WO2020147486A1 - 一种车辆控制方法、装置、设备和计算机存储介质 - Google Patents

一种车辆控制方法、装置、设备和计算机存储介质 Download PDF

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Publication number
WO2020147486A1
WO2020147486A1 PCT/CN2019/126015 CN2019126015W WO2020147486A1 WO 2020147486 A1 WO2020147486 A1 WO 2020147486A1 CN 2019126015 W CN2019126015 W CN 2019126015W WO 2020147486 A1 WO2020147486 A1 WO 2020147486A1
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WIPO (PCT)
Prior art keywords
unmanned vehicle
roadside
vehicle
obstacle
collision
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Ceased
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PCT/CN2019/126015
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English (en)
French (fr)
Inventor
朱晓星
刘祥
杨凡
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Beijing Baidu Netcom Science and Technology Co Ltd
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Beijing Baidu Netcom Science and Technology Co Ltd
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Application filed by Beijing Baidu Netcom Science and Technology Co Ltd filed Critical Beijing Baidu Netcom Science and Technology Co Ltd
Priority to JP2021518845A priority Critical patent/JP2021527903A/ja
Priority to US17/251,667 priority patent/US20210276589A1/en
Priority to EP19909985.4A priority patent/EP3796285A4/en
Publication of WO2020147486A1 publication Critical patent/WO2020147486A1/zh
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W60/00—Drive control systems specially adapted for autonomous road vehicles
    • B60W60/001—Planning or execution of driving tasks
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
    • G01S17/931—Lidar systems specially adapted for specific applications for anti-collision purposes of land vehicles
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W60/00—Drive control systems specially adapted for autonomous road vehicles
    • B60W60/001—Planning or execution of driving tasks
    • B60W60/0015—Planning or execution of driving tasks specially adapted for safety
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
    • B60W30/09—Taking automatic action to avoid collision, e.g. braking and steering
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
    • B60W30/095—Predicting travel path or likelihood of collision
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
    • B60W30/095—Predicting travel path or likelihood of collision
    • B60W30/0956—Predicting travel path or likelihood of collision the prediction being responsive to traffic or environmental parameters
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W60/00—Drive control systems specially adapted for autonomous road vehicles
    • B60W60/001—Planning or execution of driving tasks
    • B60W60/0015—Planning or execution of driving tasks specially adapted for safety
    • B60W60/0018—Planning or execution of driving tasks specially adapted for safety by employing degraded modes, e.g. reducing speed, in response to suboptimal conditions
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W60/00—Drive control systems specially adapted for autonomous road vehicles
    • B60W60/001—Planning or execution of driving tasks
    • B60W60/0027—Planning or execution of driving tasks using trajectory prediction for other traffic participants
    • B60W60/00272—Planning or execution of driving tasks using trajectory prediction for other traffic participants relying on extrapolation of current movement
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
    • G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
    • G06V20/588—Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G08G1/166—Anti-collision systems for active traffic, e.g. moving vehicles, pedestrians, bikes
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2420/00—Indexing codes relating to the type of sensors based on the principle of their operation
    • B60W2420/40—Photo, light or radio wave sensitive means, e.g. infrared sensors
    • B60W2420/408—Radar; Laser, e.g. lidar
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2552/00—Input parameters relating to infrastructure
    • B60W2552/05—Type of road, e.g. motorways, local streets, paved or unpaved roads
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2552/00—Input parameters relating to infrastructure
    • B60W2552/53—Road markings, e.g. lane marker or crosswalk
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/40—Dynamic objects, e.g. animals, windblown objects
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/40—Dynamic objects, e.g. animals, windblown objects
    • B60W2554/402—Type
    • B60W2554/4023—Type large-size vehicles, e.g. trucks
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2556/00—Input parameters relating to data
    • B60W2556/45—External transmission of data to or from the vehicle
    • B60W2556/50—External transmission of data to or from the vehicle of positioning data, e.g. GPS [Global Positioning System] data

Definitions

  • This application relates to the field of automatic control, and in particular to a vehicle control method, device, equipment and computer storage medium.
  • GPS-IMU Inertial Measurement Unit
  • lidar is mainly used to detect obstacles.
  • the obstacle detection of existing unmanned vehicles can only detect obstacles that appear in the field of lidar, and cannot detect obstacles due to obstacles.
  • unmanned vehicles can only react after identifying traffic participants, such as Braking, etc.; due to the limited reaction time left for unmanned vehicles by sudden traffic participants, collisions may occur even if they brake, which increases the probability of sudden danger and accidents.
  • Various aspects of the present application provide a vehicle control method and device, which are used to avoid traffic participants who suddenly appear from blind spots on the roadside and avoid collision accidents.
  • a vehicle control method including:
  • calculating the roadside blind zone of an unmanned vehicle includes:
  • the current lane of the unmanned vehicle determine the roadside blind areas on both sides of the current lane where the unmanned vehicle is located in the blind area.
  • the above aspects and any possible implementation manners further provide an implementation manner.
  • the judgment criteria include: there is a roadside blind area, the roadside blind area is caused by a large vehicle, and the large vehicle is located on the outer lane of the road alone.
  • an implementation manner is further provided, and judging the risk of collision between an unmanned vehicle and a traffic participant appearing from the roadside blind zone includes:
  • controlling the driving of the unmanned vehicle includes:
  • controlling the unmanned vehicle to decelerate includes:
  • an implementation manner is further provided, and the method further includes:
  • the risk of collision between the unmanned vehicle and the traffic participant appearing from the blind spot on the roadside is repeatedly determined at a preset time interval.
  • a vehicle control device including:
  • the acquisition module is used to acquire the information of the obstacles to be identified around the unmanned vehicle scanned by lidar, and calculate the roadside blind area of the unmanned vehicle;
  • the judgment module is used to judge the risk of collision between an unmanned vehicle and a traffic participant appearing from the roadside blind zone;
  • the control module is used to control the driving of the unmanned vehicle according to whether there is a risk of collision.
  • an implementation manner is further provided, and the acquisition module is specifically configured to:
  • the current lane of the unmanned vehicle determine the roadside blind areas on both sides of the current lane where the unmanned vehicle is located in the blind area.
  • the judging module judges the risk of collision between an unmanned vehicle and a traffic participant appearing from the roadside blind zone, it is also used for To determine whether the current road scene is a potential collision scene, the judgment criteria include: there is a roadside blind area, the roadside blind area is caused by a large vehicle, and the large vehicle is separately located in the outer lane of the road.
  • the judgment module is specifically configured to:
  • control module is specifically configured to:
  • control module is specifically further configured to:
  • the above aspect and any possible implementation manner further provide an implementation manner in which the judgment module repeatedly judges the collision between the unmanned vehicle and the traffic participant appearing from the roadside blind zone at a preset time interval risk.
  • a computer device including a memory, a processor, and a computer program stored on the memory and capable of running on the processor.
  • the processor executes the program as described above. The method described.
  • Another aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, and when the program is executed by a processor, the method as described above is implemented.
  • the embodiment of the application predicts the risk of collision between a traffic participant who suddenly appears from a blind spot on the roadside and an unmanned vehicle, so as to avoid a traffic participant who suddenly appears from a blind spot on the roadside and avoid collision accidents. .
  • FIG. 1 is a schematic flowchart of a vehicle control method provided by an embodiment of this application;
  • FIG. 2 is a schematic structural diagram of a vehicle control device provided by an embodiment of the application.
  • Figure 3 shows a block diagram of an exemplary computer system/server 012 suitable for implementing embodiments of the present invention.
  • Fig. 1 is a schematic diagram of a vehicle control method provided by an embodiment of the application. As shown in Fig. 1, the method includes the following steps:
  • Step S11 Obtain the information of the obstacle to be identified around the unmanned vehicle scanned by the lidar, and calculate the roadside blind area of the unmanned vehicle;
  • Step S12 judging the risk of collision between the unmanned vehicle and the traffic participant appearing from the blind spot on the roadside;
  • Step S13 If there is a risk of collision, control the unmanned vehicle to decelerate.
  • step S11 In a preferred implementation of step S11,
  • the electronic device (such as the vehicle's trip computer or vehicle terminal) running on the vehicle control method for avoiding roadside blind area traffic participants can control the lidar through a wired connection or a wireless connection sensor.
  • the trip computer or the on-board terminal can control the lidar sensor to collect point cloud data in a certain area at a certain frequency.
  • the above-mentioned target area may be an area of an obstacle to be detected.
  • wireless connection methods can include but are not limited to 3G/4G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other currently known or future wireless connection methods .
  • the information of the obstacle to be identified in this embodiment can be obtained by scanning laser radar.
  • the specifications of the lidar can be 16 lines, 32 lines or 64 lines and so on. The higher the number of lines, the greater the unit energy density of the lidar.
  • the lidar mounted on the current vehicle rotates 360 times per second to scan the information of the obstacle to be identified in a circle around the current vehicle, which is a frame of information about the obstacle to be identified.
  • the information of the obstacle to be recognized in this embodiment may include the point cloud of the obstacle to be recognized and the reflection value of the obstacle to be recognized. There may be one or more obstacles to be identified around the current vehicle.
