WO2024207911A1 - 远程驾驶方法、装置、电子设备、存储介质及程序产品 - Google Patents

远程驾驶方法、装置、电子设备、存储介质及程序产品 Download PDF

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Publication number
WO2024207911A1
WO2024207911A1 PCT/CN2024/080030 CN2024080030W WO2024207911A1 WO 2024207911 A1 WO2024207911 A1 WO 2024207911A1 CN 2024080030 W CN2024080030 W CN 2024080030W WO 2024207911 A1 WO2024207911 A1 WO 2024207911A1
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WO
WIPO (PCT)
Prior art keywords
driving
vehicle
environment
target vehicle
target
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Ceased
Application number
PCT/CN2024/080030
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English (en)
French (fr)
Inventor
张立明
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Tencent Technology Shenzhen Co Ltd
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Tencent Technology Shenzhen Co Ltd
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Publication date
Application filed by Tencent Technology Shenzhen Co Ltd filed Critical Tencent Technology Shenzhen Co Ltd
Priority to EP24783996.2A priority Critical patent/EP4589393A4/en
Publication of WO2024207911A1 publication Critical patent/WO2024207911A1/zh
Priority to US19/171,688 priority patent/US20250231562A1/en
Anticipated expiration legal-status Critical
Ceased legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Program-control systems
    • G05B19/02—Program-control systems electric
    • G05B19/04—Program control other than numerical control, i.e. in sequence controllers or logic controllers
    • G05B19/042—Program control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/20—Control system inputs
    • G05D1/22—Command input arrangements
    • G05D1/221—Remote-control arrangements
    • G05D1/222—Remote-control arrangements operated by humans
    • G05D1/224—Output arrangements on the remote controller, e.g. displays, haptics or speakers
    • G05D1/2244—Optic
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B19/00—Program-control systems
    • G05B19/02—Program-control systems electric
    • G05B19/04—Program control other than numerical control, i.e. in sequence controllers or logic controllers
    • G05B19/042—Program control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
    • G05B19/0423—Input/output
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/20—Control system inputs
    • G05D1/22—Command input arrangements
    • G05D1/221—Remote-control arrangements
    • G05D1/222—Remote-control arrangements operated by humans
    • G05D1/224—Output arrangements on the remote controller, e.g. displays, haptics or speakers
    • G05D1/2244—Optic
    • G05D1/2245—Optic providing the operator with a purely computer-generated representation of the environment of the vehicle, e.g. virtual reality
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/20—Control system inputs
    • G05D1/22—Command input arrangements
    • G05D1/221—Remote-control arrangements
    • G05D1/227—Handing over between remote control and on-board control; Handing over between remote control arrangements
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/60—Intended control result
    • G05D1/69—Coordinated control of the position or course of two or more vehicles
    • G05D1/698—Control allocation
    • G05D1/6987—Control allocation by centralised control off-board any of the vehicles
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
    • G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
    • G08G1/096725—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control where the received information generates an automatic action on the vehicle control
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
    • G08G1/096733—Systems involving transmission of highway information, e.g. weather, speed limits where a selection of the information might take place
    • G08G1/096741—Systems involving transmission of highway information, e.g. weather, speed limits where a selection of the information might take place where the source of the transmitted information selects which information to transmit to each vehicle
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
    • G08G1/096766—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission
    • G08G1/096775—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission where the origin of the information is a central station
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G08G1/164—Centralised systems, e.g. external to vehicles
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/20—Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B2219/00—Program-control systems
    • G05B2219/20—Pc systems
    • G05B2219/23—Pc programming
    • G05B2219/23051—Remote control, enter program remote, detachable programmer
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D2105/00—Specific applications of the controlled vehicles
    • G05D2105/20—Specific applications of the controlled vehicles for transportation
    • G05D2105/22—Specific applications of the controlled vehicles for transportation of humans
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D2107/00—Specific environments of the controlled vehicles
    • G05D2107/10—Outdoor regulated spaces
    • G05D2107/13—Spaces reserved for vehicle traffic, e.g. roads, regulated airspace or regulated waters
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D2109/00—Types of controlled vehicles
    • G05D2109/10—Land vehicles

Definitions

  • the present application relates to technical fields such as cloud technology, smart transportation, autonomous driving, and remote driving.
  • the present application relates to a remote driving method, device, electronic device, storage medium, and program product.
  • Remote driving is a driving technique in which the driving rights are taken over by the backend server, and the staff of the backend server remotely operates in the driving simulation cabin to control the driving of the car.
  • multiple cameras are installed on the moving vehicle to collect video information about the vehicle's surrounding environment, and then transmitted back to the remote driving simulation cabin through the network for display in the driving simulation cabin.
  • the remote driver observes the video information about the vehicle's surrounding environment through the displayed video screen, and then operates the steering wheel, accelerator pedal, etc. in the driving simulation cabin.
  • the remote driver's operation information is transmitted to the moving vehicle through the network through the driving simulation cabin to control the vehicle's driving.
  • An embodiment of the present application provides a remote driving method, which is applied to a remote driving entity, and includes:
  • a second environment image corresponding to the target vehicle is displayed, wherein the second environment image includes an image of at least a portion of the target environment corresponding to the current position of the target vehicle.
  • the method for acquiring the global scene data of the target environment includes:
  • the target environment is three-dimensionally modeled, and the model data of the environment model obtained by modeling is used as the global scene data.
  • An embodiment of the present application provides a remote driving method, which is applied to a server and includes:
  • the current location of the target vehicle is sent to the remote driving entity.
  • the present application also provides a remote driving device, which is applied to a remote driving entity and includes:
  • a first display module is used to display a first environment image corresponding to the target vehicle in response to the remote driving request, wherein the first environment image includes an image of at least a portion of the target environment corresponding to the target vehicle when the target vehicle is in a first position; the first environment image is generated based on local scene data corresponding to the first position in pre-constructed global scene data of the target environment;
  • the second display module is used to display a second environment image corresponding to the target vehicle in response to the driver's vehicle driving operation on the target vehicle, wherein the second environment image includes an image of at least a portion of the target environment corresponding to the current position of the target vehicle.
  • the embodiment of the present application also provides a remote driving device, which is applied to a server and includes:
  • a first sending module is used to send a first position of a target vehicle and local scene data corresponding to the first position to the remote driving entity in response to receiving a remote driving request from the remote driving entity, wherein the local scene data corresponding to the first position is scene data corresponding to at least a part of the target environment when the target vehicle is at the first position;
  • a second sending module configured to send the driving instruction to the target vehicle in response to receiving the driving instruction from the remote driving entity, wherein the driving instruction is based on an instruction corresponding to the vehicle driving operation for the target vehicle in the remote driving entity;
  • the third sending module is used to send the current location of the target vehicle to the remote driving entity in response to receiving the current location of the target vehicle during the driving process based on the driving instruction.
  • An embodiment of the present application provides a remote driving entity, wherein the remote driving entity includes a processor and a display;
  • the display is used to implement a remote driving method as described in any one of the above remote driving methods; and the processor is used to implement a remote driving method as described in any one of the above remote driving methods.
  • An embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the above-mentioned remote driving method.
  • An embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the remote driving method described above is implemented.
  • An embodiment of the present application provides a computer program product, including a computer program, which implements the above-mentioned remote driving method when executed by a processor.
  • FIG1 is a schematic diagram of an implementation environment of a remote driving method provided in an embodiment of the present application.
  • FIG2 is a schematic diagram of the structure of a remote driving entity provided in an embodiment of the present application.
  • FIG3 is a schematic diagram of the structure of a driving simulation cabin provided in an embodiment of the present application.
  • FIG4 is a schematic diagram of a flow chart of a remote driving method provided in an embodiment of the present application.
  • FIG5 is a schematic diagram of a scene of a partial environment model provided in an embodiment of the present application.
  • FIG6 is a schematic diagram of signaling interaction of a remote driving method provided in an embodiment of the present application.
  • FIG7 is a schematic diagram of a flow chart of a remote driving method provided in an embodiment of the present application.
  • FIG8 is a schematic diagram of the structure of a remote driving device provided in an embodiment of the present application.
  • FIG9 is a schematic diagram of the structure of a remote driving device provided in an embodiment of the present application.
  • FIG. 10 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application.
  • any data related to the object such as driver information, driver's driving age, driver's experience level, controlled vehicle associated with the driver, driver's driving operations to control the vehicle, driving route, etc.
  • driver information such as driver information, driver's driving age, driver's experience level, controlled vehicle associated with the driver, driver's driving operations to control the vehicle, driving route, etc.
  • the collection, use and processing of the relevant data need to comply with the relevant laws, regulations and standards of the relevant countries and regions.
  • the above method actually uses the camera to transmit data to the driving simulation cabin in real time, and the bandwidth occupancy is very high. Especially for multiple driving simulation cabins in the same network, it is very easy to cause network congestion, and the stability and real-time performance of video transmission cannot be guaranteed, resulting in poor stability of remote driving and actual driving efficiency.
  • the present application proposes a remote driving method, device, electronic device, storage medium and program product, which can improve the stability of remote driving and the actual driving efficiency.
  • FIG1 is a schematic diagram of an implementation environment of a remote driving method provided by the present application.
  • the implementation environment includes: a remote driving entity 11, a vehicle 12, and a server 13.
  • the server 13 establishes communication with the remote driving entity 11 and the vehicle 12 respectively. Letter connection.
  • the remote driving entity 11 may be a control entity of the remote driving vehicle 12.
  • the driver may perform driving operations on the remote driving entity 11 to control the vehicle 12 to travel.
  • the remote driving entity 11 may send control instructions corresponding to the driver's driving operations to the server 13, and the server 13 sends the control instructions to the vehicle 12 controlled by the remote driving entity 11; the vehicle 12 travels according to the driver's driving operations on the remote driving entity 11 based on the received control instructions.
  • the remote driving entity 11 may be a driving simulation cabin, which may include a display unit 111 , a driver input unit 112 , and a cockpit host 113 .
  • the display unit 111 is used to display the surrounding environment of the vehicle 12 , and the display unit 111 may include any one or more components with display function, such as an electronic display screen, a projector, a curved screen, a folding screen, a multi-faceted screen, etc.
  • the driver input unit 112 can be used to receive the driving operation input by the driver.
  • the driver input unit 112 can be a component in the simulated vehicle 12 that can be operated by the driver.
