辆又可分为娱乐车辆、竞赛车辆等。又例如，基于动力装置类型可分为内燃机车辆、电动车辆、喷气车辆等。基于空间大小可分为微型车、小型车、紧凑型车、中型车、中大型车等，本申请实施例对车辆类型的划分不作具体限定。
示例性地，不同类型的车辆的驾驶操控难易度不同，擅长的行驶环境也不同。例如，小型车辆的调控相较于中大型车辆的调控更灵活，故小型车辆对某些种类风险事件的应对能力强于中大型车辆对风险事件的应对能力。因此，小型车辆的最低风险路径的包容性可能强于中大型车辆的最低风险路径的包容性，换句话说，最低风险路径对小型车辆的抗风险能力的要求大于最低风险路径对中大型车辆的抗风险能力的要求。在其他种类的风险事件的应对方面，小型车辆的应对能力可能不如大型车辆。
结合车辆类型规划车端设备的最低风险路径，使得最低风险路径与车辆的风险应对能力匹配，从而不同类型的车辆均有适合的最低风险路径。
示例性地，假设车辆类型为电动车时，在规划最低风险路径时，结合车辆类型需考虑到最低风险路径应包含至少一个充电桩站点能及时为车辆补充电力，以保证车辆能沿着最低风险路径顺利行驶至目的地。
一具体实施中，根据风险事件信息规划最低风险路径，包括：根据风险事件信息和车端设备所处的自动驾驶级别，规划车端设备的最低风险路径。
自动驾驶又可以称为智能驾驶或辅助驾驶，是车辆智能化发展的重要方向，随着感知技术的发展以及芯片能力的提升，智能驾驶为人们提供了越来越多的丰富的驾驶功能，逐渐实现不同级别的驾驶体验。自动机工程师学会(society of automotive engineers，SAE)提供了一种驾驶自动化分级标准，包括驾驶等级L0至L5，其中L0级为无自动化，由人类驾驶者全权操作车辆，在行驶过程中可以得到驾驶系统的警告或辅助，例如自动紧急制动(autonomous emergency braking，AEB)，盲点检测(blind spot monitoring，BSM)或车道偏离报警(lane departure warning，LDW)等。L1级为驾驶支援，驾驶操作由人类驾驶者和驾驶系统共同完成，驾驶系统可以通过驾驶环境对方向盘或加减速操作提供驾驶支援，其他的驾驶操作由人类驾驶员进行，例如自适应巡航控制(adaptive cruise control，ACC)或车道保持辅助/支持(lane keep assistance/support，LKA/LKS)等；L2级为部分自动化，通过驾驶环境对方向盘和加减速中的多项提供驾驶支援，其他的驾驶动作由人类驾驶员进行，例如结合了自适应巡航控制(adaptive cruise control，ACC)和车道保持辅助(lane keep assistance，LKA)的跟车功能；L3级为有条件自动化，可以由驾驶系统完成所有的驾驶操作，但人类驾驶员需要在适当的时候应答驾驶系统的请求，即人类驾驶员需要做好接管驾驶系统的准备；L4级为高度自动化，可以由驾驶系统完成所有的驾驶操作，人类驾驶员不一定需要对驾驶系统的请求作出应答，例如在道路和环境条件允许的情况下(比如封闭的园区、高速公路、城市道路或固定的行车线路等)人类驾驶员可以不接管驾驶；L5级为完全自动化，在各种人类驾驶员可以应对的道路和环境条件下的驾驶操作均可以由驾驶系统自主完成。可见，L0至L2的级别，驾驶系统 主要为驾驶员提供支持，驾驶员仍然需要做好驾驶监督，根据需要进行转向、制动或加速以保证安全。L3至L5级别，驾驶系统可以代替驾驶员完成所有的驾驶操作，L3级别下，驾驶员要做好接管驾驶的准备，L4和L5级别驾驶系统可以实现部分条件和所有条件下的完全驾驶，驾驶员可以选择是否接管。
以上分级是一种示例，随着技术的演进或者在不同国家或地区的规定不同，以上分级可以变化，例如，中国工业和信息化部提出的车辆自动化分级包括在车辆驾驶自动化的6个等级，其中0-2级为驾驶辅助，系统辅助人类执行动态驾驶任务，驾驶主体仍为驾驶员；3-5级为自动驾驶，系统在设计运行条件下代替人类执行动态驾驶任务，当功能激活时，驾驶主体是系统。各级名称及定义如下：0级驾驶自动化(应急辅助，emergency assistance)系统不能持续执行动态驾驶任务中的车辆横向或纵向运动控制，但具备持续执行动态驾驶任务中的部分目标和事件探测与响应的能力。1级驾驶自动化(部分驾驶辅助，partial driver assistance)系统在其设计运行条件(或称为设计运行范围ODD)下持续地执行动态驾驶任务中的车辆横向或纵向运动控制，且具备与所执行的车辆横向或纵向运动控制相适应的部分目标和事件探测与响应的能力。2级驾驶自动化(组合驾驶辅助，combined driver assistance)系统在其设计运行条件下持续地执行动态驾驶任务中的车辆横向和纵向运动控制，且具备与所执行的车辆横向和纵向运动控制相适应的部分目标和事件探测与响应的能力。3级驾驶自动化(有条件自动驾驶，conditionally automated driving)系统在其设计运行条件下持续地执行全部动态驾驶任务。4级驾驶自动化(高度自动驾驶，highly automated driving)系统在其设计运行条件下持续地执行全部动态驾驶任务并自动执行最小风险策略。5级驾驶自动化(完全自动驾驶，fully automated driving)系统在任何可行驶条件下持续地执行全部动态驾驶任务并自动执行最小风险策略。其中，横向控制主要用于车辆转向的控制，例如，控制方向盘扭矩或角度以控制车辆的方向；纵向控制主要用于车辆的速度控制，例如控制制动踏板、加速踏板、或档位等以控制车辆的加/减速、刹车等。
无论采用何种分级方式，本申请实施例的描述可以适用于不同的分级情况。