  • the lidar scans you can use the current vehicle's centroid position as the origin of the coordinate system, and take the two directions parallel to the horizontal plane as the x direction and y direction respectively, as the length and width directions, and the direction perpendicular to the ground as the z direction , As the height direction. Then, the obstacle to be recognized can be identified in the coordinate system according to the relative position and distance between each point in the point cloud of the obstacle to be recognized and the origin.
  • the predetermined point cloud recognition model is used to identify the obstacle to be identified.
  • the preset point cloud recognition model may be various pre-trained algorithms capable of recognizing obstacles in point cloud data, for example, it may be an ICP algorithm (Iterative Closest Point), a random forest algorithm, and the like.
  • the identified obstacles are labeled to obtain a labeling result.
  • the marked shape may be the smallest rectangular parallelepiped circumscribed to each obstacle, or it may be an irregular curved surface close to the outer surface of each obstacle.
  • the above labeling results include the recognition results of various obstacles.
  • the point cloud data includes vehicles, traffic participants, and trees.
  • the labeling results also include labels or texts representing different obstacles, such as 1 for Bus, 2 stands for car, 3 stands for traffic participants, etc.
  • the traffic participants may be pedestrians, bicycles, vehicles, animals, etc., when they appear on the road, they will affect the driving of unmanned vehicles.
  • the traffic participant is a pedestrian as an example.
  • the roadside blind area of the unmanned vehicle is calculated based on the driving direction of the unmanned vehicle and the position and size of the obstacle.
  • Obtain the location information and heading information of the unmanned vehicle determine the location relationship between the unmanned vehicle and the road on which it is located, and calculate the blind area of the unmanned vehicle based on the location relationship and the obstacle recognition result.
  • the high-precision location information of the unmanned vehicle is matched with a series of longitude and latitude recording points of the road data to determine the specific location of the unmanned vehicle on the road, and then the unmanned vehicle is calculated according to the road environment where the unmanned vehicle is located. Blind area for people driving vehicles.
  • the lidar of the unmanned vehicle is used as the origin, a tangent is made to the left and right edges of the obstacle, and the fan-shaped area formed by the inner side of the two tangents and the obstacle is determined as the blind zone.
  • the blind zone is caused by obstacles blocking the scanning of the lidar.
  • this embodiment is aimed at the potential collision risk of a traffic participant who suddenly appears in front of the unmanned vehicle from the blind spot on the roadside and the unmanned vehicle.
  • the traffic participants in the blind area caused by the obstruction of the vehicle in front of the unmanned vehicle since they will not appear suddenly, it will not affect the safe driving of the unmanned vehicle.
  • the roadside blind areas located on both sides of the current lane where the unmanned vehicle is located in the blind area are determined.
  • Roadside blind spots are generally caused by large vehicles, such as buses and trucks, parked on the side of the road or driving in the outer lane of unmanned vehicles. Due to its large size, it will block lidar scanning, making it impossible for unmanned vehicles to know whether there are traffic participants outside. A traffic participant suddenly enters the road where the unmanned vehicle is located from the blind spot on the roadside. Even if the unmanned vehicle brakes, it is likely to still collide with the traffic participant due to the limitation of the braking distance.
  • the judgment criteria include: there is a roadside blind area, the roadside blind area is caused by a large vehicle, and the large vehicle is separately located in the outer lane of the road.
  • the preset point cloud recognition model can identify the type and size of the corresponding obstacle to determine whether the obstacle is a large vehicle.
  • a sensor such as a camera of an unmanned vehicle is used to identify the lane line of the road to determine whether the large vehicle is located in the outer lane of the road. If it is judged to be a large vehicle that is parked in the outer lane of the road, it is more likely to be a bus arriving and disembarking passengers. In this case, it is very likely that traffic participants will suddenly enter the driverless vehicle from the front of the bus Lane (bus driving in the same direction as the driverless vehicle), or the lane where the driverless vehicle is located suddenly from the rear of the bus (bus facing the driverless vehicle).
  • the large vehicle is located on the outer lane of the road alone, it is considered that a traffic participant suddenly enters the lane of the driverless vehicle from the front of the roadside blind area in front of the large vehicle (driving in the same direction as the driverless vehicle). Bus), or suddenly move from the rear of the bus to the lane where the driverless vehicle is located (a bus facing the driverless vehicle) is more likely.
  • step S12 In a preferred implementation of step S12,
  • the possible range of the traffic participant is calculated, including the traffic participant and the vehicle at the current moment And the location of the next moment.
  • a traffic participant gets off the bus on the side of the road, he is located in the blind area caused by the bus blocking the lidar; when the traffic participant crosses the road from the front of the bus, the driverless vehicle scans and recognizes the The traffic participant brakes, but if the distance between the traffic participant and the unmanned vehicle is less than the shortest braking distance of the unmanned vehicle's current speed when the traffic participant appears, a collision accident will occur.
  • the speed of traffic participants crossing the road can be specified as 5 meters per second, which is higher than the general speed of the general population to cover the crossing behavior of most people.
  • the preset safety threshold is assumed to be 1 second, that is, traffic participants can cross the lane within 1 second. If the difference between the time when the unmanned vehicle arrives at the junction and the time when the traffic participants arrive at the junction is less than or equal to 1 second, there is a risk of collision.
  • the distance between the unmanned vehicle and the junction is 100 meters
  • the speed of the unmanned vehicle is 72 kilometers per hour, or 20 meters per second
  • the time to reach the junction is 5 seconds
  • the traffic participants are from the roadside
  • the distance from the blind zone to the junction is 10 meters
  • the time to reach the junction is 2 seconds; then the absolute difference between the time the driverless vehicle arrives at the junction and the time when the traffic participants reach the junction is greater than 1 second, then no There is a risk of collision.
  • the distance between the unmanned vehicle and the junction is 60 meters
  • the speed of the unmanned vehicle is 72 kilometers per hour, or 20 meters per second
  • the time to reach the junction is 3 seconds
  • the traffic participants are from the roadside
  • the distance from the blind zone to the junction is 10 meters, and the time to reach the junction is 2 seconds; then the absolute difference between the time for the unmanned vehicle to reach the junction and the time for the traffic participants to reach the junction is less than 1 second, then there is Risk of collision.
  • step S13 In a preferred implementation of step S13,
  • the unmanned vehicle keeps its current navigation and speed to continue driving.
  • the speed of the unmanned vehicle is adjusted according to the shortest braking distance of the unmanned vehicle at different vehicle speeds so that the shortest braking distance is smaller than the distance between the unmanned vehicle and the intersection. This can absolutely guarantee that unmanned vehicles will not collide with traffic participants who emerge from blind spots on the roadside.
  • the above judgment and control steps are repeated until the unmanned vehicle leaves the intersection.
  • the risk of collision between the unmanned vehicle and traffic participants appearing from the roadside blind zone is calculated separately, and the unmanned vehicle is controlled to decelerate to ensure that the unmanned vehicle does not It will collide with traffic participants appearing in the multiple blind spots on the roadside.
  • the unmanned vehicles in the prior art can only react to the detected obstacles, and for traffic participants appearing from the blind spots on the roadside, they can only take emergency braking but cannot avoid collisions.
  • Risk situation By judging the risk of collision between traffic participants and the unmanned vehicle in the blind area on the roadside, the unmanned vehicle is controlled in advance to slow down, and the safe driving of the unmanned vehicle is realized.
  • a vehicle control method including:
  • the driving of the unmanned vehicle is controlled.
  • determining the roadside blind zone of the unmanned vehicle includes:
  • the method before judging the risk of collision between the unmanned vehicle and a traffic participant appearing from the roadside blind zone, the method further includes:
  • the criteria for determining the potential collision scene include: there is a roadside blind area, the roadside blind area is caused by a large vehicle, and the large vehicle is located in an outer lane of the road.
  • using the information of the obstacle to be identified to perform obstacle recognition includes:
  • Obstacle recognition is performed on the obstacle information to be recognized by using a preset point cloud recognition model to obtain the type, size and position of the obstacle.
  • judging the risk of collision between the unmanned vehicle and a traffic participant emerging from the roadside blind zone includes:
  • the difference between the predicted time for the unmanned vehicle to arrive at the junction and the absolute value of the predicted time for the traffic participant to arrive at the junction is less than or equal to a preset safety threshold, it is determined that there is a risk of collision.
  • controlling the driving of the unmanned vehicle includes:
  • FIG. 2 is a schematic structural diagram of a vehicle control device provided by an embodiment of the application, as shown in FIG. 2, including:
  • the acquisition module 21 is used to acquire information about obstacles to be identified around the unmanned vehicle scanned by lidar, and calculate the roadside blind area of the unmanned vehicle;
  • the judging module 22 is used to judge the risk of collision between an unmanned vehicle and a traffic participant appearing from the roadside blind zone;
  • the control module 23 is used for controlling the unmanned vehicle to decelerate if there is a risk of collision.
  • the electronic device (such as the vehicle's trip computer or on-board terminal) running on the vehicle control method for traffic participants in the blind spot avoidance side can control the lidar sensor through a wired connection or a wireless connection .
  • the trip computer or on-board terminal can control the lidar sensor to collect point cloud data in a certain area at a certain frequency.
  • the above-mentioned target area may be an area of an obstacle to be detected.