  • the driver input unit 112 can include but is not limited to: a steering wheel, an accelerator pedal, a brake pedal, etc.
  • the driver input unit 112 can be a virtual component, such as a virtual steering wheel, a virtual accelerator pedal, a virtual brake pedal, etc. with corresponding physical functions displayed on a display screen; it can also be a component with a physical structure, such as a physical steering wheel, a physical accelerator pedal, etc.
  • the cockpit host 113 can be a real machine or a virtual machine that provides certain functions for the remote driving entity 11.
  • the cockpit host 113 can provide at least one of a data transceiver storage function, a data rendering function, or a remote configuration function.
  • the data transceiver storage function can be used to implement the sending of control instructions corresponding to the trigger of the driver, the reception and storage of scene data of the target environment where the vehicle 12 is located, etc.
  • the data rendering function can be used to render the scene data to generate corresponding image rendering data, so that the display unit 111 can display the corresponding image based on the image rendering data.
  • the remote configuration function allows the user to remotely configure the vehicle on the cockpit host 113, such as selecting the vehicle to be remotely driven, starting the vehicle, etc.
  • the remote driving entity 11 can be any physical device that simulates the internal driving environment of the vehicle and has a display function.
  • the remote driving entity 11 can be a driving simulation cabin;
  • FIG3 shows a structural schematic diagram of a possible driving simulation cabin; as shown in FIG3, the driving simulation cabin can be equipped with multiple display screens 301, a steering wheel 302, an accelerator pedal 303, a brake pedal 304 and other entities; of course, it can also be equipped with a seat 305 that simulates the driver's seat of the vehicle. The driver can sit in the seat 305 and operate the steering wheel 302, the accelerator pedal 303, the brake pedal 304, etc. based on the surrounding driving environment of the vehicle displayed on the display screen 301.
  • the remote driving entity 11 may also be other devices with display functions and supporting the driver's driving operations, such as a driving console including a display screen and some specific function buttons, or a computer device with multiple screens or a single screen, a personal computer, a smart phone, an electronic game terminal for simulated driving, etc.
  • the specific function buttons may include but are not limited to: virtual display buttons or physical buttons with the same functions as vehicle driving parts such as a steering wheel, an accelerator pedal, and a brake pedal.
  • a remote driving controller 121 may be installed on the vehicle 12 .
  • the remote driving controller 121 is used to control the vehicle 12 based on the control instructions transmitted by the server 13.
  • the remote driving controller 121 can communicate with the vehicle 12 to obtain the driving information of the vehicle 12, such as the speed, steering wheel steering, and fuel consumption.
  • the remote driving controller 121 also has a positioning function.
  • the remote driving controller 121 can send the real-time positioning information of the vehicle 12 and the driving information such as the speed and fuel consumption to the server 13, so that the server 13 can synchronize this information to the remote driving controller 121 in real time.
  • the implementation environment may also include a base station 14.
  • the base station 14 may be used for real-time communication between the remote driving control 121 and the server 13.
  • the remote driving control 121 sends real-time positioning information, driving information, etc. to the server 13 through the base station 14, and receives control instructions sent by the server 13.
  • the server 13 may be the remote control cloud in FIG1
  • the remote driving entity 11 may be the driving simulation cabin in FIG1
  • the implementation environment may include multiple driving simulation cabins, multiple vehicles equipped with remote driving controllers, a remote control cloud, and a base station.
  • Multiple driving simulation cabins such as driving simulation cabin 1, driving simulation cabin 2, ... driving simulation cabin n can control corresponding controlled vehicles among multiple vehicles through the remote control cloud and the base station.
  • a driving simulation cabin can be associated with one or more controlled vehicles and can control the driving of one or more controlled vehicles at the same time.
  • the vehicle may refer to any form of traveling vehicle with a traveling function.
  • the vehicle may include a two-wheeled vehicle, a four-wheeled car, a three-wheeled motor vehicle, or a vehicle with more wheels. It may also include excavators, unmanned excavators, cranes and other mechanical equipment that support lifting and handling operations. It may also include intelligent mobile machines with a vehicle body movement function, such as intelligent robots, electronic intelligent robot dogs, wheeled composite quadruped robots, movable dual-arm robots, mobile robots used in shopping malls or exhibition halls, etc.
  • This application does not limit the specific type of vehicle, its appearance, mode of movement or driving, etc. This application does not limit the type, quantity, appearance, etc. of the vehicles controlled by the remote driving entity 11.
  • the server 13 may be a remote driving cloud, which can be used for data transmission and reception; it can also store the location information uploaded by each vehicle. Such as high-precision positioning information; it can also store global scene data of the target environment, such as model data of a three-dimensional visualization model of a closed road environment; and it can also be used to send, receive and store driving instructions of the remote driving entity 11.
  • the server 13 may be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server or server cluster that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, and big data and artificial intelligence platforms.
  • the terminal may be a smart phone, tablet computer, laptop computer, digital broadcast receiver, desktop computer, vehicle-mounted terminal (such as vehicle-mounted navigation terminal, vehicle-mounted computer, etc.), smart speaker, smart watch, etc.
  • the terminal and the server may be directly or indirectly connected via wired or wireless communication, and may also be determined based on the actual application scenario requirements, which is not limited here.
  • FIG4 is a flow chart of a remote driving method provided in an embodiment of the present application.
  • the execution subject of the method may be a remote driving entity, which may be any electronic device such as a driving simulation cabin for remote simulated driving, a driving console with display function, a single-screen or multi-screen terminal, or an electronic game terminal for simulated driving.
  • the method includes the following steps.
  • Step 201 In response to a remote driving request, the remote driving entity displays a first environment image corresponding to a target vehicle.
  • the first environment image includes an image of at least a portion of the target environment corresponding to the target vehicle when the target vehicle is in the first position; the first environment image is generated based on local scene data corresponding to the first position in the pre-constructed global scene data of the target environment.
  • the target environment may be the driving environment corresponding to the target vehicle.
  • the target environment may include roads for vehicles to travel, as well as scene elements such as buildings, facilities, traffic signs, traffic lights, trees, lawns, rivers, and mountains.
  • the target environment may be a physical environment in the real world, for example, the target environment may be an industrial environment such as a factory workshop, an industrial park, or an operating environment such as a mining area or a port area, or a special environment area affected by mudslides, or by weather such as heavy rain or blizzards.
  • the target environment may also be a virtual environment, such as a virtual environment that can be constructed using some virtual roads, building settings, and other scene elements during the test phase; for example, a virtual environment area that simulates a mudslide scene, or a test operating environment that simulates a port or mine in bad weather, or a virtual park environment that simulates the operating process in an industrial park.
  • a virtual environment such as a virtual environment that can be constructed using some virtual roads, building settings, and other scene elements during the test phase; for example, a virtual environment area that simulates a mudslide scene, or a test operating environment that simulates a port or mine in bad weather, or a virtual park environment that simulates the operating process in an industrial park.
  • the target environment may be a closed driving environment, which provides an environment in which multiple vehicles including the target vehicle are driving, and the multiple vehicles are all driving under the control of the corresponding remote driving entity; the closed driving environment does not include pedestrians and other vehicles that are not controlled by the remote driving entity.
  • the target environment may be an open driving environment, which includes not only vehicles driving under the control of the remote driving entity, but also some pedestrians, traditional vehicles that are not controlled by the remote driving entity, bicycles, etc.
  • the global scene data of the target environment is used to display the scene picture based on the target environment.
  • the global scene data of the target environment carries the scene elements in the target environment.
  • the scene elements refer to the elements that constitute the scene in the target environment, such as roads, traffic signs, traffic lights, etc.
  • the scene elements in the target environment may include static scene elements and dynamic scene elements.
  • static scene elements refer to scene elements that are static and unchanged during the update cycle of the target environment, and may include but are not limited to: roads, traffic signs, buildings on both sides of the road, walls, lawns, trees, etc.
  • Dynamic scene elements refer to scene elements whose states are changeable during the update cycle of the target environment, and may include but are not limited to: traffic lights, building clock towers, etc.
  • the global scene data includes scene data corresponding to each scene element in the target environment.
  • the scene data includes, but is not limited to, data such as the shape, color, and position coordinates of the scene elements.
  • the scene data corresponding to the road may include the shape, position, color of each position point on the road surface, shape and color of the traffic lines on the road surface, etc.
  • the global scene data may include each position point in the target environment and rendering data corresponding to each position point.
  • the position point may be each position coordinate covered by the target environment, and the rendering data may include RGB data, brightness data, etc. corresponding to the position coordinate.
  • the global scene data of the target environment may be pre-built and stored.
  • the method for obtaining the global scene data may include the following steps A1-A2:
  • Step A1 pre-scanning the target environment by a target scanning device to obtain point cloud data of the target environment
  • Step A2 Based on the point cloud data obtained by the scan, three-dimensional modeling is performed on the target environment, and the model data of the environment model obtained by the modeling is used as the global scene data.
  • the target scanning device may include a laser radar device, which can control the laser radar device to move in the target environment, and scan and obtain point cloud data of various environmental positions in the target environment during the movement.
  • the point cloud data may include the position coordinates of multiple key position points of the target environment, as well as the color information, reflection intensity information, etc. of each position point, and then three-dimensional modeling is performed based on the point cloud data.
  • the laser radar device can be mounted on a drivable vehicle or a flying drone to complete the scanning of various position points in the target environment.
  • the target scanning device may also include an image acquisition device, such as a 3D camera; if the point cloud data only includes the position coordinates of key position points, the color, light intensity and other image data of each position point can also be obtained by scanning with a 3D camera, and three-dimensional modeling can be performed using the point cloud data scanned by the lidar device and the data scanned by the 3D camera.
  • an image acquisition device such as a 3D camera
  • the position and position coordinates in the point cloud data or global scene data may be position coordinates in a world coordinate system.
  • the world coordinate system may be WGS84 (world geodetic system).
  • the above steps A1-A2 can be performed by other devices, such as pre-building global scene data by a special environmental monitoring device, and sending the pre-built global scene data to the server before the remote driving entity starts remote driving, and the server stores the global scene data.
  • the above steps A1-A2 can also be performed directly by the server, such as establishing a communication connection between the server and the target scanning device. Before starting remote driving, the server uses the communication connection to obtain point cloud data, and constructs and stores the global scene data through step A2. This application does not limit who performs the above steps A1-A2.