设计运行范围(Operational Domain Design，ODD)是指自动驾驶系统可以安全运行的条件，其设置的条件可以包括地理位置、道路类型、速度范围、天气、时间、国家和地方性交通法律法规等。以高速公路巡航控制系统(Highway Pilot，HWP)为例，系统在识别车辆已处于ODD范围内(比如车辆当前行驶在高速公路上，天气晴朗，车速合适，光照条件良好，全球定位导航系统(Global Positioning System，GPS)信号稳定等)，待驾驶员确认激活系统后，HWP系统将持续执行全部的动态驾驶任务。
示例性地，对于L3和L4级别，主要由自动驾驶系统控制车辆行驶，在此情况下，结合车辆所处的自动驾驶级别，尽可能使得规划的最低风险路径所经过的区域均满足ODD的要求，也就是说，最低风险路径需尽可能避开道路状态差、天气条件恶劣的区域。在一些可能的实施例中，对于处于L3或L4级别的车辆，当最低风险路径经过的区域不满足ODD的要求时，也可以请求驾驶员接管，在此不作具体限定。
而对于处于L5级别的车辆，车辆本身具有较好的风险应对能力，在此情况下，规划的最低风险路径的包容性也较强。另外，可以看出，处于L0、L1或L2级别的车辆主要依赖于驾驶员执行驾驶操作，车辆的风险应对能力主要依赖于驾驶员的驾驶经验，在此情况下，除了结合车辆所处的自动驾驶级别，还可以结合驾驶员的驾驶习惯和/或驾驶能力规划最低风险路 径。
在一些可能的实施例中，根据风险事件信息规划最低风险路径时，还可以结合车端设备的感知能力、车端设备的车辆类型和车端设备所处的自动驾驶级别中的多项进行最低风险路径的规划，具体可参考上述实施例中的相关叙述，在此不再赘述。
示例性地，最低风险路径的获取过程具体可以是：获得多条路径，确定预测区域范围包括这多条路径中的位置点；预测车端设备行驶至位置点处的预计时间；根据风险事件信息中的时间信息和位置信息确定与预测区域范围相对应的预测时间范围；确定预测时间范围包括上述预计时间；根据风险事件确定多条路径中至少一条路径的行驶风险；根据路径的行驶风险确定多条路径中的最低风险路径。
也就是说，在规划最低风险路径时，车端设备需结合自身的行驶速度估算自身实际到达路径的各个位置点的预计时间，尽可能使得车端设备在各个预计时间所在的位置不属于风险事件所在的预测区域范围，或者尽可能使得各个预计时间不属于风险事件的预测时间范围。
具体来说，假设车端设备从A点经过B点到达C点，且A、B、C三点附近当前时刻均存在风险事件，但规划车端设备的最低风险路径时，不仅根据当前时刻这三个位置点的风险事件为该车辆规划路径，还综合考虑车端设备分别到达A、B、C三点的预计时间，例如，预计t1时刻到达A点、t2时刻到达B点以及t3时刻到达C点，则分别使用t1时刻A点附近的风险事件信息、t2时刻B点附近的风险事件信息以及t3时刻C点附近的风险事件信息进行车端设备的路径规划。
例如，在图1中，假设当前时刻为9:00，车辆欲从位置A到达位置D，可行驶路径有两条，分别为路径1和路径2，其中，路径1为A→B→C→D，路径2为A→B→E→F→C→D，路径1的长度小于路径2的长度。假设车辆在位置A接收到广播的风险事件信息，根据风险事件信息获得指示路段BC中的区域1在预测时间范围9:30-11:00内为高风险区域的信息，下面以两个例子说明车辆基于风险事件信息执行路径规划操作的过程：
例1：车辆先选定路径1：A→B→C→D，车辆基于自身当前的行驶速度估算自身到达位置B、位置C和位置D的时刻分别为9:05、9:20和9:25，由此可以看出，车辆在9:20之前成功穿越路段BC中的区域1，早于风险事件的预测时间范围9:30-11:00的起始时刻9:30，故确定路径1：A→B→C→D为无风险路径，且路径1的长度小于路径2的长度，故路径1为最优导航路径。
例2：车辆先选定路径1：A→B→C→D，假设车辆基于自身当前的行驶速度估算自身到达位置B、位置C和位置D的时刻分别为9:30、10:30和11:00，由此可以看出，车辆到达路段BC中区域1的时刻属于风险事件的预测事件范围9:30-11:00，也就是说，若车辆按照路径A→B→C→D行驶，则车辆在预测时间范围9:30-11:00内会经过高风险的区域1，即路径1经过的区域存在风险事件，路径1的安全性低。因此，车辆分析路径2，经分析确定路径2：A→B→E→F→C→D为无风险路径，从而将路径2作为最低风险路径，从而实现在预测时段9:30-11:00避开区域1，提高了驾驶的安全性和驾驶决策的准确率。
在本申请实施例中，在获得最低风险路径后，车端设备还可以向用户推荐最低风险路径，或者，车端设备控制自身车辆沿着最低风险路径行驶。
在本申请实施例中，在获得最低风险路径后，车端设备还可以向用户推荐多条路径，其中，所述多条路径包括最低风险路径，从用户接收反馈信息，反馈信息用于指示用户从多条 路径中选择的路径，并控制自身车辆沿着用户选择的路径行驶。
例如，在用户输入起点和终点后，导航应用程序可以根据出行策略，例如路程最短、用时最短、红灯最少、最省油或最省过路费等，向用户推荐出行路线。车辆、便携终端、安装于道路侧或者云端的设备均可以使用风险事件信息，通过安装的导航应用程序，结合用户的出行需求(包括但不限于起点、终点、出行时间或出行方式)，为用户制定最低风险路径或者为用户推荐包括最低风险路径的多条路径。多条路径除了包括最低风险路径外，还可以包括用时最少路径、距离最短路径等。在一些可能的实施例中，最低风险路径也可能同时用时最少或者距离最短，在此不作具体限定。