  • wireless connection methods can include but are not limited to 3G/4G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other currently known or future wireless connection methods .
  • the information of the obstacle to be identified in this embodiment can be obtained by scanning laser radar.
  • the specifications of the lidar can be 16 lines, 32 lines or 64 lines and so on. The higher the number of lines, the greater the unit energy density of the lidar.
  • the lidar mounted on the current vehicle rotates 360 times per second to scan the information of the obstacle to be identified in a circle around the current vehicle, which is a frame of information about the obstacle to be identified.
  • the information of the obstacle to be recognized in this embodiment may include the point cloud of the obstacle to be recognized and the reflection value of the obstacle to be recognized. There may be one or more obstacles to be identified around the current vehicle.
  • the lidar scans you can use the current vehicle's centroid position as the origin of the coordinate system, and take the two directions parallel to the horizontal plane as the x direction and y direction respectively, as the length and width directions, and the direction perpendicular to the ground as the z direction , As the height direction. Then, the obstacle to be recognized can be identified in the coordinate system according to the relative position and distance between each point in the point cloud of the obstacle to be recognized and the origin.
  • the predetermined point cloud recognition model is used to identify the obstacle to be identified.
  • the preset point cloud recognition model may be various pre-trained algorithms capable of recognizing obstacles in point cloud data, for example, it may be an ICP algorithm (Iterative Closest Point), a random forest algorithm, and the like.
  • the identified obstacles are labeled to obtain a labeling result.
  • the marked shape can be the smallest rectangular parallelepiped circumscribed to each obstacle, or it can be an irregular curved surface close to the outer surface of each obstacle.
  • the above labeling results include the recognition results of various obstacles.
  • the point cloud data includes vehicles, traffic participants, and trees.
  • the labeling results also include labels or texts representing different obstacles, such as 1 for Bus, 2 stands for car, 3 stands for traffic participants, etc.
  • the traffic participants may be pedestrians, bicycles, vehicles, animals, etc., when they appear on the road, they will affect the driving of unmanned vehicles.
  • the traffic participant is a pedestrian as an example.
  • the roadside blind area of the unmanned vehicle is calculated based on the driving direction of the unmanned vehicle and the position and size of the obstacle.
  • Obtain the location information and heading information of the unmanned vehicle determine the location relationship between the unmanned vehicle and the road on which it is located, and calculate the blind area of the unmanned vehicle based on the location relationship and the obstacle recognition result.
  • the specific location of the unmanned vehicle on the road is determined, and then calculated according to the road environment where the unmanned vehicle is located The blind spot of unmanned vehicles.
  • the lidar of the unmanned vehicle is used as the origin, a tangent is made to the left and right edges of the obstacle, and the fan-shaped area formed by the inner side of the two tangents and the obstacle is determined as the blind zone.
  • the blind zone is caused by obstacles blocking the scanning of the lidar.
  • this embodiment is aimed at the potential collision risk of a traffic participant who suddenly appears in front of the unmanned vehicle from the blind spot on the roadside and the unmanned vehicle.
  • the traffic participants in the blind area caused by the obstruction of the vehicle in front of the unmanned vehicle since they will not appear suddenly, it will not affect the safe driving of the unmanned vehicle.
  • the roadside blind areas located on both sides of the current lane where the unmanned vehicle is located in the blind area are determined.
  • Roadside blind spots are generally caused by large vehicles, such as buses and trucks, parked on the side of the road or driving in the outer lane of unmanned vehicles. Due to its large size, it will block lidar scanning, making it impossible for unmanned vehicles to know whether there are traffic participants or vehicles outside. A traffic participant suddenly enters the road where the unmanned vehicle is located from the blind spot on the roadside. Even if the unmanned vehicle brakes, it is likely to still collide with the traffic participant due to the limitation of the braking distance.
  • the judgment criteria include: there is a roadside blind area, the roadside blind area is caused by a large vehicle, and the large vehicle is separately located in the outer lane of the road.
  • the preset point cloud recognition model can identify the type and size of the corresponding obstacle to determine whether the obstacle is a large vehicle.
  • a sensor such as a camera of an unmanned vehicle is used to identify the lane line of the road to determine whether the large vehicle is located in the outer lane of the road. If it is judged to be a large vehicle that is parked in the outer lane of the road, it is more likely to be a bus arriving and disembarking passengers. In this case, it is very likely that traffic participants suddenly enter the driverless vehicle from the front of the bus Lane (bus driving in the same direction as the driverless vehicle), or suddenly from the rear of the bus to the lane where the driverless vehicle is located (bus facing the driverless vehicle).
  • the large vehicle is located in the outer lane of the road alone, it is considered that a traffic participant suddenly enters the lane of the driverless vehicle from the front of the roadside blind area in front of the large vehicle (driving in the same direction as the driverless vehicle). Bus), or suddenly move from the rear of the bus to the lane where the driverless vehicle is located (a bus facing the driverless vehicle) is more likely.
  • the possible range of the traffic participant is calculated, including the traffic participant and the vehicle at the current moment And the location of the next moment.
  • a traffic participant gets off the bus on the side of the road, he is located in the blind area caused by the bus blocking the lidar; when the traffic participant crosses the road from the front of the bus, the driverless vehicle scans and recognizes the The traffic participant brakes, but if the distance between the traffic participant and the unmanned vehicle is less than the shortest braking distance of the unmanned vehicle's current speed when the traffic participant appears, a collision accident will occur.
  • the speed of traffic participants crossing the road can be specified as 5 meters per second, which is higher than the general speed of the general population to cover the crossing behavior of most people.
  • the trajectory of the driverless vehicle and the traffic participant crossing the road intersects.
  • the time for the driverless vehicle to reach the intersection the driverless vehicle and the intersection.
  • the distance between the points ⁇ the speed of the unmanned vehicle; the time when the traffic participant arrives at the junction the distance between the traffic participant and the junction ⁇ the speed of the traffic participant. Since the lane width is 3.5 meters, the preset safety threshold is assumed to be 1 second, that is, traffic participants can cross the lane within 1 second. If the difference between the time when the unmanned vehicle arrives at the junction and the time when the traffic participants arrive at the junction is less than or equal to 1 second, there is a risk of collision.
  • the distance between the unmanned vehicle and the junction is 100 meters
  • the speed of the unmanned vehicle is 72 kilometers per hour, or 20 meters per second
  • the time to reach the junction is 5 seconds
  • the traffic participants are from the roadside
  • the distance from the blind zone to the junction is 10 meters
  • the time to reach the junction is 2 seconds; then the absolute difference between the time the driverless vehicle arrives at the junction and the time when the traffic participants reach the junction is greater than 1 second, then no There is a risk of collision.
  • the distance between the unmanned vehicle and the junction is 60 meters
  • the speed of the unmanned vehicle is 72 kilometers per hour, or 20 meters per second
  • the time to reach the junction is 3 seconds
  • the traffic participants are from the roadside
  • the distance from the blind zone to the junction is 10 meters, and the time to reach the junction is 2 seconds; then the absolute difference between the time for the unmanned vehicle to reach the junction and the time for the traffic participants to reach the junction is less than 1 second, then there is Risk of collision.
  • control module 23 In a preferred implementation of the control module 23,
  • the unmanned vehicle keeps its current navigation and speed to continue driving.
  • the speed of the unmanned vehicle is adjusted according to the shortest braking distance of the unmanned vehicle at different vehicle speeds so that the shortest braking distance is smaller than the distance between the unmanned vehicle and the intersection. This can absolutely guarantee that unmanned vehicles will not collide with traffic participants who emerge from blind spots on the roadside.
  • the above judgment and control steps are repeated until the unmanned vehicle leaves the intersection.
  • the risk of collision between the unmanned vehicle and traffic participants appearing from the roadside blind zone is calculated separately, and the unmanned vehicle is controlled to decelerate to ensure that the unmanned vehicle does not It will collide with traffic participants appearing in the multiple blind spots on the roadside.
  • the unmanned vehicles in the prior art can only react to the detected obstacles, and for traffic participants appearing from the blind spots on the roadside, they can only take emergency braking but cannot avoid collisions.
  • Risk situation By judging the risk of collision between traffic participants and the unmanned vehicle in the blind area on the roadside, the unmanned vehicle is controlled in advance to slow down, and the safe driving of the unmanned vehicle is realized.
  • the disclosed method and device can be implemented in other ways.
  • the device embodiments described above are only schematic.
  • the division of the unit is only a logical function division, and there may be other divisions in actual implementation, for example, multiple units or components may be combined or Can be integrated into another system, or some features can be ignored, or not implemented.
  • the displayed or discussed mutual coupling or direct coupling or communication connection may be indirect coupling or communication connection through some interfaces, devices or units, and may be in electrical, mechanical, or other forms.
  • the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.
  • the integrated unit may be implemented in the form of hardware, or may be implemented in the form of hardware plus software functional units.
  • FIG. 3 shows a block diagram of an exemplary computer system/server 012 suitable for implementing embodiments of the present invention.
  • the computer system/server 012 shown in FIG. 3 is only an example, and should not bring any limitation to the function and application scope of the embodiment of the present invention.
  • the computer system/server 012 is represented in the form of a general-purpose computing device.