  • the remote driving entity when it starts driving the target vehicle, it can obtain the global scene data or the local scene data of at least part of the environment corresponding to the location of the target vehicle from the server.
  • the server can update the global scene data periodically, and the server can update the global scene data of the target environment according to the update cycle corresponding to the target environment.
  • the target environment carried in the global scene data can be a relatively fixed environment that does not change within the update cycle. For example, if the update cycle is 1 day, the scene elements in the target environment can be scene elements that do not change within a day, such as temporary roadblocks, vegetation, etc. If the environment changes frequently, such as a construction site, the update frequency of the global scene data needs to be increased.
  • the range of static objects included in the environment will also change.
  • the building structure under construction, the pile of building materials, etc. can be regarded as dynamic elements, and the elements that do not change within the update cycle, such as temporary fences and brackets, can be considered as relatively static elements within the update cycle.
  • FIG5 shows a partial environment model corresponding to the target environment, in which static elements such as lawns, roads, building bodies, walls, etc. in the target environment are restored.
  • this application only uses the above-mentioned three-dimensional modeling using point cloud data as an example to illustrate the process of obtaining global scene data.
  • other data can also be obtained for three-dimensional modeling, or the environmental model data of two-dimensional modeling or four-dimensional modeling can also be used as global scene data. This application does not limit this.
  • the remote driving entity may start remote driving based on a trigger operation of the driver.
  • the remote driving entity may display multiple candidate vehicles, from which the driver selects a target vehicle to start remote driving of the target vehicle.
  • the server assigns a target vehicle to the driver, and the remote driving entity starts remote driving of the assigned vehicle. Accordingly, the implementation method of starting remote driving of the target vehicle may include the following two implementation methods: method 1 and method 2:
  • Method 1 In response to a first driving trigger operation, the remote driving entity displays a remote configuration page and receives a selection operation for a target vehicle from at least one candidate vehicle.
  • the remote configuration page displays the vehicle information of the at least one candidate vehicle;
  • the remote driving request is a first driving request triggered by a selection operation.
  • the first driving trigger operation is an operation that triggers the configuration of remote driving.
  • the first driving trigger operation includes but is not limited to: a start operation on the remote driving platform, a trigger operation on the configuration button on the platform page, etc.
  • the remote configuration page may display the selection controls corresponding to each candidate vehicle, and the driver may trigger the selection control corresponding to the target vehicle based on the vehicle information of each candidate vehicle displayed on the page; the remote configuration page may also pop up a prompt page prompting whether to start, and the driver may start the remote driving of the target vehicle by triggering the start driving control in the prompt page.
  • the start driving process can also be directly triggered when the trigger operation on the selection control is detected.
  • the implementation method of step 201 may include: when the remote driving entity detects the selection operation of the target vehicle in the remote configuration page, triggering a first driving request; in response to the first driving request, displaying the first environment image.
  • the remote driving entity may send the first driving request to the server, the first driving request is used to request remote driving of the target vehicle, and the first driving request may carry the vehicle identification information of the target vehicle.
  • Method 2 The remote driving entity responds to the second driving trigger operation, displays the information entry page, and receives the driver information entry operation triggered by the entry control.
  • the server can match the global candidate vehicles based on the driver's information, obtain multiple matching vehicles that match the driver's information, and provide the remote driving entity with information about the multiple matching vehicles.
  • the remote driving entity can display the vehicle information of the multiple matching vehicles provided by the server, such as information about multiple vehicles that match the driver's license, driving experience level, etc.
  • the driver can also select a target vehicle from the multiple matching vehicles.
  • the remote driving entity detects the driver's selection operation of the target vehicle from the multiple matching vehicles and sends a remote driving request for the target vehicle to the server.
  • the second driving request is used to request the allocation of a remotely driven vehicle.
  • the implementation method of step 201 may include: when the remote driving entity detects an input operation in the information input page, a second driving request is triggered based on the input driver information; in response to the second driving request, the second environment image is displayed.
  • the remote driving entity may send the second driving request to the server; for example, the second driving request may carry current login information, such as driver ID, driver's license ID, login name, login account, etc., so that the server obtains the associated driver information based on the current login information; for another example, the second driving request may carry driver information.
  • the first position may be the initial position of the target vehicle when remote driving is started.
  • the initial position may be the end position of the target vehicle in the most recent historical driving process, or it may be a pre-configured default starting position.
  • the first position may also be the position of the target vehicle during driving after remote driving has been started; for example, after remote driving is started, the remote driving entity may update the displayed environment image according to a pre-configured period, and the first environment image may be the environment image corresponding to the previous period.
  • step 201 in response to a first driving request or a second driving request, the remote driving entity receives at least one of global scene data or first scene data of a target environment from a server, where the first scene data is local scene data of at least a portion of the environment corresponding to the first position; the remote driving entity displays a first environment image based on the received global scene data or first scene data.
  • the first environment image may include an image of at least a portion of the environment corresponding to the first position.
  • the at least portion of the environment corresponding to the first position includes: a target environment corresponding to the first position, or at least one item of a portion of the environment corresponding to the target environment of the first position. That is, the first environment image may display global scene elements of the target environment corresponding to the first position, or local scene elements of at least a portion of the environment.
  • the remote driving entity may render image rendering data of scene elements in at least part of the environment based on the first scene data, and display an image of at least part of the environment corresponding to the first position based on the image rendering data.
  • the remote driving entity may also render image rendering data of global scene elements in the target environment where the first position is located based on the global scene data, and display the first environment image based on the image rendering data.
  • the remote driving entity may display an image of at least part of the environment from the perspective of the target vehicle. From the perspective of the target vehicle, it means at least part of the surrounding environment seen from the perspective of the target vehicle; for example, the surrounding environment of the target vehicle is the area within sight from the first position.
  • the various environmental elements in the first environmental image may be arranged according to certain rules based on the relative position with the target vehicle, such as the rule of large near and small far.
  • the at least partial environment may be an environment within a certain area obtained based on the first position; for example, the at least partial environment corresponding to the first position may include the surrounding area of the first position, such as a spatial area within a preset distance range centered on the first position in the target environment, such as an environmental area within 10 meters, 30 meters, or 100 meters of the target vehicle.
  • the at least partial environment may be an environment within a specified angle range, such as the environment in front of the target vehicle, the surrounding environment on the left and right sides, etc. The surrounding environment or the surrounding environment within a specified 270° range centered on the target vehicle, or the surrounding 360° panoramic environment, etc.
  • the remote driving entity may also display the state data of surrounding vehicles, weather, lighting, etc. in the first environment image. Accordingly, the implementation of step 201 includes at least one of the following methods 1 to 4:
  • Method 1 In response to the first driving request or the second driving request, display the first image.
  • the first environment image can be a first image.
  • the first image includes at least part of the environment corresponding to the first position, and various surrounding vehicles of the target vehicle.
  • the first image displays scene elements such as roads, buildings, and traffic signs in the surrounding environment, and can also display surrounding vehicles in the surrounding environment, such as parked surrounding vehicles or surrounding vehicles in motion, etc.
  • the actual shape, color, license plate, vehicle model, driving status and other information of the surrounding vehicles can be restored and displayed in the first image, and the driving status can be such as the rear lights flashing, about to turn, slowing down, about to stop, etc.
  • step 201 may include: in response to the first driving request or the second driving request, the remote driving entity receives the position information of each vehicle corresponding to the target environment from the server, and determines each surrounding vehicle of the target vehicle based on the position information of each vehicle; and displays the first image based on at least one of the global scene data or the first scene data, and based on the position information of each surrounding vehicle.
  • Each surrounding vehicle is displayed at a corresponding position in the first image, and each vehicle corresponding to the target environment may be a vehicle in the target environment, and may include the target vehicle and each surrounding vehicle of the target vehicle.
  • the remote driving entity can display the surrounding environment of the target vehicle in the second image and the surrounding vehicles at the corresponding positions of the corresponding surrounding environment based on the position information of each vehicle including the target vehicle.
  • the target vehicle can send its own position information to the remote driving entity, for example, by positioning through the driving controller installed on the target vehicle, and sending the position information of the target vehicle to the server, and the server synchronizes the position information of the target vehicle to the remote driving entity.
  • the same method as the target vehicle can be adopted, and each other vehicle can send its own position information to other corresponding remote driving entities; the remote driving entity can obtain the position information of other vehicles from other remote driving entities corresponding to each other vehicle.
  • Method 2 In response to the first driving request or the second driving request, display the second image.
  • the first environment image may be a second image, wherein the second image includes at least a portion of the environment corresponding to the first position, and information about each surrounding vehicle and relative positions between each surrounding vehicle and the target vehicle.
  • step 201 may include: in response to the first driving request or the second driving request, the remote driving entity receives the driving status and position information of each vehicle corresponding to the target environment from the server, and determines the relative position information and relative driving status of each surrounding vehicle and the target vehicle based on the position information and driving status of each vehicle. And based on at least one of the global scene data or the first scene data, and based on the relative position information and relative driving status of each surrounding vehicle and the target vehicle, displays the second image.
  • the remote driving entity may also display the relative position information and relative driving status between the target vehicle and each surrounding vehicle in the second image.
  • the relative distance between the target vehicle and the surrounding vehicles is marked in the second image, such as the target vehicle is 10 meters away from the front vehicle and 20 meters away from the rear vehicle, etc. It may also be marked with the relative driving status of the surrounding vehicles relative to the target vehicle, whether the speed of the surrounding vehicles is slower or faster, whether the surrounding vehicles are about to turn or stop, etc.
  • Method three in response to the second driving request, display a third image.
  • the first environment image may be a third image, wherein the third image includes at least a portion of the environment corresponding to the first position and vehicle information of the target vehicle assigned by the server.
  • step 201 may include: in response to the second driving request, the remote driving entity receives the vehicle information of the assigned target vehicle from the server; and based on at least one of the global scene data or the first scene data, and based on the vehicle information of the target vehicle, displays a third image.
  • the remote driving entity may also display the vehicle information of the assigned target vehicle in the third image so that the driver can promptly understand the situation of the remotely operated vehicle.
  • Mode 4 In response to the first driving request or the second driving request, display a fourth image.
  • the first environment image may be a fourth image.
  • the fourth image includes at least a portion of the environment corresponding to the first position and state data of the target environment, wherein the state data includes at least one of meteorological data of at least a portion of the environment corresponding to the first position, light intensity, or a current state of a state-variable object in the target environment.