参见图10，图10是本申请实施例提供的一种路径推荐的界面示意图。图10的导航显示界面中向用户推荐多条不同策略下的出行路线，右下角的矩形框以文字形式对各条路线进行了说明：经由A-B-F-D-E的路线1是行程距离最短的路线，总路程为3.6公里；经由A-B-C-D-E的路线2是风险最低的路线，风险事件数量为0；经由A-G-H-E的路线3是用时最少的路线，总时长为15分钟。图10中各路线上的圆形区域表示风险事件所在的预测范围区域，可以看出，路线1有两个圆形区域，故路线1的风险事件数量为2；路线3有1个圆形区域，故路线3的风险事件数量为1；而路线2的风险事件数量为0，因此，路线2为最低风险路径。可选地，用户可以从图10所示的三条路线中选择一条路线，响应于用户的选择操作，控制车辆沿着用户选择的路线行驶。
在本申请实施例中，根据风险事件信息，执行对自身车辆的控制包括：在确定车辆即将经过风险事件所在的预测区域范围，控制车辆执行下述操作中的至少一项：变换车道；调整行驶速度；更新导航路线；开启警示灯；和向驾驶员或驾驶系统提示风险事件。由此，在驾驶过程中，车端设备基于风险事件信息方便实时决策，以提高自身的安全性。
可选地，在一些可能的实施例中，还可以执行：
S304：车端设备根据风险事件信息生成地图显示界面。
在本申请实施例中，车端设备的显示装置可以呈现地图显示界面。例如，显示装置可以是车端设备的车机平板、车载显示器或抬头显示(head up display，HUD)系统等，在此不作具体限定。
在本申请实施例中，可以通过下述方式中的至少一种在地图显示界面上呈现风险事件信息：
(1)根据时间信息和位置信息在地图显示界面上动态播放风险事件的变化；
(2)标记风险事件中在当前时间的风险等级超过阈值的至少一个风险事件的预测区域范围和上述至少一个风险事件的描述信息；
(3)标记风险事件中与导航路径相关的至少一个风险事件的预测区域范围和上述至少一个风险事件的描述信息；
(4)标记用户选择的时间下的至少一个风险事件的预测区域范围和上述至少一个风险事件的描述信息；
(5)标记符合用户选择的风险类型的至少一个风险事件的预测区域范围和上述至少一个风险事件的描述信息；
(6)用不同颜色标记不同风险等级的风险事件所对应的预测区域范围；和
(7)用不同颜色标记不同风险类型的风险事件所对应的预测区域范围。
对于方式(1)，可以在地图显示界面上动态播放风险事件的变化，使得驾驶员可以预先知晓前方区域内的风险事件的变化趋势，能及时调整自身的驾驶策略，提高自身的安全性。
对于方式(2)，基于上述表1，假设阈值设置为“2”，则风险等级超过阈值的风险事件即为风险等级超过阈值“2”的风险事件，也就是说，标记显示危险程度为“高风险”的风险事件的相关信息。如此，可提示用户重点关注风险等级超过阈值的风险事件。
对于方式(3)，标记的与导航路径相关的风险事件满足时间和空间两个维度。例如，导航路径指示从A点经过B点到达C点，且A、B、C三点附近当前时刻均存在风险事件，但预计车辆到达A、B、C三点的时间分别为t1、t2、t3，则仅需标记t1时刻A点附近的风险事件的相关信息、t2时刻B点附近的风险事件的相关信息和t3时刻C点附近的风险事件的相关信息。在标记与导航路径相关的风险事件时，考虑到显示的清晰度，也可以先标记距离当前位置一定范围内与导航路径相关的至少一个风险事件。
对于方式(4)，用户还可以在地图显示界面上选择感兴趣的时段，并查看该时段下的至少一个风险事件的相关信息，时段信息有利于提高驾驶决策的准确率。
上述在地图显示界面上呈现风险事件信息时，用户的选择操作可以是基于点触、拖动、滑动、语音等任意一种形式生成的。
参见图11，图11是本申请实施例提供的一种显示装置的界面。在图11中，显示装置的界面主要包括两部分，其中一部分为风险事件选择界面，另一部分为地图显示界面。在风险事件选择界面中，设置有“局部”选项键和“全局”选项键，其中，“局部”选项键下罗列有至少一条道路的选项键，例如，道路A、道路B、道路C等。进一步地，当指向“道路B”选项键时，右侧罗列有道路B上的风险事件列表，可以看出，道路B上的风险事件列表包括事件1、事件2、事件3和事件4。在地图显示界面中，当用户在左侧的风险事件选择界面选中某选项键后，响应于该操作，在右侧的地图显示界面的显示区域呈现该风险事件的相关信息。
在图11中，当希望在地图显示界面的显示区域显示“事件3”时，用户可以点击“事件3”选项键，也可以将“事件3”拖动至右侧显示区域，也可以从“事件3”选项键的位置滑动至右侧显示区域，还可以是发出“显示事件3”的语音指令等。
在一些可能的实施例中，还可以在图11所示的风险事件选择界面设置“时段”选项键，以供用户可以查看感兴趣的时段下的至少一个风险事件的相关信息。可选的，该时段可以是按照默认设置生成的，以直接供用户选择。该时段也可以是用户自己在线设置的，在此不作具体限定。
在一些可能的实施例中，还可以在图11所示的风险事件选择界面设置“风险等级”、“风险类型”、“地理区域范围”等选项键中的至少一个，可作为在有地图显示界面显示风险事件的过滤条件。例如，仅标记风险等级为中风险以上的风险事件的相关信息、标记风险类型为碰撞风险的风险事件的相关信息、标记距离当前位置100m以内的地理区域范围的风险事件的相关信息等等。
需要说明的是，图11只是一种显示装置的界面的示例，本申请实施例并不限定显示装置的界面仅为图11所示形式。在一些可能的实施例中，还可以在图11所示的风险事件选择界面的“局部”选项键下增加地图瓦片的标识，以实现对各道路的分类。又例如，还可以在图11所示的“道路”选项键下添加车道的标识，以实现对道路的进一步细化，本省申请实施例不作具体限定。
在本申请实施例中，在地图显示界面上呈现风险事件信息时，可以仅标记风险事件所在的预测区域范围，也可以在标记预测区域范围的同时，通过弹框或界面文字等方式显示风险事件的详细信息。