  • the components of the computer system/server 012 may include, but are not limited to: one or more processors or processing units 016, a system memory 028, and a bus 018 connecting different system components (including the system memory 028 and the processing unit 016).
  • the bus 018 represents one or more of several types of bus structures, including a memory bus or a memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the multiple bus structures.
  • these architectures include, but are not limited to, industry standard architecture (ISA) bus, micro channel architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and peripheral component interconnection ( PCI) bus.
  • ISA industry standard architecture
  • MAC micro channel architecture
  • VESA Video Electronics Standards Association
  • PCI peripheral component interconnection
  • the computer system/server 012 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by the computer system/server 012, including volatile and nonvolatile media, removable and non-removable media.
  • the system memory 028 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 030 and/or cache memory 032.
  • the computer system/server 012 may further include other removable/non-removable, volatile/nonvolatile computer system storage media.
  • the storage system 034 can be used to read and write non-removable, non-volatile magnetic media (not shown in FIG. 3, usually referred to as a "hard drive").
  • a disk drive for reading and writing to removable non-volatile disks such as "floppy disks”
  • a removable non-volatile optical disk such as CD-ROM, DVD-ROM
  • other optical media read and write optical disc drives.
  • each drive can be connected to the bus 018 through one or more data media interfaces.
  • the memory 028 may include at least one program product, and the program product has a set (for example, at least one) program modules, which are configured to perform the functions of the embodiments of the present invention.
  • a program/utility tool 040 with a set of (at least one) program module 042 can be stored in, for example, the memory 028.
  • Such program module 042 includes, but is not limited to, an operating system, one or more application programs, and other programs Modules and program data, each of these examples or some combination may include the realization of a network environment.
  • the program module 042 generally executes the functions and/or methods in the described embodiments of the present invention.
  • the computer system/server 012 can also communicate with one or more external devices 014 (such as a keyboard, pointing device, display 024, etc.).
  • the computer system/server 012 communicates with an external radar device, and can also communicate with one or Multiple devices that enable users to interact with the computer system/server 012, and/or communicate with any devices that enable the computer system/server 012 to communicate with one or more other computing devices (such as network cards, modems, etc.) Communication. This communication can be performed through an input/output (I/O) interface 022.
  • I/O input/output
  • the computer system/server 012 can also communicate with one or more networks (such as a local area network (LAN), a wide area network (WAN), and/or a public network, such as the Internet) through the network adapter 020.
  • networks such as a local area network (LAN), a wide area network (WAN), and/or a public network, such as the Internet
  • the network adapter 020 communicates with other modules of the computer system/server 012 through the bus 018.
  • other hardware and/or software modules can be used in conjunction with the computer system/server 012, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems , Tape drives and data backup storage systems.
  • the processing unit 016 executes the functions and/or methods in the described embodiments of the present invention by running a program stored in the system memory 028.
  • the above-mentioned computer program may be set in a computer storage medium, that is, the computer storage medium is encoded with a computer program.
  • the program When the program is executed by one or more computers, one or more computers can execute the operations shown in the above embodiments of the present invention. Method flow and/or device operation.
  • the computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium.