  • step 201 may include: in response to the first driving request or the second driving request, the remote driving entity receives status data of the target environment from the server, the status data including at least one of the meteorological data of the first location, the light intensity, or the current state of a state-variable first object in the target environment; the remote driving entity displays a fourth image based on at least one of the global scene data or the first scene data, and based on the status data of the target environment.
  • the state-variable object may include dynamic elements in the target environment, such as a traffic light, a building tower clock, etc.; for example, the current state of the traffic light is whether the currently indicated traffic light is a red light, a green light, or a yellow light.
  • Step 202 In response to the driver's vehicle driving operation on the target vehicle, the remote driving entity displays a second environment image corresponding to the target vehicle.
  • the second environment image includes an image of at least a portion of the target environment corresponding to the current location of the target vehicle.
  • the vehicle driving operation may be a driving operation of the driver controlling the driving of the target vehicle on the remote driving entity. For example, the turning operation of the steering wheel in the driving simulation cabin, the stepping operation of the brake pedal or the accelerator pedal, etc.
  • the remote driving entity may obtain the second scene data of at least a part of the environment corresponding to the current position in the target environment based on the current position; the remote driving entity may render the image rendering data corresponding to the current position based on the global scene data or the second scene data, and display the second environment image on the display screen based on the obtained image rendering data.
  • the second environmental image may also include, but is not limited to, at least one of the following: surrounding vehicles, the relative positions between the surrounding vehicles and the target vehicle, and status data corresponding to the environmental position at the next moment; accordingly, the implementation method of displaying at least one item of information in the second environmental image is the same process as the corresponding method in method one, method two or method four in the above step 201, and will not be repeated here.
  • the remote driving entity can also predict the driving condition of the target vehicle and display the predicted driving condition to the driver.
  • the remote driving entity can display the current driving condition and the predicted driving condition in split screens.
  • the remote driving entity includes at least a first split screen and a second split screen; accordingly, the process of displaying the second environment image in step 202 may include: displaying the second environment image in the first split screen.
  • the prediction and display process of the prediction situation may be implemented by the following steps B1-B2:
  • Step B1 The remote driving entity predicts the environmental position of the target vehicle at the next moment based on the current position and the driving state of the target vehicle;
  • Step B2 The remote driving entity displays a third environment image corresponding to the environment position of the target vehicle at the next moment in the second split screen.
  • the driving state may include the driving speed and driving direction of the target vehicle.
  • the remote driving entity may predict the environmental position to which the target vehicle will travel at the next moment based on the current position, driving speed point and driving direction.
  • the remote driving entity may obtain the third scene data of at least part of the environment corresponding to the environmental position at the next moment based on the environmental position at the next moment; the remote driving entity may render the image rendering data corresponding to the environmental position at the next moment based on the global scene data or the third scene data, and display the third environmental image in the second split screen based on the obtained image rendering data.
  • the third environmental image may also include but is not limited to at least one of the following: surrounding vehicles, the relative position between the surrounding vehicles and the target vehicle, and the state data corresponding to the environmental position at the next moment; this process is the same as the display process of the first environmental image in the above step 201, and will not be repeated here.
  • first split screen and the second split screen may be different screen display areas in a physical screen, or may be two independent physical display screens, and this application does not limit this.
  • the remote driving entity may predict the location of the target vehicle in advance before obtaining the location information sent from the target vehicle to generate image rendering data used when displaying the environment image in advance. Based on this, the current location obtained from the target vehicle can be used to verify the predicted location, so as to be displayed in combination with the verification result.
  • step 202 the process of predicting the position in advance and generating the image rendering data corresponding to the predicted position in advance can be implemented by the following steps C1 to C3:
  • Step C1 based on the target environment and the acquired traveling state information of the target vehicle, predict the current position of the target vehicle to obtain a predicted position;
  • Step C2 based on the predicted position, obtaining local scene data corresponding to the predicted position in the global scene data, and obtaining state data of at least a portion of the environment corresponding to the predicted position;
  • Step C3 Based on the local scene data and state data corresponding to the predicted position, rendering is performed to obtain rendered image data corresponding to the predicted position.
  • the travel status information may include information such as the speed, direction, and position reached by the target vehicle during the travel process.
  • the speed, direction, and historical position of the target vehicle at at least one historical moment may be obtained, and the position that can be reached at the current moment may be predicted to obtain the predicted position.
  • the speed, direction, and position reached at every 1 second within 5 seconds before the current moment may be predicted at the 11th second, that is, the position at the 11th second after the current moment.
  • the driving state information may also include at least one of the following: fuel consumption, power, driving trajectory, and the corresponding route to be driven in the designated operation route of the target vehicle during driving.
  • the remote driving entity may also combine the at least one information with the speed, direction, and other information to obtain the predicted position.
  • the remote driving entity may use a pre-configured target algorithm or neural network to obtain the predicted position. The position of the target vehicle is predicted through the network model.
  • the remote driving entity may obtain local scene data of at least part of the environment corresponding to the predicted position from the global scene data based on the predicted position.
  • the remote driving entity may also obtain surrounding vehicles of the target vehicle based on the predicted position; for another example, the remote driving entity may also obtain relative position information, relative driving status, and other information between the surrounding vehicles and the target vehicle based on the predicted position.
  • the remote driving entity may render rendered image data corresponding to the predicted position, that is, image rendering data, based on the obtained local scene data, surrounding vehicles, and relative position information and relative driving status between the surrounding vehicles and the target vehicle.
  • the remote driving entity can verify the predicted position based on the actual transmitted position to display the image using the rendering data generated in advance when feasible.
  • step 202 may include the following two situations:
  • Case 1 if the predicted position matches the current position obtained from the target vehicle, the second environment image is displayed based on the rendered image data corresponding to the predicted position;
  • Case 2 If the current environment position does not match the current position obtained from the target vehicle, obtain the local scene data and status data corresponding to the current position, and render the second environment image based on the local scene data and status data corresponding to the current position.
  • the image rendering data generated in advance can be directly used to display the second environment image.
  • the verification is not passed, that is, the predicted position does not match the actual position
  • the actual current position is used as the basis, and the second environment image is rendered based on the local scene data and state data corresponding to the current position.
  • an associated working vehicle having an associated working relationship with the target vehicle is traveling in the target environment; the remote driving entity can also display the collaborative working situation of the target vehicle and the associated working vehicle based on the position information of the associated working vehicle.
  • the present application further comprises the following steps D:
  • Step D the remote driving entity displays driving assistance information
  • the driving assistance information includes at least one of the following:
  • Relative operating condition information between the associated operating vehicle and the target vehicle is Relative operating condition information between the associated operating vehicle and the target vehicle.
  • the driving assistance information is information used to assist the target vehicle and the associated working vehicle in performing collaborative work.
  • the remote driving entity may display the driving assistance information in the second target image, for example, a driving assistance card may be superimposed at a corresponding position above the second target image to display the driving assistance information in the driving assistance card.
  • the remote driving entity may also display the driving assistance information in a separate page, for example, the remote driving entity may display a fourth environment image, and the driving assistance information is displayed in the fourth environment image.
  • the remote driving entity may also present the driving assistance information in an environment map, for example, the relative position information between the target vehicle and the associated work vehicle is displayed in a global map or a local map of the target environment.
  • the relative position information may include, but is not limited to: the positions of the target vehicle and the associated working vehicle respectively displayed in comparison on a global or local map, the relative distance between the target vehicle and the associated working vehicle, the relative traveled route between the target vehicle and the associated working vehicle, etc.
  • the remote driving entity may obtain the relative position information based on the position information of the target vehicle and the associated working vehicle respectively.
  • the position coordinates in the position information may be coordinates in a world coordinate system, such as the WGS84 world geodetic coordinate system.
  • each vehicle may be a vehicle that supports the networking function, which refers to the function of the vehicle communicating with the remote driving entity through a mobile communication network.
  • Mobile communication networks include but are not limited to: 4G, 5G, Cellular-V2X (C-V2X), Dedicated Short Range Communications (DSRC), etc.
  • V2X communication can be used to support the communication between each vehicle and the remote driving entity in the cloud to achieve remote control driving.
  • the location information transmitted by each vehicle through the network is structured data that complies with the target communication protocol standard, for example, structured data that complies with the 5G-V2X communication protocol standard.
  • the vehicle side only needs to send the positioning information to the remote driving entity, such as the driving simulation cabin, through the network; since the positioning information is structured data and occupies a small bandwidth of less than 0.1KByte, the remote driving entity can render the vehicle and the surrounding environment information of the vehicle based on the positioning information with a very small amount of data and the pre-built global scene data, which greatly reduces the demand for network bandwidth and can provide multi-perspective environmental information, which can help the remote driver to control the vehicle stably, accurately and with low latency.
  • the remote driving entity such as the driving simulation cabin
  • the server may pre-acquire and store other environments and global scene data pre-built based on other environments.
  • the server may adopt the process of steps A1-A2 above to acquire the global scene data of the corresponding environment. No further details are given here.
  • the remote driving entity may send a first driving request or a second driving request to the server, and the process is similar to the above process.
  • the remote driving request triggering process in steps 201 and 202 is similar and will not be described again here.
  • Step 302 In response to receiving a remote driving request from a remote driving entity, the server sends a first position of a target vehicle and local scene data corresponding to the first position to the remote driving entity.
  • the local scene data corresponding to the first position is scene data corresponding to at least a portion of the target environment when the target vehicle is at the first position.
  • the target vehicle transmits its location information to the server in real time through the base station through the installed remote driving controller, and of course, it can also transmit driving status such as speed, direction, and posture.
  • the server can synchronize with the remote driving entity in real time based on the information sent by the target vehicle.
  • Step 303 The remote driving entity receives the first position and the local scene data corresponding to the first position, and displays a first environment image corresponding to the target vehicle.
  • Step 304 In response to detecting the driver's driving operation on the target vehicle, the remote driving entity sends a driving instruction to the server.
  • the driving instruction is based on an instruction corresponding to a vehicle driving operation for a target vehicle in a remote driving entity.
  • Step 305 In response to receiving the driving instruction from the remote driving entity, the server sends the driving instruction to the target vehicle.
  • the target vehicle can travel based on the vehicle driving operation indicated by the driving instruction and send its location in real time during the driving process.
  • the remote driving controller in the target vehicle can receive the driving instructions sent by the server through the base station.