参见图12，图12是本申请实施例提供的一种地图显示界面的示意图。在图12中，对一个风险事件进行了显示，具体地，前方道路中的四边形区域表示该风险事件所在的预测区域范围，且旁边有一个弹框显示了风险事件的详细信息，包括“预测时段为9:20-10:30、位置信息为区域1的坐标、风险等级为高风险”，基于该弹框可知晓区域1在9:20-10:30存在高风险。在一些可能的实施例中，当用户在图10所示的风险事件选择界面点击了“道路B”的“事件3”时，则图11中地图显示界面的显示区域可以显示图12所示的画面。
在本申请实施例中，在地图显示界面，还可以风险事件信息中影响风险事件的动态要素。例如，在图12中，还标记了影响该风险事件的路面结冰要素，其中，四边形区域内的椭圆形区域即为路面结冰要素所在的区域范围。如此，可以提示用户关注风险区域周围的道路环境，小心驾驶。
在一些可能的实施例中，在地图显示界面呈现多个风险事件时，可以用不同的颜色标记不同风险等级的风险事件所在的预测区域范围，或者，用于不同的颜色标记不同风险类型的风险事件所在的预测区域范围。如此，直观、清晰地向用户展示了地图中不同风险等级或者不同风险类型的风险事件的分布。
在一些可能的实施例中，车端设备在检测到自身于预测时间范围接近预测区域范围时，还可以语音提示或警示用户即将进入存在风险事件的区域；在车端设备已进入预测区域范围内时，还可以持续播报“小心驾驶”、“请求接管驾驶”等语音信息提示用户。
可以看到，实施本申请实施例，云端设备可向车端设备提供具有参考意义的一定地理区域范围内可能发生的风险事件的风险事件信息，车端设备基于该风险事件信息可以执行路径规划以及对自身车辆的控制，能更好地应对地图中的风险事件，提高了车端设备的行驶安全性。另外，基于风险事件信息获得地图显示界面，直观、清晰地向用户展示了地图中风险事件的分布。
本申请实施例还提供了一种电子地图或电子地图数据结构，该电子地图或电子地图数据结构包括风险事件信息，风险事件信息包括时间信息和位置信息，时间信息用于指示风险事件的预测时间范围，位置信息用于指示风险事件的预测区域范围。该电子地图或电子地图数据被用于第一设备中，该第一设备将该包括风险事件信息的电子地图数据发送给第二设备，第二设备基于该电子地图执行路况监控、交通调度、路径规划或对车辆的控制等操作。第一设备例如为网络侧设备，例如云端设备；路侧设备，或终端；第二设备例如为终端。
一具体实施中，该风险事件信息在电子地图中以事件的数据结构进行存储。
一具体实施中，该风险事件信息在电子地图中作为动态图层数据存储。
一具体实施中，风险事件信息还包括以下内容中的至少一项：风险事件的标识信息、风险事件所属的瓦片的标识信息、风险事件所在的道路的标识信息、风险等级信息、风险类型信息、预警信息、影响风险事件的动态要素的标识信息和受风险事件影响的地图要素的信息；其中，风险等级信息用于指示风险事件的危险程度，风险类型信息用于指示风险事件的类型，预警信息用于指示基于该风险事件向驾驶员或驾驶系统提醒的内容。
参见图13，图13是本申请实施例提供的一种地图数据处理装置的功能结构示意图，地图数据处理装置30包括获取单元310和存储单元312。该地图数据处理装置30可以通过硬件、软件或者软硬件结合的方式来实现。
其中，获取单元310，用于生成风险事件信息，风险事件信息包括时间信息和位置信息，时间信息用于指示风险事件的预测时间范围，位置信息用于指示风险事件的预测区域范围；存储单元312，用于将风险事件信息作为地图数据存储。
在一些可能的实施例中，该地图数据处理装置30还包括发送单元314，发送单元314用于向车辆发送风险事件信息。在一些可能的实施例中，该地图数据处理装置30还包括处理单元316，处理单元316用于确定影响风险事件的要素发生变化，并根据变化后的要素更新风险事件信息或者消除风险事件信息，处理单元316还用于根据风险事件信息，执行路况监控、交通调度、路径规划或者对车辆的控制。
该地图数据处理装置30可用于实现图3实施例所描述的方法。在图3实施例中，获取单元310可用于执行S101，存储单元312可用于执行S102，处理单元316可用于执行S103。发送单元314可用于执行图9实施例中的S302。该地图数据处理装置30还可用于实现图8实施例所描述的方法和图9实施例所描述的云端设备侧的方法，为了说明书的简洁，在此不再赘述。
以上图13所示实施例中的各个单元的一个或多个可以软件、硬件、固件或其结合实现。所述软件或固件包括但不限于计算机程序指令或代码，并可以被硬件处理器所执行。所述硬件包括但不限于各类集成电路，如中央处理单元(central processing unit，CPU)、数字信号处理器(digital signal processor，DSP)、现场可编程门阵列(field-programmable gate array，FPGA)或专用集成电路(application-specific integrated circuit，ASIC)。
参见图14，图14是本申请实施例提供的一种地图数据处理装置的功能结构示意图，地图数据处理装置40包括接收单元410和处理单元412。该地图数据处理装置40可以通过硬件、软件或者软硬件结合的方式来实现。
其中，接收单元410，用于接收风险事件信息，风险事件信息包括时间信息和位置信息，时间信息用于指示风险事件的预测时间范围，位置信息用于指示风险事件的预测区域范围；处理单元412，用于根据风险事件信息，执行路径规划或者对自身车辆的控制。
在一些可能的实施例中，地图数据处理装置40还包括显示单元414，显示单元414用于根据风险事件信息生成地图显示界面。
该地图数据处理装置40可用于实现图9实施例所描述的车端设备侧的方法。在图9实施例中，接收单元410和处理单元412可用于执行S303，显示单元414可用于执行S304。