  • the computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or any combination of the above.
  • computer-readable storage media include: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), Erasable programmable read only memory (EPROM or flash memory), optical fiber, portable compact disk read only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.
  • the computer-readable storage medium may be any tangible medium containing or storing a program, which may be used by or in combination with an instruction execution system, apparatus, or device.
  • the computer-readable signal medium may include a data signal propagated in baseband or as a part of a carrier wave, and computer-readable program code is carried therein. This propagated data signal can take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above.
  • the computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, and the computer-readable medium may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. .
  • the program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wire, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
  • the computer program code for performing the operations of the present invention can be written in one or more programming languages or a combination thereof.
  • the programming languages include object-oriented programming languages such as Java, Smalltalk, C++, as well as conventional Procedural programming language-such as "C" language or similar programming language.
  • the program code may execute entirely on the user's computer, partly on the user's computer, as an independent software package, partly on the user's computer and partly on a remote computer, or entirely on the remote computer or server.
  • the remote computer can be connected to the user's computer through any kind of network-including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, using an Internet service provider to pass the Internet) connection).
  • LAN local area network
  • WAN wide area network

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Abstract

一种车辆控制方法、装置、设备和计算机存储介质，所述方法包括：获取激光雷达扫描无人驾驶车辆周围的待识别障碍物的信息；利用所述待识别障碍物的信息进行障碍物识别；基于所述障碍物识别的结果，确定无人驾驶车辆的路侧盲区；判断所述无人驾驶车辆与从所述路侧盲区中出现的交通参与者的碰撞风险；根据判断结果，控制无人驾驶车辆的行驶。通过判断路侧盲区出现交通参与者与无人驾驶车辆的碰撞风险，提前控制无人驾驶车辆进行减速，提高了无人驾驶车辆的安全性。

Description

一种车辆控制方法、装置、设备和计算机存储介质
本申请要求了申请日为2019年01月15日，申请号为201910036316.9发明名称为“一种激光雷达路侧盲区交通参与者避让方法和装置”的中国专利申请的优先权。
技术领域
本申请涉及自动控制领域，尤其涉及一种车辆控制方法、装置、设备和计算机存储介质。
背景技术
在无人驾驶车辆中，集成了多类传感器：GPS-IMU(惯性测量单元，Inertial Measurement Unit)组合导航模块、相机、激光雷达、毫米波雷达等传感器。
无人驾驶车辆行驶过程中，主要依靠激光雷达对障碍物进行检测，但是，现有无人驾驶车辆的障碍物检测，只能检测到出现在激光雷达视野中的障碍物，无法检测由于障碍物遮挡造成的盲区中的情况。而对“鬼探头”，即行人、自行车、车辆、动物等交通参与者突然从其他障碍物遮挡造成的盲区中出现，而无人驾驶车辆只能在识别到交通参与者后才能进行反应，如刹车等；由于突然出现的交通参与者留给无人驾驶车辆的反应时间有限，即使刹车也可能会发生碰撞，这就增加了突发危险和事故发生的几率。
发明内容
本申请的多个方面提供一种车辆控制方法和装置，用以避让从路侧盲区突然出现的交通参与者，避免碰撞事故。
本申请的一方面，提供一种车辆控制方法，包括：
获取激光雷达扫描无人驾驶车辆周围的待识别障碍物的信息，计算无人驾驶车辆的路侧盲区；
判断无人驾驶车辆与从所述路侧盲区中出现的交通参与者的碰撞风险；
根据是否存在碰撞风险，控制无人驾驶车辆的行驶。
如上所述的方面和任一可能的实现方式，进一步提供一种实现方式，计算无人驾驶车辆的路侧盲区包括：
获取无人驾驶车辆的位置信息、航向信息，基于无人驾驶车辆与所在道路的位置关系和障碍物识别结果，得到无人驾驶车辆的盲区；
根据无人驾驶车辆的当前车道，确定盲区中位于无人驾驶车辆所在的当前车道两侧的路侧盲区。
如上所述的方面和任一可能的实现方式，进一步提供一种实现方式，在判断无人驾驶车辆与从所述路侧盲区中出现的交通参与者的碰撞风险之前，判断当前道路场景是否为潜在碰撞场景，判断标准包括：存在路侧盲区、所述路侧盲区为大型车辆造成、所述大型车辆单独位于道路外侧车道。
如上所述的方面和任一可能的实现方式，进一步提供一种实现方式，判断无人驾驶车辆与从所述路侧盲区中出现的交通参与者的碰撞风险包括：
若无人驾驶车辆到达交汇点的时间与所述交通参与者到达交汇点的时间的绝对值之差小于或等于预设安全阈值，则存在碰撞风险。
如上所述的方面和任一可能的实现方式，进一步提供一种实现方式， 根据是否存在碰撞风险，控制无人驾驶车辆的行驶包括：
若存在碰撞风险，则控制无人驾驶车辆进行减速。
如上所述的方面和任一可能的实现方式，进一步提供一种实现方式，控制无人驾驶车辆进行减速包括：
调节无人驾驶车辆的速度，使其最短刹车距离小于无人驾驶车辆与交汇点的距离。
如上所述的方面和任一可能的实现方式，进一步提供一种实现方式，所述方法还包括：
以预设时间间隔重复判断无人驾驶车辆与从所述路侧盲区中出现的交通参与者的碰撞风险。
本申请的另一方面，提供一种车辆控制装置，包括：
获取模块，用于获取激光雷达扫描无人驾驶车辆周围的待识别障碍物的信息，计算无人驾驶车辆的路侧盲区；
判断模块，用于判断无人驾驶车辆与从所述路侧盲区中出现的交通参与者的碰撞风险；
控制模块，用于根据是否存在碰撞风险，控制无人驾驶车辆的行驶。
如上所述的方面和任一可能的实现方式，进一步提供一种实现方式，所述获取模块具体用于：
获取无人驾驶车辆的位置信息、航向信息，基于无人驾驶车辆与所在道路的位置关系和障碍物识别结果，得到无人驾驶车辆的盲区；
根据无人驾驶车辆的当前车道，确定盲区中位于无人驾驶车辆所在的当前车道两侧的路侧盲区。
如上所述的方面和任一可能的实现方式，进一步提供一种实现方式， 所述判断模块判断无人驾驶车辆与从所述路侧盲区中出现的交通参与者的碰撞风险之前，还用于判断当前道路场景是否为潜在碰撞场景，判断标准包括：存在路侧盲区、所述路侧盲区为大型车辆造成、所述大型车辆单独位于道路外侧车道。
如上所述的方面和任一可能的实现方式，进一步提供一种实现方式，所述判断模块具体用于：
若无人驾驶车辆到达交汇点的时间与所述交通参与者到达交汇点的时间的绝对值之差小于或等于预设安全阈值，则存在碰撞风险。
如上所述的方面和任一可能的实现方式，进一步提供一种实现方式，所述控制模块具体用于：
若存在碰撞风险，则控制无人驾驶车辆进行减速。
如上所述的方面和任一可能的实现方式，进一步提供一种实现方式，所述控制模块具体还用于：
调节无人驾驶车辆的速度，使其最短刹车距离小于无人驾驶车辆与交汇点的距离。
如上所述的方面和任一可能的实现方式，进一步提供一种实现方式，所述判断模块以预设时间间隔重复判断无人驾驶车辆与从所述路侧盲区中出现的交通参与者的碰撞风险。
本发明的另一方面，提供一种计算机设备，包括存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序，所述处理器执行所述程序时实现如以上所述的方法。
本发明的另一方面，提供一种计算机可读存储介质，其上存储有计算机程序，所述程序被处理器执行时实现如以上所述的方法。
由所述技术方案可知，本申请实施例，通过预测从路侧盲区突然出现的交通参与者与无人驾驶车辆的碰撞风险，用以避让从路侧盲区突然出现的交通参与者，避免碰撞事故。