  • the remote driving controller can communicate with the vehicle's control system in real time through the vehicle's CAN (Controller Area Network).
  • the remote driving controller can obtain information such as the vehicle speed, steering wheel angle, fuel consumption, etc. during driving through the CAN bus;
  • the remote driving controller has a positioning function, which can be used to locate the position of the vehicle in real time;
  • the remote driving controller can send the vehicle's real-time position and information such as the vehicle speed, steering wheel angle, fuel consumption, etc. during driving to the server in real time.
  • the remote driving controller can communicate in real time with the vehicle's control system through the vehicle's CAN (Controller Area Network).
  • the remote driving controller can communicate with the vehicle's ECU (Electronic Control Unit), VCU (Vehicle Control Unit), or MCU (Microcontroller Unit) through the CAN bus to control the vehicle's deceleration, acceleration, turning, parking, etc. during driving, so as to realize the process of driving according to driving instructions.
  • ECU Electronic Control Unit
  • VCU Vehicle Control Unit
  • MCU Microcontroller Unit
  • Step 306 In response to receiving the current location of the target vehicle sent during the driving process based on the driving instruction, send the current location of the target vehicle to the remote driving entity.
  • Step 307 In response to receiving the current location of the target vehicle, the remote driving entity displays a second environment image.
  • the target vehicle can provide real-time feedback of the target vehicle's location information to the server at a certain period during the driving process based on the driving instruction. This process can be achieved based on the remote driving controller installed on the target vehicle.
  • the target vehicle can also send information such as speed, direction, posture, driving status, etc. during the driving process to the server.
  • the server synchronizes the current location of the target vehicle to the remote driving entity in real time, so that the remote driving entity can display the second environment image in time.
  • the entire process of the remote driving may include the following steps:
  • the 3D modeling in this application is performed in a relatively fixed environment.
  • the fixed environment in this application refers to environmental equipment that does not change within a day, such as temporary roadblocks. If the environment changes frequently, such as a construction site, the 3D modeling update frequency needs to be increased, and accordingly, the range of objects included in the fixed environment will also change.
  • All controlled vehicles upload real-time information to the remote control cloud through the base station, including positioning status, position, speed, posture, driving status, etc.
  • the remote control cloud receives and stores the real-time information of all controlled vehicles in association with the controlled vehicle ID.
  • the remote driver can view the static information of all controlled vehicles through the remote configuration function of the cockpit host, select the target controlled vehicle and start remote driving. Another solution is that when there are multiple remote drivers, the system will select the controlled vehicle based on the input of the driver.
  • the driver's license level and the required driver's license information of the controlled vehicle stored in the system automatically allocate the vehicle that the driver needs to remotely control, and provide static information associated with the assigned vehicle ID to the driver's cockpit host.
  • the remote configuration function starts the data sending, receiving and storing function, sends a command to the remote control cloud to request the real-time information of all controlled vehicles and the three-dimensional model information near the target controlled vehicle, and waits for the remote control cloud to feedback.
  • the data receiving and sending storage function continuously receives the real-time information of all controlled vehicles and the three-dimensional model near the target controlled vehicle.
  • the remote configuration function starts the data rendering function, and renders the environmental information near the target vehicle and other vehicle information in real time.
  • the data rendering perspective can be adjusted as needed, including the following perspective, the driver perspective, the bird's-eye view, the own perspective, etc.
  • the "other vehicle information" here refers to other controlled vehicles.
  • the rendering of these vehicles is based on the static information and real-time information of these vehicles (such as the size, color, position, direction, etc. of the vehicle) stored in the remote control cloud. Therefore, the perception information directly from the controlled vehicle is avoided, saving communication bandwidth.
  • the driver operates through the driver input unit, and the data transceiver storage function receives the operation information, stores it and sends it to the remote control cloud.
  • the remote control cloud sends the relevant instructions to the remote driving controller of the target controlled vehicle.
  • the remote driving controller of the controlled vehicle receives remote control cloud instructions and controls the controlled vehicle to perform response actions according to the instructions.
  • the remote pilot can turn off and stop remote control through the remote configuration function of the cockpit host.
  • the remote driving method of the present application relates to technical fields such as cloud technology, smart transportation, autonomous driving, and remote driving.
  • cloud storage technology in cloud technology can be used to create logical volumes to achieve structured storage of global scene data in various environments.
  • the remote driving method of the present application can be used in transportation systems such as intelligent transportation systems and intelligent vehicle-road cooperative systems.
  • ITS Intelligent Traffic System
  • Intelligent Transportation System is the effective and comprehensive application of advanced science and technology (information technology, computer technology, data communication technology, sensor technology, electronic control technology, automatic control theory, operations research, artificial intelligence, etc.) to transportation, service control and vehicle manufacturing, strengthening the connection between vehicles, roads and users, thus forming a comprehensive transportation system that ensures safety, improves efficiency, improves the environment and saves energy. Or;
  • the Intelligent Vehicle Infrastructure Cooperative System referred to as the vehicle-infrastructure cooperative system
  • ITS intelligent transportation system
  • the vehicle-infrastructure cooperative system adopts advanced wireless communication and new generation Internet technologies to implement all-round dynamic real-time information interaction between vehicles and roads, and carries out active vehicle safety control and road cooperative management based on the collection and integration of dynamic traffic information in all time and space, fully realizing the effective coordination of people, vehicles and roads, ensuring traffic safety, and improving traffic efficiency, thus forming a safe, efficient and environmentally friendly road traffic system.
  • cloud computing is a computing model that distributes computing tasks on a resource pool composed of a large number of computers, so that various application systems can obtain computing power, storage space and information services as needed.
  • the network that provides resources is called “cloud”.
  • the resources in the “cloud” are infinitely expandable in the eyes of users, and can be obtained at any time, used on demand, expanded at any time, and paid for by use.
  • the PaaS (Platform as a Service) layer can be deployed on the IaaS (Infrastructure as a Service) layer, and the SaaS (Software as a Service) layer can be deployed on the PaaS layer. SaaS can also be deployed directly on IaaS.
  • PaaS is a platform for software operation, such as databases, web containers, etc.
  • SaaS is a variety of business software, such as web portals, SMS mass senders, etc.
  • SaaS and PaaS are upper layers relative to IaaS.
  • Cloud storage is a new concept extended and developed from the concept of cloud computing.
  • a distributed cloud storage system (hereinafter referred to as storage system) refers to a storage system that uses cluster applications, grid technology, and distributed storage file systems to bring together a large number of different types of storage devices (storage devices are also called storage nodes) in the network through application software or application interfaces to work together and provide external data storage and business access functions.
  • the storage method of the storage system is: create a logical volume.
  • create a logical volume physical storage space is allocated to each logical volume.
  • the physical storage space may be composed of disks of a storage device or several storage devices.
  • the client stores data on a logical volume, that is, the data is stored on the file system.
  • the file system divides the data into many parts, each of which is an object.
  • the object contains not only data but also additional information such as data identification (ID, ID entity).
  • ID data identification
  • the file system writes each object to the physical storage space of the logical volume, and the file system records the storage location information of each object.
  • the file system can allow the client to access the data according to the storage location information of each object.
  • the storage system allocates physical storage space to logical volumes. Specifically, the physical storage space is divided into stripes in advance according to the estimated capacity of the objects stored in the logical volume (this estimate is often relatively large compared to the actual capacity of the objects to be stored) and the group of independent redundant array of independent disks (RAID).
  • a logical volume can be understood as a The physical storage space is allocated to the logical volume by dividing it into stripes.
  • FIG8 is a schematic diagram of the structure of a remote driving device provided in an embodiment of the present application.
  • the device is applied to a remote driving entity, as shown in FIG8 , and the device includes:
  • the first display module 801 is used to display a first environment image corresponding to the target vehicle in response to the remote driving request, wherein the first environment image includes an image of at least a portion of the target environment corresponding to the target vehicle when the target vehicle is in the first position; the first environment image is generated based on local scene data corresponding to the first position in the pre-constructed global scene data of the target environment;
  • the second display module 802 is used to display a second environment image corresponding to the target vehicle in response to the driver's vehicle driving operation on the target vehicle, and the second environment image includes an image of at least a portion of the target environment corresponding to the current location of the target vehicle.
  • the device further includes a global scene data acquisition module, and the global scene data acquisition module is used to:
  • the target environment is three-dimensionally modeled, and the model data of the environment model obtained by modeling is used as the global scene data.
  • the device further includes at least one of the following:
  • a third display module is used to display a remote configuration page in response to the first driving trigger operation, and receive a selection operation for a target vehicle from at least one candidate vehicle; the remote configuration page displays vehicle information of the at least one candidate vehicle; and the remote driving request is a first driving request triggered by the selection operation;
  • the fourth display module is used to display an information entry page in response to a second driving trigger operation, and receive a driver information entry operation triggered by an entry control; the information entry page displays an entry control for entering driver information; and the remote driving request is a second driving request triggered by the driver information entry operation.
  • the first display module is used for at least one of the following:
  • the first driving request In response to the first driving request or the second driving request, displaying a first image, the first image including at least a portion of the environment corresponding to the first position and each surrounding vehicle of the target vehicle;
  • the second driving request In response to the first driving request or the second driving request, displaying a second image, the second image including at least a portion of the environment corresponding to the first position, and each surrounding vehicle and relative position information between each surrounding vehicle and the target vehicle;
  • a fourth image is displayed, which includes at least a portion of the environment corresponding to the first position, and status data of the target environment, wherein the status data includes at least one of meteorological data of at least a portion of the environment corresponding to the first position, light intensity, or a current state of a state-variable object in the target environment.
  • the first display module in response to the remote driving request, before displaying the first environment image, is further used for at least one of the following:
  • state data of the target environment is received from the server, the state data including at least one of weather data of the first location, light intensity, or a current state of a state-variable first object in the target environment.
  • the remote driving entity includes at least a first split screen and a second split screen
  • the second display module is used for:
  • the device also includes:
  • a first prediction module used to predict the environmental position of the target vehicle at the next moment based on the current position and the driving state of the target vehicle;
  • the fifth display module is used to display, in the second split screen, a third environment image corresponding to the environment position of the target vehicle at the next moment.