以上图14所示实施例中的各个单元的一个或多个可以软件、硬件、固件或其结合实现。所述软件或固件包括但不限于计算机程序指令或代码，并可以被硬件处理器所执行。所述硬件包括但不限于各类集成电路，如中央处理单元(central processing unit，CPU)、数字信号处理器(digital signal processor，DSP)、现场可编程门阵列(field-programmable gate array，FPGA)或专用集成电路(application-specific integrated circuit，ASIC)。
本申请还提供一种地图数据处理装置。如图15所示，地图数据处理装置50包括：处理器501、通信接口502、存储器503和总线504。处理器501、存储器503和通信接口502之间通过总线504通信。应理解，本申请不限定地图数据处理装置50中的处理器、存储器的个数。
一具体实施中，地图数据处理装置50可以是上述包括风险事件信息的电子地图或电子地图数据的生成端。地图数据处理装置50可以是网络侧设备、路侧设备或终端。其中，网络侧设备例如可以是部署在网络侧的服务器(例如应用服务器或地图服务器)，或者为该服务器中的组件或者芯片。网络侧设备可以部署在云环境或者边缘环境中，本申请实施例不做具体限定。路侧设备例如可以是路侧单元(Road Side Unit，RSU)、多接入边缘计算(Multi-Access Edge Computing，MEC)或者传感器等装置，或者是这些装置内部的组件或者芯片，也可以是由RSU和MEC组成的系统级设备，或者是由RSU和传感器组成的系统级设备，还可以是由RSU、MEC和传感器组成的系统级设备。终端可以是车辆、便携移动设备(例如，手机、平板等)等任一种设备、或可以是上述任一种设备内的装置、部件或芯片，例如车载单元OBU，本申请实施例不做具体限定。
另一具体实施中，地图数据处理装置50可以是上述包括风险事件信息的电子地图的使用端。地图数据处理装置50可以是网络侧设备、路侧设备或终端。其中，网络侧设备例如可以是部署在网络侧的服务器(例如应用服务器或地图服务器)，或者为该服务器中的组件或者芯片。网络侧设备可以部署在云环境或者边缘环境中，本申请实施例不做具体限定。路侧设备例如可以是路侧单元(Road Side Unit，RSU)、多接入边缘计算(Multi-Access Edge Computing，MEC)或者传感器等装置，或者是这些装置内部的组件或者芯片，也可以是由RSU和MEC组成的系统级设备，或者是由RSU和传感器组成的系统级设备，还可以是由RSU、MEC和传感器组成的系统级设备。终端可以是车辆、智能穿戴设备(例如，运动手环、手表等)、便携移动设备(例如，手机、平板等)等任一种设备、或可以是上述任一种设备内的装置、部件或芯片，例如OBU，本申请实施例不做具体限定。
总线504可以是外设部件互连标准(peripheral component interconnect，PCI)总线或扩展工业标准结构(extended industry standard architecture，EISA)总线等。总线可以分为地址总线、数据总线、控制总线等。为便于表示，图15中仅用一条线表示，但并不表示仅有一根总线或一种类型的总线。总线504可包括在地图数据处理装置50各个部件(例如，存储器503、处理器501、通信接口502)之间传送信息的通路。
处理器501可以包括中央处理器(central processing unit，CPU)、微处理器(micro processor，MP)或者数字信号处理器(digital signal processor，DSP)等处理器中的任意一种或多种。
存储器503用于提供存储空间，存储空间中可以存储操作系统和计算机程序等数据。存储器503可以是随机存取存储器(random access memory，RAM)、可擦除可编程只读存储器(erasable programmable read only memory，EPROM)、只读存储器(read-only memory，ROM)，或便携式只读存储器(compact disc read memory，CD-ROM)等中的一种或者多种的组合。存储器503可以单独存在，也可以集成于处理器501内部。
通信接口502可用于为处理器501提供信息输入或输出。或者可替换的，该通信接口502可用于接收外部发送的数据和/或向外部发送数据，可以为包括诸如以太网电缆等的有线链路 接口，也可以是无线链路(如Wi-Fi、蓝牙、通用无线传输等)接口。或者可替换的，通信接口502还可以包括与接口耦合的发射器(如射频发射器、天线等)，或者接收器等。
在一些可能的实施例中，当地图数据处理装置50可以是上述包括风险事件信息的地图的使用端时，地图数据处理装置50还包括显示器505，显示器505与处理器501通过总线504连接或耦合。显示器505用于根据风险事件信息生成地图显示界面。显示器505可以是显示屏，显示屏可以是液晶显示器(Liquid Crystal Display，LCD)、有机或无机发光二极管(Organic Light-Emitting Diode，OLED)、有源矩阵有机发光二极体面板(Active Matrix/Organic Light Emitting Diode，AMOLED)等。显示器505也可以是车机平板、车载显示器或者抬头显示(head up display，HUD)系统等。
该地图数据处理装置50中的处理器501用于读取存储器503中存储的计算机程序，用于执行前述的方法，例如图3、图8或图9所描述的方法。