附图说明
为了更清楚地说明本申请实施例中的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作一简单地介绍，显而易见地，下面描述中的附图是本申请的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动性的前提下，还可以根据这些附图获得其他的附图。
图1为本申请一实施例提供的车辆控制方法的流程示意图；
图2为本申请一实施例提供的车辆控制装置的结构示意图；
图3示出了适于用来实现本发明实施方式的示例性计算机系统/服务器012的框图。
具体实施方式
为使本申请实施例的目的、技术方案和优点更加清楚，下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有作出创造性劳动前提下所获得的全部其他实施例，都属于本申请保护的范围。
图1为本申请一实施例提供的车辆控制方法的示意图，如图1所示，包括以下步骤：
步骤S11、获取激光雷达扫描无人驾驶车辆周围的待识别障碍物的信息，计算无人驾驶车辆的路侧盲区；
步骤S12、判断无人驾驶车辆与从所述路侧盲区中出现的交通参与 者的碰撞风险；
步骤S13、若存在碰撞风险，则控制无人驾驶车辆进行减速。
步骤S11的一种优选实现方式中，
在本实施例中，所述用于避让路侧盲区交通参与者的车辆控制方法运行于其上的电子设备(例如车辆的行车电脑或车载终端)可以通过有线连接方式或者无线连接方式控制激光雷达传感器。具体地，行车电脑或车载终端可以控制激光雷达传感器以某一频率采集某一区域的点云数据。上述目标区域可以是待检测障碍物的区域。
需要指出的是，上述无线连接方式可以包括但不限于3G/4G连接、WiFi连接、蓝牙连接、WiMAX连接、Zigbee连接、UWB(ultra wideband)连接、以及其他现在已知或将来开发的无线连接方式。
本实施例的待识别障碍物的信息可以采用激光雷达扫射得到的。激光雷达的规格可以采用16线、32线或者64线等等。其中线数越高表示激光雷达的单位能量密度越大。本实施例中，装在当前车辆上的激光雷达在每一秒中旋转360次，扫描当前车辆周围一圈的待识别的障碍物的信息，为一帧待识别的障碍物的信息。本实施例中的待识别的障碍物的信息可以包括待识别的障碍物的点云以及待识别的障碍物的反射值。当前车辆周围的待识别的障碍物可以有一个，也可以有多个。激光雷达扫描之后，可以以当前车辆的质心位置为坐标系的原点，并取平行于水平面的两个方向分别为x方向和y方向，作为长度方向和宽度方向，垂直于地面的方向为z方向，作为高度方向。然后可以根据待识别障碍物的点云中的每一个点与原点的相对位置和距离，在坐标系中标识待识别的障碍物。
本实施例中，获取无人驾驶车辆周围的待识别障碍物的信息后，利用预设的点云识别模型对所述待识别障碍物进行识别。所述预设的点云识别模型可以是各种预先训练的能够识别点云数据中障碍物的算法，例如可以是ICP算法(Iterative Closest Point，就近点搜索法)、随机森林算法等。在利用上述点云识别模型识别点云数据中的障碍物后，对识别出的障碍物进行标注，得到标注结果。在对识别出的障碍物进行标注时，标注出的形状可以为与各障碍物外切的最小长方体，也可以是与各障碍物的外表面贴近的不规则曲面。可以理解的是，上述标注结果中包括对各障碍物的识别结果，例如点云数据中包括车辆、交通参与者以及树木，则标注结果中也包括表示不同障碍物的标号或文字，如1代表公交车、2代表小汽车、3代表交通参与者等。
所述交通参与者可以是行人、自行车、车辆、动物等，当其出现在道路上，会对无人驾驶车辆的行驶造成影响。
在本实施例中，以所述交通参与者为行人为例。
本实施例中，基于无人驾驶车辆的行驶方向和障碍物的位置、尺寸，计算得到无人驾驶车辆的路侧盲区。
获取无人驾驶车辆的位置信息、航向信息，确定无人驾驶车辆与所在道路的位置关系，基于所述位置关系和障碍物识别结果，计算得到无人驾驶车辆的盲区。
优选地，通过无人驾驶车辆的高精度位置信息和道路数据的一系列经纬度记录点进行匹配，确定无人驾驶车辆在道路的具体位置，然后根据无人驾驶车辆所处的道路环境计算得到无人驾驶车辆的盲区。
优选地，以无人驾驶车辆的激光雷达为原点，向障碍物左右边缘做 切线，并将两条切线内侧与障碍物形成的扇形区域确定为盲区。所述盲区为障碍物遮挡激光雷达的扫描造成的。
本实施例中，针对的是从路侧盲区中突然出现在无人驾驶车辆前方的交通参与者与无人驾驶车辆发生的潜在碰撞风险。对于无人驾驶车辆前方的车辆遮挡造成的盲区中的交通参与者，由于不会突然出现，因此不会对无人驾驶车辆的安全行驶造成影响。
优选地，根据无人驾驶车辆的当前车道，确定盲区中位于无人驾驶车辆所在的当前车道两侧的路侧盲区。
路侧盲区一般是由停靠在路侧或行车在无人驾驶车辆的外侧车道的大型车辆，例如公交车、卡车造成的。由于其体积较大，会遮挡激光雷达的扫描，使无人驾驶车辆无法获知其外侧是否存在交通参与者。交通参与者突然从路侧盲区进入无人驾驶车辆所在道路，即使无人驾驶车辆进行刹车，由于刹车距离的限制，很可能仍会与所述交通参与者发生碰撞。
判断当前道路场景是否为潜在碰撞场景，判断标准包括：存在路侧盲区、所述路侧盲区为大型车辆造成、所述大型车辆单独位于道路外侧车道。
优选地，对路侧盲区，一般仅考虑由大型车辆造成的路侧盲区。预设的点云识别模型可以识别对应的障碍物类型和尺寸，以判断所述障碍物是否为大型车辆。
优选地，通过无人驾驶车辆的摄像头等传感器，识别道路车道线，确定所述大型车辆是否位于道路外侧车道。如果判断为停靠与道路外侧车道的大型车辆，则其为到站上下乘客的公交车的概率较大，对于这种 情况，很有可能有交通参与者从公交车车头突然进入无人驾驶车辆所在车道(与无人驾驶车辆同向行驶的公交车)，或从公交车车尾突然进行无人驾驶车辆所在车道(与无人驾驶车辆相向形式的公交车)。
优选地，若所述大型车辆单独位于道路外侧车道，则认为所述大型车辆前方的路侧盲区中出现交通参与者从车头突然进入无人驾驶车辆所在车道(与无人驾驶车辆同向行驶的公交车)，或从公交车车尾突然进行无人驾驶车辆所在车道(与无人驾驶车辆相向形式的公交车)的概率较大。
在步骤S12的一种优选实现方式中，
判断无人驾驶车辆与从所述路侧盲区中出现的交通参与者的碰撞风险。
优选地，假设从所述路侧盲区中出现交通参与者横穿马路，进入无人驾驶车辆的前方，计算所述交通参与者可能出现的范围，包括，所述交通参与者和车辆在当前时刻以及接下来的时刻所处位置。
例如，交通参与者从路侧的公交车下车后，位于公交车遮挡激光雷达造成的盲区中；交通参与者从公交车车头方向出现横穿马路，则无人驾驶车辆扫描并识别到所述交通参与者，进行刹车，但是若如交通参与者出现时与无人驾驶车辆的距离已经小于无人驾驶车辆当前时速的最短刹车距离，则造成碰撞事故。
其中，横穿马路的交通参与者速度我们可以规定为5米/秒，即高于普通人群的一般速度以覆盖大多数人的横穿行为。
计算无人驾驶车辆是否与横穿马路的交通参与者存在碰撞风险，无人驾驶车辆与横穿马路的交通参与者的轨迹交汇，无人驾驶车辆到达交 汇点的时间＝无人驾驶车辆与交汇点的距离÷无人驾驶车辆速度；交通参与者到达交汇点的时间＝交通参与者与交汇点的距离÷交通参与者速度。由于车道宽度为3.5米，则预设的安全阈值假设规定为1秒，即交通参与者可以在1秒之内越过该车道。若无人驾驶车辆到达交汇点的时间与交通参与者到达交汇点的时间的绝对值之差小于或等于1秒，则存在碰撞风险。
例如，无人驾驶车辆与交汇点的距离为100米，无人驾驶车辆的速度为72千米/小时，即20米/秒，到达交汇点的时间为5秒；而交通参与者从路侧盲区到达交汇点的距离为10米，则到达交汇点的时间为2秒；则无人驾驶车辆到达交汇点的时间与交通参与者到达交汇点的时间的绝对值之差大于1秒，则不存在碰撞风险。
例如，无人驾驶车辆与交汇点的距离为60米，无人驾驶车辆的速度为72千米/小时，即20米/秒，到达交汇点的时间为3秒；而交通参与者从路侧盲区到达交汇点的距离为10米，则到达交汇点的时间为2秒；则无人驾驶车辆到达交汇点的时间与交通参与者到达交汇点的时间的绝对值之差小于1秒，则存在碰撞风险。
在步骤S13的一种优选实现方式中，
根据是否存在碰撞风险，控制无人驾驶车辆的行驶。
若不存在碰撞风险；无人驾驶车辆保持现有航行和速度继续行驶。
若存在碰撞风险，则控制无人驾驶车辆进行减速。
优选地，根据无人驾驶车辆在不同车速下的最短刹车距离，调节无人驾驶车辆的速度，使其最短刹车距离小于无人驾驶车辆与交汇点的距离。这可以绝对保证无人驾驶车辆不会与从路侧盲区出现的交通参与者 发生碰撞。
优选地，以预设时间间隔，例如0.1秒，重复执行上述判断和控制步骤；直至无人驾驶车辆驶离所述交汇点。
优选地，若存在多个路侧盲区，则分别计算无人驾驶车辆与从所述路侧盲区中出现的交通参与者的碰撞风险，控制无人驾驶车辆进行减速，以保证无人驾驶车辆不会与所述多个路侧盲区出现的交通参与者发生碰撞。
采用上述实施例提供的技术方案，能够避免现有技术中无人驾驶车辆只能对探测到的障碍物进行反应，对于从路侧盲区出现的交通参与者只能采取紧急刹车，但无法避免碰撞风险的情况；通过判断路侧盲区出现交通参与者与无人驾驶车辆的碰撞风险，提前控制无人驾驶车辆进行减速，实现了无人驾驶车辆的安全驾驶。
需要说明的是，对于前述的各方法实施例，为了简单描述，故将其都表述为一系列的动作组合，但是本领域技术人员应该知悉，本申请并不受所描述的动作顺序的限制，因为依据本申请，某些步骤可以采用其他顺序或者同时进行。其次，本领域技术人员也应该知悉，说明书中所描述的实施例均属于优选实施例，所涉及的动作和模块并不一定是本申请所必须的。
上述方法的主要技术方案如下：
提供了一种车辆控制方法，包括：
获取激光雷达扫描无人驾驶车辆周围的待识别障碍物的信息；
利用所述待识别障碍物的信息进行障碍物识别；
基于所述障碍物识别的结果，确定无人驾驶车辆的路侧盲区；
判断所述无人驾驶车辆与从所述路侧盲区中出现的交通参与者的碰撞风险；
根据判断结果，控制无人驾驶车辆的行驶。
作为其中一种实现方式，基于所述障碍物识别的结果，确定无人驾驶车辆的路侧盲区包括：
获取无人驾驶车辆的位置信息和航向信息，以确定所述无人驾驶车辆与所在道路的位置关系；
基于所述位置信息和所述障碍物识别的结果，确定无人驾驶车辆的盲区；
基于所述位置关系，确定所述盲区中位于所述无人驾驶车辆所在车道两侧的路侧盲区。
作为其中一种实现方式，在判断所述无人驾驶车辆与从所述路侧盲区中出现的交通参与者的碰撞风险之前，还包括：