  • the device further includes:
  • a second prediction module is used to predict the current position of the target vehicle based on the target environment and the acquired traveling state information of the target vehicle to obtain a predicted position;
  • An acquisition module configured to acquire, based on the predicted position, local scene data corresponding to the predicted position in the global scene data, and acquire state data of at least a portion of the environment corresponding to the predicted position;
  • a rendering module is used to render rendered image data corresponding to the predicted position based on the local scene data and state data corresponding to the predicted position.
  • the second display module is used to:
  • the predicted position matches the current position obtained from the target vehicle, displaying the second environment image based on the rendered image data corresponding to the predicted position;
  • the current environment position does not match the current position obtained from the target vehicle, local scene data and status data corresponding to the current position are obtained, and the second environment image is rendered based on the local scene data and status data corresponding to the current position.
  • an associated operation vehicle having an associated operation relationship with the target vehicle is traveling in the target environment
  • the device also includes:
  • the sixth display module is used to display driving assistance information, where the driving assistance information includes at least one of the following:
  • Relative operating condition information between the associated operating vehicle and the target vehicle is Relative operating condition information between the associated operating vehicle and the target vehicle.
  • the target vehicle is any one of a plurality of controlled vehicles controlled by the remote driving entity
  • the device also includes:
  • a position information acquisition module used to acquire position information of non-controlled objects in the target environment and position information of controlled vehicles around the target vehicle;
  • a road condition statistics module used for collecting statistics of road condition information of the surrounding environment of the target vehicle based on the position information of each non-controlled object and the position information of the controlled vehicles around the target vehicle;
  • a prompt module is used to display prompt information in response to the target vehicle's road condition information meeting the preset conditions, and the prompt information is used to prompt that the automatic driving situation is met.
  • the automatic driving start module is used to start the automatic driving function of the target vehicle in response to receiving an automatic driving start operation for the target vehicle.
  • the remote driving method provided by the present application displays a first environment image including at least a portion of the environment corresponding to the first position. Since global scene data is pre-constructed based on the target environment, the first environment image can be directly generated based on the local scene data corresponding to the first position. When there is a vehicle driving operation on the target vehicle, the local scene data corresponding to the current position can be used to generate and display a second environment image of the environment where the current position is located. By combining the local scene data with the vehicle position, the display of the vehicle's surrounding environment can be achieved. There is no need for the target vehicle to transmit the captured video of the surrounding environment in real time, which greatly reduces the bandwidth occupied by data transmission during remote driving.
  • FIG9 is a schematic diagram of the structure of a remote driving device provided in an embodiment of the present application.
  • the device is applied to a server, as shown in FIG9 , and the device includes:
  • a first sending module 901 is used for sending, in response to receiving a remote driving request from a remote driving entity, a first position of a target vehicle and local scene data corresponding to the first position to the remote driving entity, where the local scene data corresponding to the first position is scene data corresponding to at least a portion of a target environment when the target vehicle is at the first position;
  • a second sending module 902 configured to send the driving instruction to the target vehicle in response to receiving the driving instruction from the remote driving entity, wherein the driving instruction is based on an instruction corresponding to the vehicle driving operation for the target vehicle in the remote driving entity;
  • the third sending module 903 is used to send the current location of the target vehicle to the remote driving entity in response to receiving the current location of the target vehicle during the driving process based on the driving instruction.
  • the remote driving method provided by the present application displays a first environment image including at least a portion of the environment corresponding to the first position. Since global scene data is pre-constructed based on the target environment, the first environment image can be directly generated based on the local scene data corresponding to the first position. When there is a vehicle driving operation on the target vehicle, the local scene data corresponding to the current position can be used.
  • the device of the embodiments of the present application can execute the method provided by the embodiments of the present application, and the implementation principles are similar.
  • the actions performed by each module in the device of each embodiment of the present application correspond to the steps in the method of each embodiment of the present application.
  • FIG10 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application.
  • the electronic device includes: a memory, a processor, and a computer program stored in the memory, and the processor executes the above computer program to implement the steps of the remote driving method, which can achieve the following compared with the related art:
  • the remote driving method provided by the present application displays a first environment image including at least a portion of the environment corresponding to the first position. Since global scene data is pre-constructed based on the target environment, the first environment image can be directly generated based on the local scene data corresponding to the first position. When there is a vehicle driving operation on the target vehicle, the local scene data corresponding to the current position can be used to generate and display a second environment image of the environment where the current position is located. By combining the local scene data with the vehicle position, the display of the vehicle's surrounding environment can be achieved. There is no need for the target vehicle to transmit the captured video of the surrounding environment in real time, which greatly reduces the bandwidth occupied by data transmission during remote driving.
  • An electronic device is provided in an embodiment of the present application, as shown in FIG10 , and the electronic device 1000 shown in FIG10 includes: a processor 1001 and a memory 1003.
  • the processor 1001 and the memory 1003 are connected, such as through a bus 1002.
  • the electronic device 1000 may also include a transceiver 1004, and the transceiver 1004 may be used for data interaction between the electronic device and other electronic devices, such as data transmission and/or data reception.
  • the transceiver 1004 is not limited to one, and the structure of the electronic device 1000 does not constitute a limitation on the embodiment of the present application.
  • Processor 1001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of this application. Processor 1001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.
  • the bus 1002 may include a path for transmitting information between the above components.
  • the bus 1002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc.
  • the bus 1002 may be divided into an address bus, a data bus, a control bus, etc.
  • FIG10 is represented by only one thick line, but it does not mean that there is only one bus or one type of bus.
  • the memory 1003 can be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), magnetic disk storage medium ⁇ other magnetic storage devices, or any other medium that can be used to carry or store computer programs and can be read by a computer, without limitation herein.
  • ROM Read Only Memory
  • RAM Random Access Memory
  • EEPROM Electrically Erasable Programmable Read Only Memory
  • CD-ROM Compact Disc Read Only Memory
  • optical disk storage including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.
  • magnetic disk storage medium ⁇ other magnetic storage devices or any other medium that can be used to carry or store computer programs and can be
  • the memory 1003 is used to store the computer program for executing the embodiment of the present application, and the execution is controlled by the processor 1001.
  • the processor 1001 is used to execute the computer program stored in the memory 1003 to implement the steps shown in the above method embodiment.
  • electronic equipment includes but is not limited to: servers, terminals or cloud computing center equipment, remote driving entities, driving simulation cabins, etc.
  • An embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored.
  • the computer program is executed by a processor, the steps and corresponding contents of the aforementioned method embodiment can be implemented.
  • the embodiment of the present application also provides a computer program product, including a computer program, which can implement the steps and corresponding contents of the aforementioned method embodiment when executed by a processor.
  • each operation step is indicated by arrows in the flowchart of the embodiment of the present application
  • the implementation order of these steps is not limited to the order indicated by the arrows.
  • the implementation steps in each flowchart can be performed in other orders according to demand.
  • some or all of the steps in each flowchart may include multiple sub-steps or multiple stages based on actual implementation scenarios. Some or all of these sub-steps or stages may be executed at the same time, and each sub-step or stage in these sub-steps or stages may also be executed at different times respectively. In different scenarios at the execution time, the execution order of these sub-steps or stages may be flexibly configured according to demand, and the embodiment of the present application does not limit this.

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Abstract

一种远程驾驶方法、装置、电子设备、存储介质及程序产品，方法包括显示目标车辆对应的第一环境图像，第一环境图像包括目标车辆在第一位置时对应的目标环境的至少部分环境的图像；第一环境图像是基于预先构建的目标环境的全局场景数据中第一位置对应的局部场景数据生成的；响应于驾驶员针对目标车辆的车辆行驶操作，利用目标车辆当前所在位置对应的局部场景数据，显示当前所在位置对应的第二环境图像。

Description

远程驾驶方法、装置、电子设备、存储介质及程序产品
本申请要求于2023年04月07日提交中国专利局、申请号为202310406488.7名称为“远程驾驶方法、装置、电子设备、存储介质及程序产品”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及云技术、智慧交通、自动驾驶、远程驾驶等技术领域，本申请涉及一种远程驾驶方法、装置、电子设备、存储介质及程序产品。
背景
远程驾驶，是将驾驶权交由后台服务器接管，由后台服务器的工作人员远程在驾驶模拟舱内操作以控制汽车行驶的驾驶技术。
相关技术中，通过在行驶车辆上安置多个摄像头，采集车辆周围环境视频信息，并通过网络回传到远程的驾驶模拟舱，由驾驶模拟舱进行展示。远程驾驶员通过所展示的视频画面来观察车辆周围环境视频信息，进而操纵驾驶模拟舱中的方向盘、加速踏板等，通过驾驶模拟舱将远程驾驶员的操作信息通过网络传输给行驶车辆，控制车辆行驶。
技术内容
本申请实施例提供了一种远程驾驶方法，所述方法应用于远程驾驶实体，所述方法包括：
响应于远程驾驶请求，显示目标车辆对应的第一环境图像，所述第一环境图像包括所述目标车辆在第一位置时对应的目标环境的至少部分环境的图像；所述第一环境图像是基于预先构建的目标环境的全局场景数据中第一位置对应的局部场景数据生成的；
响应于驾驶员针对所述目标车辆的车辆行驶操作，显示所述目标车辆对应的第二环境图像，所述第二环境图像包括所述目标车辆当前所在位置对应的目标环境的至少部分环境的图像。
在一个可能实现方式中，所述目标环境的全局场景数据的获取方式，包括：
通过目标扫描设备预先对所述目标环境进行扫描，得到所述目标环境的点云数据；
基于扫描得到的点云数据，对所述目标环境进行三维建模，将建模得到的环境模型的模型数据作为所述全局场景数据。
本申请实施例提供了一种远程驾驶方法，所述方法应用于服务器，所述方法包括：
响应于接收到远程驾驶实体的远程驾驶请求，向所述远程驾驶实体发送目标车辆的第一位置和第一位置对应的局部场景数据，所述第一位置对应的局部场景数据是目标车辆在第一位置时对应于目标环境的至少部分环境的场景数据；
响应于接收到所述远程驾驶实体的行驶指令，向目标车辆发送所述行驶指令，所述行驶指令是基于在远程驾驶实体中针对目标车辆的车辆行驶操作对应的指令；
响应于接收到目标车辆基于所述行驶指令行驶过程中的当前所在位置，向所述远程驾驶实体发送所述目标车辆的当前所在位置。
本申请实施例还提供了一种远程驾驶装置，所述装置应用于远程驾驶实体，所述装置包括：
第一显示模块，用于响应于远程驾驶请求，显示目标车辆对应的第一环境图像，所述第一环境图像包括所述目标车辆在第一位置时对应的目标环境的至少部分环境的图像；所述第一环境图像是基于预先构建的目标环境的全局场景数据中第一位置对应的局部场景数据生成的；
第二显示模块，用于响应于驾驶员针对所述目标车辆的车辆行驶操作，显示所述目标车辆对应的第二环境图像，所述第二环境图像包括所述目标车辆当前所在位置对应的目标环境的至少部分环境的图像。
本申请实施例还提供了一种远程驾驶装置，所述装置应用于服务器，所述装置包括：
第一发送模块，用于响应于接收到远程驾驶实体的远程驾驶请求，向所述远程驾驶实体发送目标车辆的第一位置和第一位置对应的局部场景数据，所述第一位置对应的局部场景数据是目标车辆在第一位置时对应于目标环境的至少部分环境的场景数据；
第二发送模块，用于响应于接收到所述远程驾驶实体的行驶指令，向目标车辆发送所述行驶指令，所述行驶指令是基于在远程驾驶实体中针对目标车辆的车辆行驶操作对应的指令；
第三发送模块，用于响应于接收到目标车辆基于所述行驶指令行驶过程中的当前所在位置，向所述远程驾驶实体发送所述目标车辆的当前所在位置。
本申请实施例提供了远程驾驶实体，所述远程驾驶实体，包括处理器和显示器；
其中，所述显示器用于实现如上述远程驾驶方法中任一项所述的远程驾驶方法；所述处理器用于实现如上述远程驾驶方法中任一项所述的远程驾驶方法。
本申请实施例提供了一种电子设备，包括存储器、处理器及存储在存储器上的计算机程序，所述处理器执行所述计算机程序以实现上述的远程驾驶方法。
本申请实施例提供了一种计算机可读存储介质，其上存储有计算机程序，所述计算机程序被处理器执行时实现上述的远程驾驶方法。
本申请实施例提供了一种计算机程序产品，包括计算机程序，所述计算机程序被处理器执行时实现上述的远程驾驶方法。
附图说明
为了更清楚地说明本申请实施例中的技术方案，下面将对本申请实施例描述中所需要使用的附图作简单地介绍。
图1为本申请实施例提供的一种远程驾驶方法的实施环境示意图；
图2为本申请实施例提供的一种远程驾驶实体的结构示意图；
图3为本申请实施例提供的一种驾驶模拟舱的结构示意图；
图4为本申请实施例提供的一种远程驾驶方法的流程示意图；
图5为本申请实施例提供的一种部分环境模型的场景示意图；
图6为本申请实施例提供的一种远程驾驶方法信令交互示意图；
图7为本申请实施例提供的一种远程驾驶方法的流程示意图；
图8为本申请实施例提供的一种远程驾驶装置的结构示意图；