在一种可能的设计方式中，地图数据处理装置50可为执行图3、图8或图9(云端设备侧)所示方法的执行主体中的一个或多个模块，该处理器501可用于读取存储器中存储的一个或多个计算机程序，用于执行以下操作：
生成风险事件信息，风险事件信息包括时间信息和位置信息，时间信息用于指示风险事件的预测时间范围，位置信息用于指示风险事件的预测区域范围；
通过存储单元312将风险事件信息作为地图数据存储；
通过发送单元314发送风险事件信息；
以及确定影响风险事件的要素发生变化，根据变化后的要素更新风险事件信息或者消除风险事件信息。
在另一种可能的设计方式中，地图数据处理装置50可为执行图8或图9所示方法的执行主体中的一个或多个模块，该处理器501可用于读取存储器中存储的一个或多个计算机程序，用于执行以下操作：
通过接收单元410接收风险事件信息，风险事件信息包括时间信息和位置信息，时间信息用于指示风险事件的预测时间范围，位置信息用于指示风险事件的预测区域范围；
根据风险事件信息，执行路况监控、交通调度、路径规划或对车辆的控制；
或者通过显示单元414根据风险事件信息生成地图显示界面。
本申请实施例还提供了一种通信系统，该通信系统包括第一地图数据处理装置和第二地图数据处理装置，其中，第一地图数据处理装置例如可以是图13所示的地图数据处理装置30，也可以是图15所述的作为地图生成端的地图数据处理装置50；第二地图数据处理装置例如可以是图14所示的地图数据处理装置40，也可以是图15所述的作为地图使用端的地图数据处理装置50。第一地图数据处理装置可用于执行上述图3、图8实施例所描述的方法以及图9实施例所描述的云端设备侧的方法，第二地图数据处理装置可用于执行上述图8实施例所描述的方法以及图9实施例所描述的车端设备侧的方法。
在本文上述的实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详细描述 的部分，可以参见其他实施例的相关描述。
需要说明的是，本领域普通技术人员可以看到上述实施例的各种方法中的全部或部分步骤是可以通过程序来指令相关的硬件来完成，该程序可以存储于一计算机可读存储介质中，存储介质包括只读存储器(Read-Only Memory，ROM)、随机存储器(Random Access Memory，RAM)、可编程只读存储器(Programmable Read-only Memory，PROM)、可擦除可编程只读存储器(Erasable Programmable Read Only Memory，EPROM)、一次可编程只读存储器(One-time Programmable Read-Only Memory，OTPROM)、电子抹除式可复写只读存储(Electrically-Erasable Programmable Read-Only Memory，EEPROM)、只读光盘(Compact Disc Read-Only Memory，CD-ROM)或其他光盘存储器、磁盘存储器、磁带存储器、或者能够用于携带或存储数据的计算机可读的任何其他介质。
本申请的技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的全部或部分可以以软件产品的形式体现出来，该计算机程序产品存储在一个存储介质中，包括若干指令用以使得一个设备(可以是个人计算机，服务器，或者网络设备、机器人、单片机、芯片、机器人等)执行本申请各个实施例所述方法的全部或部分步骤。

Claims (40)

  1. 一种地图数据处理方法，其特征在于，所述方法包括：
    获取风险事件信息，所述风险事件信息包括时间信息和位置信息，所述时间信息用于指示风险事件的预测时间范围，所述位置信息用于指示所述风险事件的预测区域范围；
    将所述风险事件信息作为地图数据存储。
  2. 根据权利要求1所述的方法，其特征在于，所述方法还包括：
    根据所述风险事件信息规划最低风险路径。
  3. 根据权利要求2所述的方法，其特征在于，所述方法还包括：
    向用户推荐所述最低风险路径，或者控制车辆沿所述最低风险路径行驶。
  4. 根据权利要求2所述的方法，其特征在于，所述方法还包括：
    向用户推荐多条路径，所述多条路径包括所述最低风险路径；
    从用户接收反馈信息，所述反馈信息用于指示所述用户从所述多条路径中选择的路径；
    控制车辆沿所述选择的路径行驶。
  5. 根据权利要求2-4任一项所述的方法，其特征在于，所述根据所述风险事件信息规划最低风险路径，包括：
    根据所述风险事件信息和车辆的感知能力，规划所述车辆的最低风险路径；
    根据所述风险事件信息和所述车辆的车辆类型，规划所述车辆的最低风险路径；或者
    根据所述风险事件信息和所述车辆处于的自动驾驶级别，规划所述车辆的最低风险路径。
  6. 根据权利要求2-5任一项所述的方法，其特征在于，所述最低风险路径为：
    无风险事件的路径；
    没有风险等级超于风险阈值的风险事件的路径；或者
    没有特定风险类型的风险事件的路径。
  7. 根据权利要求2-6任一项所述的方法，其特征在于，所述根据所述风险事件信息规划最低风险路径，包括：
    规划多条路径；
    确定所述预测区域范围包括所述多条路径中的位置点；
    预估车辆在预计时间行驶至所述位置点；
    根据所述风险事件信息中的所述时间信息和所述位置信息确定与所述预测区域范围相对应的所述预测时间范围；
    确定所述预测时间范围包括所述预计时间；
    根据所述风险事件确定所述多条路径中至少一条路径的行驶风险；