判断当前道路场景是否为潜在碰撞场景，如果是，则继续执行判断所述无人驾驶车辆与从所述路侧盲区中出现的交通参与者的碰撞风险的步骤；
其中，所述潜在碰撞场景的判断标准包括：存在路侧盲区、所述路侧盲区由大型车辆造成且所述大型车辆位于道路外侧车道。
作为其中一种实现方式，利用所述待识别障碍物的信息进行障碍物识别包括：
利用预设的点云识别模型对所述待识别障碍物信息进行障碍物识别，得到障碍物的类型、尺寸和位置。
作为其中一种实现方式，判断所述无人驾驶车辆与从所述路侧盲区 中出现的交通参与者的碰撞风险包括：
确定所述无人驾驶车辆的预测行驶轨迹与从所述路侧盲区中出现的交通参与者的预测轨迹的交汇点；
若无人驾驶车辆到达所述交汇点的预测时间与所述交通参与者到达所述交汇点的预测时间的绝对值之差小于或等于预设安全阈值，则判断出存在碰撞风险。
作为其中一种实现方式，根据判断结果，控制无人驾驶车辆的行驶包括：
若存在碰撞风险，则控制无人驾驶车辆进行减速，使所述无人驾驶车辆的最短刹车距离小于所述无人驾驶车辆与所述交汇点的距离。
以上是关于方法实施例的介绍，以下通过装置实施例，对本发明所述方案进行进一步说明。
图2为本申请一实施例提供的车辆控制装置的结构示意图，如图2所示，包括：
获取模块21，用于获取激光雷达扫描无人驾驶车辆周围的待识别障碍物的信息，计算无人驾驶车辆的路侧盲区；
判断模块22，用于判断无人驾驶车辆与从所述路侧盲区中出现的交通参与者的碰撞风险；
控制模块23，用于若存在碰撞风险，则控制无人驾驶车辆进行减速。
在获取模块21的一种优选实现方式中，
在本实施例中，所述用于避让侧盲区交通参与者的车辆控制方法运行于其上的电子设备(例如车辆的行车电脑或车载终端)可以通过有线连接方式或者无线连接方式控制激光雷达传感器。具体地，行车电脑或车 载终端可以控制激光雷达传感器以某一频率采集某一区域的点云数据。上述目标区域可以是待检测障碍物的区域。
需要指出的是，上述无线连接方式可以包括但不限于3G/4G连接、WiFi连接、蓝牙连接、WiMAX连接、Zigbee连接、UWB(ultra wideband)连接、以及其他现在已知或将来开发的无线连接方式。
本实施例的待识别障碍物的信息可以采用激光雷达扫射得到的。激光雷达的规格可以采用16线、32线或者64线等等。其中线数越高表示激光雷达的单位能量密度越大。本实施例中，装在当前车辆上的激光雷达在每一秒中旋转360次，扫描当前车辆周围一圈的待识别的障碍物的信息，为一帧待识别的障碍物的信息。本实施例中的待识别的障碍物的信息可以包括待识别的障碍物的点云以及待识别的障碍物的反射值。当前车辆周围的待识别的障碍物可以有一个，也可以有多个。激光雷达扫描之后，可以以当前车辆的质心位置为坐标系的原点，并取平行于水平面的两个方向分别为x方向和y方向，作为长度方向和宽度方向，垂直于地面的方向为z方向，作为高度方向。然后可以根据待识别障碍物的点云中的每一个点与原点的相对位置和距离，在坐标系中标识待识别的障碍物。
本实施例中，获取无人驾驶车辆周围的待识别障碍物的信息后，利用预设的点云识别模型对所述待识别障碍物进行识别。所述预设的点云识别模型可以是各种预先训练的能够识别点云数据中障碍物的算法，例如可以是ICP算法(Iterative Closest Point，就近点搜索法)、随机森林算法等。在利用上述点云识别模型识别点云数据中的障碍物后，对识别出的障碍物进行标注，得到标注结果。在对识别出的障碍物进行标注时， 标注出的形状可以为与各障碍物外切的最小长方体，也可以是与各障碍物的外表面贴近的不规则曲面。可以理解的是，上述标注结果中包括对各障碍物的识别结果，例如点云数据中包括车辆、交通参与者以及树木，则标注结果中也包括表示不同障碍物的标号或文字，如1代表公交车、2代表小汽车、3代表交通参与者等。
所述交通参与者可以是行人、自行车、车辆、动物等，当其出现在道路上，会对无人驾驶车辆的行驶造成影响。
在本实施例中，以所述交通参与者为行人为例。
本实施例中，基于无人驾驶车辆的行驶方向和障碍物的位置、尺寸，计算得到无人驾驶车辆的路侧盲区。
获取无人驾驶车辆的位置信息、航向信息，确定无人驾驶车辆与所在道路的位置关系，基于所述位置关系和障碍物识别结果，计算得到无人驾驶车辆的盲区。
优选地，通过无人驾驶车辆的高精度位置信息，和道路数据的一系列经纬度记录点进行匹配，确定无人驾驶车辆在道路的具体位置，然后根据无人驾驶车辆所处的道路环境计算得到无人驾驶车辆的盲区。
优选地，以无人驾驶车辆的激光雷达为原点，向障碍物左右边缘做切线，并将两条切线内侧与障碍物形成的扇形区域确定为盲区。所述盲区为障碍物遮挡激光雷达的扫描造成的。
本实施例中，针对的是从路侧盲区中突然出现在无人驾驶车辆前方的交通参与者与无人驾驶车辆发生的潜在碰撞风险。对于无人驾驶车辆前方的车辆遮挡造成的盲区中的交通参与者，由于不会突然出现，因此不会对无人驾驶车辆的安全行驶造成影响。
优选地，根据无人驾驶车辆的当前车道，确定盲区中位于无人驾驶车辆所在的当前车道两侧的路侧盲区。
路侧盲区一般是由停靠在路侧或行车在无人驾驶车辆的外侧车道的大型车辆，例如公交车、卡车造成的。由于其体积较大，会遮挡激光雷达的扫描，使无人驾驶车辆无法获知其外侧是否存在交通参与者或车辆。交通参与者突然从路侧盲区进入无人驾驶车辆所在道路，即使无人驾驶车辆进行刹车，由于刹车距离的限制，很可能仍会与所述交通参与者发生碰撞。
判断当前道路场景是否为潜在碰撞场景，判断标准包括：存在路侧盲区、所述路侧盲区为大型车辆造成、所述大型车辆单独位于道路外侧车道。
优选地，对路侧盲区，一般仅考虑由大型车辆造成的路侧盲区。预设的点云识别模型可以识别对应的障碍物类型和尺寸，以判断所述障碍物是否为大型车辆。
优选地，通过无人驾驶车辆的摄像头等传感器，识别道路车道线，确定所述大型车辆是否位于道路外侧车道。如果判断为停靠与道路外侧车道的大型车辆，则其为到站上下乘客的公交车的概率较大，对于这种情况，很有可能有交通参与者从公交车车头突然进入无人驾驶车辆所在车道(与无人驾驶车辆同向行驶的公交车)，或从公交车车尾突然进行无人驾驶车辆所在车道(与无人驾驶车辆相向形式的公交车)。
优选地，若所述大型车辆单独位于道路外侧车道，则认为所述大型车辆前方的路侧盲区中出现交通参与者从车头突然进入无人驾驶车辆所在车道(与无人驾驶车辆同向行驶的公交车)，或从公交车车尾突然进 行无人驾驶车辆所在车道(与无人驾驶车辆相向形式的公交车)的概率较大。
在判断模块22的一种优选实现方式中，
判断无人驾驶车辆与从所述路侧盲区中出现的交通参与者的碰撞风险。
优选地，假设从所述路侧盲区中出现交通参与者横穿马路，进入无人驾驶车辆的前方，计算所述交通参与者可能出现的范围，包括，所述交通参与者和车辆在当前时刻以及接下来的时刻所处位置。
例如，交通参与者从路侧的公交车下车后，位于公交车遮挡激光雷达造成的盲区中；交通参与者从公交车车头方向出现横穿马路，则无人驾驶车辆扫描并识别到所述交通参与者，进行刹车，但是若如交通参与者出现时与无人驾驶车辆的距离已经小于无人驾驶车辆当前时速的最短刹车距离，则造成碰撞事故。
其中，横穿马路的交通参与者速度我们可以规定为5米/秒，即高于普通人群的一般速度以覆盖大多数人的横穿行为。
计算无人驾驶车辆是否与横穿马路的交通参与者存在碰撞风险，无人驾驶车辆与横穿马路的交通参与者的轨迹交汇，无人驾驶车辆到达交汇点的时间＝无人驾驶车辆与交汇点的距离÷无人驾驶车辆速度；交通参与者到达交汇点的时间＝交通参与者与交汇点的距离÷交通参与者速度。由于车道宽度为3.5米，则预设的安全阈值假设规定为1秒，即交通参与者可以在1秒之内越过该车道。若无人驾驶车辆到达交汇点的时间与交通参与者到达交汇点的时间的绝对值之差小于或等于1秒，则存在碰撞风险。
例如，无人驾驶车辆与交汇点的距离为100米，无人驾驶车辆的速度为72千米/小时，即20米/秒，到达交汇点的时间为5秒；而交通参与者从路侧盲区到达交汇点的距离为10米，则到达交汇点的时间为2秒；则无人驾驶车辆到达交汇点的时间与交通参与者到达交汇点的时间的绝对值之差大于1秒，则不存在碰撞风险。
例如，无人驾驶车辆与交汇点的距离为60米，无人驾驶车辆的速度为72千米/小时，即20米/秒，到达交汇点的时间为3秒；而交通参与者从路侧盲区到达交汇点的距离为10米，则到达交汇点的时间为2秒；则无人驾驶车辆到达交汇点的时间与交通参与者到达交汇点的时间的绝对值之差小于1秒，则存在碰撞风险。
在控制模块23的一种优选实现方式中，
根据是否存在碰撞风险，控制无人驾驶车辆的行驶。
若不存在碰撞风险；无人驾驶车辆保持现有航行和速度继续行驶。
若存在碰撞风险，则控制无人驾驶车辆进行减速。
优选地，根据无人驾驶车辆在不同车速下的最短刹车距离，调节无人驾驶车辆的速度，使其最短刹车距离小于无人驾驶车辆与交汇点的距离。这可以绝对保证无人驾驶车辆不会与从路侧盲区出现的交通参与者发生碰撞。
优选地，以预设时间间隔，例如0.1秒，重复执行上述判断和控制步骤；直至无人驾驶车辆驶离所述交汇点。
优选地，若存在多个路侧盲区，则分别计算无人驾驶车辆与从所述路侧盲区中出现的交通参与者的碰撞风险，控制无人驾驶车辆进行减速，以保证无人驾驶车辆不会与所述多个路侧盲区出现的交通参与者发生碰 撞。
采用上述实施例提供的技术方案，能够避免现有技术中无人驾驶车辆只能对探测到的障碍物进行反应，对于从路侧盲区出现的交通参与者只能采取紧急刹车，但无法避免碰撞风险的情况；通过判断路侧盲区出现交通参与者与无人驾驶车辆的碰撞风险，提前控制无人驾驶车辆进行减速，实现了无人驾驶车辆的安全驾驶。
在所述实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详述的部分，可以参见其他实施例的相关描述。
在本申请所提供的几个实施例中，应该理解到，所揭露的方法和装置，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。所述集成的单元既可以采用硬件的形式实现，也可以采用硬件加软件功能单元的形式实现。
图3示出了适于用来实现本发明实施方式的示例性计算机系统/服务器012的框图。图3显示的计算机系统/服务器012仅仅是一个示例，不应对本发明实施例的功能和使用范围带来任何限制。
如图3所示，计算机系统/服务器012以通用计算设备的形式表现。计算机系统/服务器012的组件可以包括但不限于：一个或者多个处理器或者处理单元016，系统存储器028，连接不同系统组件(包括系统存储器028和处理单元016)的总线018。
总线018表示几类总线结构中的一种或多种，包括存储器总线或者存储器控制器，外围总线，图形加速端口，处理器或者使用多种总线结构中的任意总线结构的局域总线。举例来说，这些体系结构包括但不限于工业标准体系结构(ISA)总线，微通道体系结构(MAC)总线，增强型ISA总线、视频电子标准协会(VESA)局域总线以及外围组件互连(PCI)总线。
计算机系统/服务器012典型地包括多种计算机系统可读介质。这些介质可以是任何能够被计算机系统/服务器012访问的可用介质，包括易失性和非易失性介质，可移动的和不可移动的介质。
系统存储器028可以包括易失性存储器形式的计算机系统可读介质，例如随机存取存储器(RAM)030和/或高速缓存存储器032。计算机系统/服务器012可以进一步包括其它可移动/不可移动的、易失性/非易失性计算机系统存储介质。仅作为举例，存储系统034可以用于读写不可移动的、非易失性磁介质(图3未显示，通常称为“硬盘驱动器”)。尽管图3中未示出，可以提供用于对可移动非易失性磁盘(例如“软盘”)读写的磁盘驱动器，以及对可移动非易失性光盘(例如CD-ROM, DVD-ROM或者其它光介质)读写的光盘驱动器。在这些情况下，每个驱动器可以通过一个或者多个数据介质接口与总线018相连。存储器028可以包括至少一个程序产品，该程序产品具有一组(例如至少一个)程序模块，这些程序模块被配置以执行本发明各实施例的功能。
具有一组(至少一个)程序模块042的程序/实用工具040，可以存储在例如存储器028中，这样的程序模块042包括——但不限于——操作系统、一个或者多个应用程序、其它程序模块以及程序数据，这些示例中的每一个或某种组合中可能包括网络环境的实现。程序模块042通常执行本发明所描述的实施例中的功能和/或方法。
计算机系统/服务器012也可以与一个或多个外部设备014(例如键盘、指向设备、显示器024等)通信，在本发明中，计算机系统/服务器012与外部雷达设备进行通信，还可与一个或者多个使得用户能与该计算机系统/服务器012交互的设备通信，和/或与使得该计算机系统/服务器012能与一个或多个其它计算设备进行通信的任何设备(例如网卡，调制解调器等等)通信。这种通信可以通过输入/输出(I/O)接口022进行。并且，计算机系统/服务器012还可以通过网络适配器020与一个或者多个网络(例如局域网(LAN)，广域网(WAN)和/或公共网络，例如因特网)通信。如图3所示，网络适配器020通过总线018与计算机系统/服务器012的其它模块通信。应当明白，尽管图3中未示出，可以结合计算机系统/服务器012使用其它硬件和/或软件模块，包括但不限于：微代码、设备驱动器、冗余处理单元、外部磁盘驱动阵列、RAID系统、磁带驱动器以及数据备份存储系统等。
处理单元016通过运行存储在系统存储器028中的程序，从而执行 本发明所描述的实施例中的功能和/或方法。
上述的计算机程序可以设置于计算机存储介质中，即该计算机存储介质被编码有计算机程序，该程序在被一个或多个计算机执行时，使得一个或多个计算机执行本发明上述实施例中所示的方法流程和/或装置操作。
随着时间、技术的发展，介质含义越来越广泛，计算机程序的传播途径不再受限于有形介质，还可以直接从网络下载等。可以采用一个或多个计算机可读的介质的任意组合。计算机可读介质可以是计算机可读信号介质或者计算机可读存储介质。计算机可读存储介质例如可以是——但不限于——电、磁、光、电磁、红外线、或半导体的系统、装置或器件，或者任意以上的组合。计算机可读存储介质的更具体的例子(非穷举的列表)包括：具有一个或多个导线的电连接、便携式计算机磁盘、硬盘、随机存取存储器(RAM)、只读存储器(ROM)、可擦式可编程只读存储器(EPROM或闪存)、光纤、便携式紧凑磁盘只读存储器(CD-ROM)、光存储器件、磁存储器件、或者上述的任意合适的组合。在本文件中，计算机可读存储介质可以是任何包含或存储程序的有形介质，该程序可以被指令执行系统、装置或者器件使用或者与其结合使用。
计算机可读的信号介质可以包括在基带中或者作为载波一部分传播的数据信号，其中承载了计算机可读的程序代码。这种传播的数据信号可以采用多种形式，包括——但不限于——电磁信号、光信号或上述的任意合适的组合。计算机可读的信号介质还可以是计算机可读存储介质以外的任何计算机可读介质，该计算机可读介质可以发送、传播或者传输用于由指令执行系统、装置或者器件使用或者与其结合使用的程序。
计算机可读介质上包含的程序代码可以用任何适当的介质传输，包括——但不限于——无线、电线、光缆、RF等等，或者上述的任意合适的组合。
可以以一种或多种程序设计语言或其组合来编写用于执行本发明操作的计算机程序代码，所述程序设计语言包括面向对象的程序设计语言—诸如Java、Smalltalk、C++，还包括常规的过程式程序设计语言—诸如“C”语言或类似的程序设计语言。程序代码可以完全地在用户计算机上执行、部分地在用户计算机上执行、作为一个独立的软件包执行、部分在用户计算机上部分在远程计算机上执行、或者完全在远程计算机或服务器上执行。在涉及远程计算机的情形中，远程计算机可以通过任意种类的网络——包括局域网(LAN)或广域网(WAN)连接到用户计算机，或者，可以连接到外部计算机(例如利用因特网服务提供商来通过因特网连接)。
最后应说明的是：以上实施例仅用以说明本申请的技术方案，而非对其限制；尽管参照前述实施例对本申请进行了详细的说明，本领域的普通技术人员应当理解：其依然可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；而这些修改或者替换，并不使相应技术方案的本质脱离本申请各实施例技术方案的精神和范围。

Claims (14)

  1. 一种车辆控制方法，其特征在于，包括：
    获取激光雷达扫描无人驾驶车辆周围的待识别障碍物的信息；
    利用所述待识别障碍物的信息进行障碍物识别；
    基于所述障碍物识别的结果，确定无人驾驶车辆的路侧盲区；
    判断所述无人驾驶车辆与从所述路侧盲区中出现的交通参与者的碰撞风险；
    根据判断结果，控制无人驾驶车辆的行驶。
  2. 根据权利要求1所述的方法，其特征在于，基于所述障碍物识别的结果，确定无人驾驶车辆的路侧盲区包括：
    获取无人驾驶车辆的位置信息和航向信息，以确定所述无人驾驶车辆与所在道路的位置关系；
    基于所述位置信息和所述障碍物识别的结果，确定无人驾驶车辆的盲区；
    基于所述位置关系，确定所述盲区中位于所述无人驾驶车辆所在车道两侧的路侧盲区。
  3. 根据权利要求1所述的方法，其特征在于，在判断所述无人驾驶车辆与从所述路侧盲区中出现的交通参与者的碰撞风险之前，还包括：
    判断当前道路场景是否为潜在碰撞场景，如果是，则继续执行判断所述无人驾驶车辆与从所述路侧盲区中出现的交通参与者的碰撞风险的步骤；
    其中，所述潜在碰撞场景的判断标准包括：存在路侧盲区、所述路侧盲区由大型车辆造成且所述大型车辆位于道路外侧车道。
  4. 根据权利要求1至3任一项所述的方法，其特征在于，利用所述待识别障碍物的信息进行障碍物识别包括：
    利用预设的点云识别模型对所述待识别障碍物信息进行障碍物识别，得到障碍物的类型、尺寸和位置。
  5. 根据权利要求1至3任一项所述的方法，其特征在于，判断所述无人驾驶车辆与从所述路侧盲区中出现的交通参与者的碰撞风险包括：
    确定所述无人驾驶车辆的预测行驶轨迹与从所述路侧盲区中出现的交通参与者的预测轨迹的交汇点；
    若无人驾驶车辆到达所述交汇点的预测时间与所述交通参与者到达所述交汇点的预测时间的绝对值之差小于或等于预设安全阈值，则判断出存在碰撞风险。
  6. 根据权利要求5所述的方法，其特征在于，根据判断结果，控制无人驾驶车辆的行驶包括：
    若存在碰撞风险，则控制无人驾驶车辆进行减速，使所述无人驾驶车辆的最短刹车距离小于所述无人驾驶车辆与所述交汇点的距离。
  7. 一种激光雷达路侧盲区交通参与者避让装置，其特征在于，包括：
    获取模块，用于获取激光雷达扫描无人驾驶车辆周围的待识别障碍物的信息；利用所述待识别障碍物的信息进行障碍物识别；基于所述障碍物识别的结果，确定无人驾驶车辆的路侧盲区；
    判断模块，用于判断所述无人驾驶车辆与从所述路侧盲区中出现的交通参与者的碰撞风险；
    控制模块，用于根据所述判断模块的判断结果，控制无人驾驶车辆的行驶。
  8. 根据权利要求7所述的装置，其特征在于，所述获取模块具体用于：
    获取无人驾驶车辆的位置信息和航向信息，以确定所述无人驾驶车辆与所在道路的位置关系；
    基于所述位置信息和所述障碍物识别的结果，确定无人驾驶车辆的盲区；
    基于所述位置关系，确定所述盲区中位于所述无人驾驶车辆所在车道两侧的路侧盲区。
  9. 根据权利要求7所述的装置，其特征在于，判断模块在判断所述无人驾驶车辆与从所述路侧盲区中出现的交通参与者的碰撞风险之前，还用于判断当前道路场景是否为潜在碰撞场景，如果是，则继续执行判断所述无人驾驶车辆与从所述路侧盲区中出现的交通参与者的碰撞风险的操作；
    其中，所述潜在碰撞场景的判断标准包括：存在路侧盲区、所述路侧盲区由大型车辆造成且所述大型车辆位于道路外侧车道。
  10. 根据权利要求7至9任一项所述的装置，其特征在于，所述获取模块在利用所述待识别障碍物的信息进行障碍物识别时，具体执行：
    利用预设的点云识别模型对所述待识别障碍物信息进行障碍物识别，得到障碍物的类型、尺寸和位置。
  11. 根据权利要求7至9任一项所述的装置，其特征在于，所述判断模块具体用于：
    确定所述无人驾驶车辆的预测行驶轨迹与从所述路侧盲区中出现的交通参与者的预测轨迹的交汇点；
    若无人驾驶车辆到达所述交汇点的预测时间与所述交通参与者到达所述交汇点的预测时间的绝对值之差小于或等于预设安全阈值，则判断出存在碰撞风险。
  12. 根据权利要求11所述的装置，其特征在于，所述控制模块具体用于：
    若存在碰撞风险，则控制无人驾驶车辆进行减速，使所述无人驾驶车辆的最短刹车距离小于所述无人驾驶车辆与所述交汇点的距离
  13. 一种计算机设备，包括存储器、处理器及存储在所述存储器上并可在所述处理器上运行的计算机程序，其特征在于，所述处理器执行所述程序时实现如权利要求1～6中任一项所述的方法。
  14. 一种计算机可读存储介质，其上存储有计算机程序，其特征在于，所述程序被处理器执行时实现如权利要求1～6中任一项所述的方法。
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