    根据所述行驶风险确定所述多条路径中的所述最低风险路径。
  8. 根据权利要求1-7任一项所述的方法，其特征在于，所述获取风险事件信息，包括：
    生成所述风险事件信息，或者，接收所述风险事件信息。
  9. 根据权利要求1-8任一项所述的方法，其特征在于，所述方法用于车辆，或者，所述方法还包括向车辆发送所述风险事件信息，所述车辆满足以下条件中的至少一项：
    所述车辆在所述预测区域范围内；
    所述车辆距离所述预测区域范围的最小距离小于第一阈值；
    所述预测区域范围与所述车辆的规划路径有交集；
    所述预测区域范围距离所述车辆的规划路径的最小距离小于第二阈值；
    所述预测区域范围所属的瓦片为所述车辆所在的瓦片；和
    所述预测区域范围所属的瓦片为所述车辆的规划路径所经过的瓦片。
  10. 根据权利要求1-9任一项所述的方法，其特征在于，所述预测区域范围位于所述地图中的道路或者车道。
  11. 根据权利要求1-10任一项所述的方法，其特征在于，所述风险事件信息还包括以下内容中的至少一项：
    所述风险事件的标识信息、所述风险事件所属的瓦片的标识信息、所述风险事件所在的道路的标识信息、风险等级信息、风险类型信息、预警信息、影响所述风险事件的动态要素的标识信息和受所述风险事件影响的地图要素的信息；
    其中，所述风险等级信息用于指示所述风险事件的危险程度，所述风险类型信息用于指示所述风险事件的类型，所述预警信息用于指示基于该风险事件向驾驶员或驾驶系统提醒的内容。
  12. 根据权利要求1-11任一项所述的方法，其特征在于，所述方法还包括：
    确定影响所述风险事件的要素发生变化，所述要素位于所述地图的静态图层或者动态图层；
    根据所述变化后的所述要素更新所述风险事件信息或者消除所述风险事件信息。
  13. 根据权利要求1-12任一项所述的方法，其特征在于，所述方法还包括：
    根据所述风险事件信息，执行路况监控、交通调度、路径规划或对车辆的控制。
  14. 根据权利要求1-13任一项所述的方法，其特征在于，所述方法还包括：
    确定车辆即将经过所述风险事件所在的所述预测区域范围；
    控制所述车辆执行以下操作中的至少一项：
    变换车道；
    调整行驶速度；
    更新导航路线；
    开启警示灯；和
    向驾驶员提示所述风险事件。
  15. 根据权利要求1-14任一项所述的方法，其特征在于，所述方法还包括：
    根据所述地图中的道路拓扑信息确定与所述预测区域范围相关联的道路区域；
    向所述道路区域内的车辆提示所述风险事件，或者管控所述道路区域的车流量。
  16. 根据权利要求1-15任一项所述的方法，其特征在于，所述方法还包括，通过以下方式中的至少一种在地图显示界面上呈现所述风险事件信息：
    根据所述时间信息和所述位置信息在所述界面上动态播放所述风险事件的变化；
    标记所述风险事件中在当前时间的风险等级超过阈值的至少一个风险事件的预测区域范围和所述至少一个风险事件的描述信息；
    标记所述风险事件中与导航路径相关的至少一个风险事件的预测区域范围和所述至少一个风险事件的描述信息；
    标记用户选择的时间下的至少一个风险事件的预测区域范围和所述至少一个风险事件的描述信息；
    标记符合用户选择的风险类型的至少一个风险事件的预测区域范围和所述至少一个风险事件的描述信息；
    用不同颜色标记不同风险等级的风险事件所对应的预测区域范围；和
    用不同颜色标记不同风险类型的风险事件所对应的预测区域范围。
  17. 根据权利要求1-16任一项所述的方法，其特征在于，所述地图数据包括第一静态图层数据和第一动态图层数据，所述风险事件信息为根据所述第一静态图层数据和所述第一动态图层数据得到。
  18. 根据权利要求17所述的方法，其特征在于，所述将所述风险事件信息作为地图数据存储，包括：
    将所述风险事件信息作为所述地图数据的第二动态图层数据存储。
  19. 一种地图数据处理装置，其特征在于，所述装置包括：
    获取单元，用于获取风险事件信息，所述风险事件信息包括时间信息和位置信息，所述时间信息用于指示风险事件的预测时间范围，所述位置信息用于指示所述风险事件的预测区域范围；
    存储单元，用于将所述风险事件信息作为地图数据存储。
  20. 根据权利要求19所述的装置，其特征在于，所述装置还包括：
    规划单元，用于根据所述风险事件信息规划最低风险路径。
  21. 根据权利要求20所述的装置，其特征在于，所述装置还包括：
    第一处理单元，用于向用户推荐所述最低风险路径，或者控制车辆沿所述最低风险路径行驶。
  22. 根据权利要求20所述的装置，其特征在于，所述装置还包括：
    第二处理单元，用于向用户推荐多条路径，所述多条路径包括所述最低风险路径；
    接收单元，用于从用户接收反馈信息，所述反馈信息用于指示所述用户从所述多条路径中选择的路径；
    所述第二处理单元，还用于控制车辆沿所述选择的路径行驶。
  23. 根据权利要求20-22任一项所述的装置，其特征在于，所述规划单元具体用于：
    根据所述风险事件信息和车辆的感知能力，规划所述车辆的最低风险路径；
    根据所述风险事件信息和所述车辆的车辆类型，规划所述车辆的最低风险路径；或者
    根据所述风险事件信息和所述车辆处于的自动驾驶级别，规划所述车辆的最低风险路径。
  24. 根据权利要求20-23任一项所述的装置，其特征在于，所述最低风险路径为：
    无风险事件的路径；
    没有风险等级超于风险阈值的风险事件的路径；或者
    没有特定风险类型的风险事件的路径。
  25. 根据权利要求20-24任一项所述的装置，其特征在于，所述规划单元具体用于：
    规划多条路径；
    确定所述预测区域范围包括所述多条路径中的位置点；
    预估车辆在预计时间行驶至所述位置点；
    根据所述风险事件信息中的所述时间信息和所述位置信息确定与所述预测区域范围相对应的所述预测时间范围；
    确定所述预测时间范围包括所述预计时间；
    根据所述风险事件确定所述多条路径中至少一条路径的行驶风险；
    根据所述行驶风险确定所述多条路径中的所述最低风险路径。
  26. 根据权利要求19-25任一项所述的装置，其特征在于，所述获取单元具体用于：
    生成所述风险事件信息，或者，接收所述风险事件信息。
  27. 根据权利要求19-26任一项所述的装置，其特征在于，所述装置为车辆，或者，所述装置还包括向车辆发送所述风险事件信息的发送单元，所述车辆满足以下条件中的至少一项：
    所述车辆在所述预测区域范围内；
    所述车辆距离所述预测区域范围的最小距离小于第一阈值；
    所述预测区域范围与所述车辆的规划路径有交集；
    所述预测区域范围距离所述车辆的规划路径的最小距离小于第二阈值；
    所述预测区域范围所属的瓦片为所述车辆所在的瓦片；和
    所述预测区域范围所属的瓦片为所述车辆的规划路径所经过的瓦片。
  28. 根据权利要求19-27任一项所述的装置，其特征在于，所述预测区域范围位于所述地图中的道路或者车道。
  29. 根据权利要求19-28任一项所述的装置，其特征在于，所述风险事件信息还包括以下内容中的至少一项：
    所述风险事件的标识信息、所述风险事件所属的瓦片的标识信息、所述风险事件所在的道路的标识信息、风险等级信息、风险类型信息、预警信息、影响所述风险事件的动态要素的标识信息和受所述风险事件影响的地图要素的信息；
    其中，所述风险等级信息用于指示所述风险事件的危险程度，所述风险类型信息用于指示所述风险事件的类型，所述预警信息用于指示基于该风险事件向驾驶员或驾驶系统提醒的内容。
  30. 根据权利要求19-29任一项所述的装置，其特征在于，所述装置还包括第三处理单元，用于：
    确定影响所述风险事件的要素发生变化，所述要素位于所述地图的静态图层或者动态图层；
    根据所述变化后的所述要素更新所述风险事件信息或者消除所述风险事件信息。
  31. 根据权利要求19-30任一项所述的装置，其特征在于，所述装置还包括第四处理单元，用于：
    根据所述风险事件信息，执行路况监控、交通调度、路径规划或对车辆的控制。
  32. 根据权利要求19-31任一项所述的装置，其特征在于，所述装置还包括第五处理单元，用于：
    确定车辆即将经过所述风险事件所在的所述预测区域范围；
    控制所述车辆执行以下操作中的至少一项：
    变换车道；
    调整行驶速度；
    更新导航路线；
    开启警示灯；和
    向驾驶员提示所述风险事件。
  33. 根据权利要求19-32任一项所述的装置，其特征在于，所述装置还包括第六处理单元，用于：
    根据所述地图中的道路拓扑信息确定与所述预测区域范围相关联的道路区域；
    向所述道路区域内的车辆提示所述风险事件，或者管控所述道路区域的车流量。
  34. 根据权利要求19-33任一项所述的装置，其特征在于，所述装置还包括显示单元，所述显示单元用于通过以下方式中的至少一种在地图显示界面上呈现所述风险事件信息：
    根据所述时间信息和所述位置信息在所述界面上动态播放所述风险事件的变化；
    标记所述风险事件中在当前时间的风险等级超过阈值的至少一个风险事件的预测区域范围和所述至少一个风险事件的描述信息；
    标记所述风险事件中与导航路径相关的至少一个风险事件的预测区域范围和所述至少一个风险事件的描述信息；
    标记用户选择的时间下的至少一个风险事件的预测区域范围和所述至少一个风险事件的描述信息；
    标记符合用户选择的风险类型的至少一个风险事件的预测区域范围和所述至少一个风险事件的描述信息；
    用不同颜色标记不同风险等级的风险事件所对应的预测区域范围；和
    用不同颜色标记不同风险类型的风险事件所对应的预测区域范围。
  35. 根据权利要求19-34任一项所述的装置，其特征在于，所述地图数据包括第一静态图层数据和第一动态图层数据，所述风险事件信息为根据所述第一静态图层数据和所述第一动态图层数据得到。
  36. 根据权利要求35所述的装置，其特征在于，所述存储单元，具体用于：
    将所述风险事件信息作为所述地图数据的第二动态图层数据存储。
  37. 一种电子地图，其特征在于，所述电子地图包括风险事件信息，所述风险事件信息包括时间信息和位置信息，所述时间信息用于指示风险事件的预测时间范围，所述位置信息用于指示所述风险事件的预测区域范围。
  38. 一种地图数据处理装置，其特征在于，所述装置包括存储器和处理器，所述存储器存储计算机程序指令，所述处理器运行所述计算机程序指令以使所述装置执行如权利要求1-18任一项所述的方法。
  39. 一种车辆，其特征在于，所述车辆包括如权利要求19-36或38任一项所述的装置。
  40. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质存储有程序指令，所述程序指令用于实现权利要求1-18中任一项所述的方法。
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