图9为本申请实施例提供的一种远程驾驶装置的结构示意图；
图10为本申请实施例提供的一种电子设备的结构示意图。
实施方式
下面结合本申请中的附图描述本申请的实施例。应理解，下面结合附图所阐述的实施方式，是用于解释本申请实施例的技术方案的示例性描述，对本申请实施例的技术方案不构成限制。
可以理解的是，在本申请的具体实施方式中，涉及到驾驶员信息、驾驶员的驾龄、驾驶员的经验等级、驾驶员所关联的被控车辆、驾驶员控制车辆的驾驶操作、行驶路线等任何与对象相关的数据，当本申请以上实施例运用到具体产品或技术中时，需要获得对象许可或者同意，且相关数据的收集、使用和处理需要遵守相关国家和地区的相关法律法规和标准。
上述方式实际上利用摄像头实时传输给驾驶模拟舱，带宽占用非常高；尤其在同一网络中的多个驾驶模拟舱，极容易造成网络拥堵，无法保证视频传输的稳定性和实时性，从而导致远程驾驶的稳定性和实际驾驶效率较差。
因此，本申请提出一种远程驾驶方法、装置、电子设备、存储介质及程序产品，可以提高远程驾驶的稳定性和实际驾驶效率。
图1为本申请提供的一种远程驾驶方法的实施环境示意图。如图1所示，该实施环境包括：远程驾驶实体11、车辆12和服务器13。该服务器13分别与远程驾驶实体11、车辆12之间建立通 信连接。
远程驾驶实体11可以是远程驾驶车辆12的控制实体。在一可能场景中，驾驶员可在远程驾驶实体11上进行驾驶操作，以控制该车辆12行驶。其中，远程驾驶实体11可将驾驶员的驾驶操作所对应的控制指令，发送至服务器13，由服务器13将该控制指令发送给该远程驾驶实体11所对应控制的车辆12；该车辆12基于接收到的控制指令，按照驾驶员在远程驾驶实体11的驾驶操作进行行驶。
一示例中，如图2所示，该远程驾驶实体11可以是驾驶模拟舱，驾驶模拟舱可包括显示单元111、驾驶员输入单元112、驾驶舱主机113。
其中，显示单元111用于对车辆12所行驶的周围环境进行展示，该显示单元111可以包括具备显示功能的电子显示屏、投影仪、曲面屏、折叠屏、多面屏等任一个或多个部件。
该驾驶员输入单元112可用于接收驾驶员所输入的驾驶操作，该驾驶员输入单元112可以是模拟的车辆12中可被驾驶员操作的部件，该驾驶员输入单元112可包括但不限于：方向盘、加速踏板、制动踏板等。该驾驶员输入单元112可以是虚拟的部件，如在显示屏上显示的具备相应实体功能的虚拟方向盘、虚拟加速踏板、虚拟制动踏板等；也可以是具备实体结构的部件，如实体方向盘、实体加速踏板等。
驾驶舱主机113可以是为远程驾驶实体11提供一定功能的真实机或虚拟机，本申请中，该驾驶舱主机113可提供有数据收发存储功能、数据渲染功能或远程配置功能中的至少一项。例如，数据收发存储功能可用于实现驾驶员对应触发的控制指令的发送、车辆12所在目标环境的场景数据的接收、存储等。例如，数据渲染功能可用于实现对场景数据进行渲染生成对应的图像渲染数据，以使得显示单元111可基于图像渲染数据来显示对应图像。例如，远程配置功能可供用户在驾驶舱主机113上远程对车辆进行配置，如选择所要远程驾驶的车辆、启动该车辆等。
需要说明的是，该远程驾驶实体11可以是模拟车辆内部驾驶环境且具备显示功能的任意实体设备。例如，该远程驾驶实体11可以是驾驶模拟舱；图3示出了一种可能的驾驶模拟舱的结构示意图；如图3所示，该驾驶模拟舱可装备有多个显示屏301、方向盘302、加速踏板303、制动踏板304等实体；当然还可装备有模拟车辆驾驶员座位的座椅305，驾驶员可坐在座椅305中基于显示屏301所展示的车辆的周围行驶环境，来操作方向盘302、加速踏板303、制动踏板304等。
又例如，该远程驾驶实体11还可以是具备显示功能且支持驾驶员的驾驶操作的其他设备，例如，包括显示屏和一些特定功能按钮的驾驶操作台、或者具备多屏或单屏的计算机设备、个人电脑、智能手机、模拟驾驶的电子游戏终端等，该特定功能按钮可包括但不限于：具备与方向盘、加速踏板、制动踏板等车辆驾驶部件的相同功能的，虚拟显示按钮或实体按键等。
在一些实施例中，该车辆12上可安装有远程驾驶控制器121。
其中，该远程驾驶控制器121用于基于服务器13传输的控制指令来对车辆12进行控制。该远程驾驶控制器121可与车辆12进行通信以获取车辆12的车速、方向盘转向、油耗等行驶信息。该远程驾驶控制器121还具备定位功能，在远程驾驶实体11控制车辆12行驶过程中，该远程驾驶控制器121可将车辆12的实时定位信息以及车速、油耗等行驶信息发送至服务器13，以便服务器13实时向远程驾驶控制器121同步这些信息。
该实施环境中还可包括基站14。该基站14可用于该远程驾驶控制121与服务器13之间的实时通信。例如，该远程驾驶控制121通过基站14向服务器13发送实时定位信息、行驶信息等，以及接收服务器13发送的控制指令。
一场景示例中，如图1所示，该服务器13可以是图1中的远程控制云，该远程驾驶实体11可以是图1中的驾驶模拟舱，该实施环境中可包括多个驾驶模拟舱、安装有远程驾驶控制器的多个车辆、远程控制云以及基站。驾驶模拟舱1、驾驶模拟舱2、……驾驶模拟舱n等多个驾驶模拟舱可通过远程控制云、基站，来控制多个车辆中的对应被控车辆。其中，一个驾驶模拟舱可关联一个或多个被控车辆，可同时控制一个或多个被控车辆的行驶。
本申请实施例中，车辆可以代指具备行驶功能的任意形式的行驶载具，例如，该车辆可包括两轮车辆、四轮汽车、三轮机动车、或更多轮的车辆，还可以包括挖掘机、无人挖掘机、吊车等支持起吊搬运作业的机械设备，还可以包括具备车体的移动功能的智能移动机器，如智能机器人、电子智能机器狗、足轮复合四足机器人、可移动的双臂机器人、商场或展厅中使用的移动机器人等。
本申请对车辆的具体类型、所表现的外观形式、移动或行驶的方式等不做限定，本申请对远程驾驶实体11所控制车辆的类型、数量、车辆外观形式等也不做限定。
其中，服务器13可以是远程驾驶云，可用于数据收发；还可存储各个车辆上传的位置信息， 如高精度定位信息；还可存储目标环境的全局场景数据，如闭道路环境三维可视化模型的模型数据；以及，还可用于收发和存储远程驾驶实体11的行驶指令。
服务器13可以是独立的物理服务器，或是多个物理服务器构成的服务器集群或者分布式系统，或是提供云服务、云数据库、云计算、云函数、云存储、网络服务、云通信、以及大数据和人工智能平台等基础云计算服务的云服务器或服务器集群。终端可以是智能手机、平板电脑、笔记本电脑、数字广播接收器、台式计算机、车载终端(例如车载导航终端、车载电脑等)、智能音箱、智能手表等。终端以及服务器可以通过有线或无线通信方式进行直接或间接地连接，也可基于实际应用场景需求确定，在此不作限定。
图4为本申请实施例提供的一种远程驾驶方法的流程示意图。该方法的执行主体可以为远程驾驶实体，该远程驾驶实体可以为在远程模拟驾驶的驾驶模拟舱、或者具备显示功能驾驶操作台、单屏或多屏终端、模拟驾驶的电子游戏终端等任意电子设备。如图4所示，该方法包括以下步骤。
步骤201、响应于远程驾驶请求，远程驾驶实体显示目标车辆对应的第一环境图像。
其中，该第一环境图像包括该目标车辆在第一位置时对应的目标环境的至少部分环境的图像；该第一环境图像是基于预先构建的目标环境的全局场景数据中第一位置对应的局部场景数据生成的。
下面先对目标环境进行介绍：
该目标环境可以是该目标车辆所对应的行驶环境。该目标环境中可包括可供车辆行驶的道路，还包括建筑、设施、交通指示牌、交通灯、树木、草坪、河流、山脉等场景元素。在一些示例中，该目标环境可以是真实世界中的实体环境，例如，目标环境可以是工厂车间、工业园区等工业环境，也可以是矿山区或港口区等作业环境，还可以是受泥石流影响、或受暴雨或者暴雪等天气影响的特殊环境区域等。又一示例中，该目标环境也可以是虚拟的环境，如测试阶段可使用一些虚拟的道路、建筑设置等场景元素来构建一个虚拟环境；例如，模拟泥石流场景的虚拟环境区域，或者模拟恶劣天气下的港口或矿山等的测试作业环境，或者模拟工业园区中作业流程的虚拟园区环境等。
在一些示例中，该目标环境可以是一个封闭行驶环境，该封闭行驶环境提供有包括目标车辆在内的多个车辆行驶的环境，该多个车辆均在对应的远程驾驶实体的控制下进行行驶；该封闭行驶环境中不包括行人、以及不受远程驾驶实体控制的其他车辆。另一示例中，该目标环境可以是一个开放行驶环境，该开放行驶环境中不仅包括在远程驾驶实体控制下行驶的车辆，还包括一些行人、不受远程驾驶实体控制的传统车辆、自行车等。
下面对全局场景数据进行介绍：
该目标环境的全局场景数据用于展示以目标环境为原型的场景画面。该目标环境的全局场景数据承载有该目标环境中的场景元素。该场景元素是指组成目标环境中的场景的元素，如道路、交通标志指示牌、交通信号灯等。其中，目标环境中的场景元素，可包括静态场景元素和动态场景元素。其中，静态场景元素是指在目标环境的更新周期内呈静态不变的场景元素，可包括但不限于：道路、交通标志指示牌、道路两边的楼体、墙体、草坪、树木等。动态场景元素是指在目标环境的更新周期内呈现状态可变的场景元素，可包括但不限于：交通信号灯、建筑钟塔等。
在一示例中，全局场景数据包括目标环境中的各个场景元素对应的场景数据。例如，场景数据包括但不限于场景元素的形状、颜色、位置坐标等数据，例如，道路对应的场景数据中，可包括该道路的形状、位置、路面中的各个位置点的颜色、路面中的交通线的形状和颜色等。又一示例中，该全局场景数据可包括目标环境中各个位置点以及各个位置点所对应的渲染数据，位置点可以是目标环境所覆盖的各个位置坐标，该渲染数据可包括该位置坐标对应的RGB数据、亮度数据等。
在一些实施例中，可预先构建并存储目标环境的全局场景数据。示例性的，全局场景数据的获取方式可包括以下步骤A1-A2：
步骤A1、通过目标扫描设备预先对该目标环境进行扫描，得到该目标环境的点云数据；
步骤A2、基于该扫描得到的点云数据，对该目标环境进行三维建模，将建模得到的环境模型的模型数据作为该全局场景数据。
在一示例中，该目标扫描设备可以包括激光雷达设备，可控制激光雷达设备在目标环境中移动，并在移动过程中扫描得到目标环境中各个环境位置的点云数据。该点云数据可包括目标环境的多个关键位置点的位置坐标、以及各个位置点的颜色信息、反射强度信息等，进而基于该点云数据进行三维建模。例如，该激光雷达设备可搭载在可行驶的车辆、或者可飞行的无人机上，以完成对目标环境中各个位置点的扫描。
又一示例中，该目标扫描设备还可以包括图像采集设备，如3D摄像机；若点云数据仅包括关键位置点的位置坐标，还可通过3D摄像机扫描得到各个位置点的颜色、光线强度等图像数据，利用该激光雷达设备扫描得到的点云数据和3D摄像机扫描得到的数据进行三维建模。
需要说明的是，点云数据或全局场景数据中的位置、位置坐标，可以是在世界坐标系中的位置坐标。例如，该世界坐标系可以是WGS84(world geodetic system，世界大地坐标系)。
在一示例中，上述步骤A1-A2可其他设备执行，如由专门的环境监测设备预先构建全局场景数据，并在远程驾驶实体启动远程驾驶之前，将预先构建的全局场景数据发送至服务器，服务器存储该全局场景数据。当然，也可由服务器直接执行上述步骤A1-A2，如服务器与目标扫描设备之间建立通信连接，在启动远程驾驶之前，服务器利用该通信连接获取点云数据，通过步骤A2构建得到全局场景数据并进行存储。本申请对上述步骤A1-A2由谁来执行不做限定。基于此，远程驾驶实体启动对目标车辆的驾驶时，便可从服务器中获取该全局场景数据、或目标车辆所在位置对应的至少部分环境的局部场景数据。示例性的，服务器可定期更新全局场景数据，服务器可按照目标环境对应的更新周期，更新该目标环境的全局场景数据。该全局场景数据中所承载的目标环境可以是相对而言在更新周期内不会变化的固定环境，例如，若更新周期是1天，则目标环境中的场景元素可以是在一天内不会变动的场景元素，例如临时路障、植被等。如果在环境变化较为频繁的场景，例如建设工地，则需要提高全局场景数据的更新频率，相应地，被纳入环境的静态物体范围也会变化，例如，正在建设中的楼体结构、建筑材料堆等可以作为动态元素，而在更新周期内不变的元素，如临时搭建的围栏、支架等可以认为是相对在更新周期内静态的元素。
如图5所示，图5中展示了目标环境对应的部分环境模型，该部分环境模型中还原了目标环境中的草坪、道路、建筑楼体、墙体等静态元素。
需要说明的是，本申请仅以上述利用点云数据进行三维建模为例，对全局场景数据的获取过程进行说明。例如，还可获取其他数据来进行三维建模，或者还可利用二维建模的环境模型数据、或是四维建模的环境模型数据作为全局场景数据。本申请对此均不作限定。
在一些实施例中，远程驾驶实体可基于驾驶员的触发操作，启动远程驾驶。一示例中，该远程驾驶实体可显示多个候选车辆，由驾驶员从中选择目标车辆，以启动对目标车辆的远程驾驶。又一示例中，由服务器为驾驶员分配目标车辆，远程驾驶实体启动对所分配车辆的远程驾驶。相应的，启动对目标车辆的远程驾驶的实现方式，可包括以下方式一和方式二的两种实现方式：
方式一、响应于第一驾驶触发操作，远程驾驶实体显示远程配置页面，接收针对至少一个候选车辆中目标车辆的选择操作。
其中，该远程配置页面中显示有该至少一个候选车辆的车辆信息；该远程驾驶请求是基于选择操作所触发的第一驾驶请求。示例性的，该第一驾驶触发操作是触发对远程驾驶进行配置的操作。例如，该第一驾驶触发操作包括但不限于：对远程驾驶平台的启动操作，对该平台页面中的配置按钮的触发操作等。例如，该远程配置页面中可显示有各个候选车辆各自对应的选择控件，驾驶员可基于该页面中所展示的各个候选车辆的车辆信息，触发目标车辆对应的选择控件；该远程配置页面中还可弹出提示是否启动的提示页面，驾驶员可通过触发该提示页面中的启动驾驶控件，以启动对目标车辆的远程驾驶。当然，也可在检测到对选择控件的触发操作时直接触发该启动驾驶过程。
对应于方式二，步骤201的实现方式可包括：远程驾驶实体检测到对远程配置页面中目标车辆的选择操作时，触发第一驾驶请求；响应于该第一驾驶请求，显示该第一环境图像。其中，该远程驾驶实体可向服务器发送该第一驾驶请求，该第一驾驶请求用于请求对目标车辆进行远程驾驶，该第一驾驶请求可携带目标车辆的车辆标识信息。
示例性的，该远程配置页面中，还可包括该驾驶员的历史驾驶信息，例如，对各个候选车辆中历史驾驶车辆的驾驶次数、驾驶时间、历史行驶路线、历史行驶区域等数据。
示例性的，该候选车辆的车辆信息，可包括用于唯一标识车辆的标识信息，还可包括车辆尺寸、车型、颜色等静态信息。一示例中，该车辆信息可如下表1所示：
表1
方式二、远程驾驶实体响应于第二驾驶触发操作，显示信息录入页面，接收基于录入控件触发的驾驶员信息录入操作。
其中，该信息录入页面中显示有用于录入驾驶员信息的录入控件；该远程驾驶请求是基于驾驶员信息录入操作所触发的第二驾驶请求。例如，该录入控件可以是信息输入框、或者候选项选择按钮等，该驾驶员信息可包括但不限于：驾驶员的驾照类型、驾驶经验等级、历史驾驶车辆的尺寸、类型、操作难度等信息。该远程驾驶实体检测到在基于录入控件触发的录入操作时，基于所录入的驾驶员信息，触发第二驾驶请求。该录入操作可包括触发录入控件并输入信息的操作，还可包括对该页面确认控件的触发操作等。
示例性的，该第二驾驶触发操作可以是触发录入驾驶员信息的操作。一示例中，该第二驾驶操作可以是对远程驾驶平台的启动运行操作、或者驾驶员登录操作等，例如，该信息录入页面可以是登录页面，该录入操作可以是登录信息的输入操作，远程驾驶实体可根据输入的登录账号、用户名等登录信息，获取该登录信息所关联的驾驶员信息，并基于此为该驾驶员分配对应的远程驾驶车辆。又一示例中，平台页面中可显示用于请求分配远程驾驶车辆的分配按钮，第二驾驶操作也可以是对平台页面中的分配按钮的触发操作。
示例性的，该远程驾驶实体向服务器发送第二驾驶请求后，服务器可基于驾驶员系信息在全局候选车辆中进行匹配，得到与驾驶员信息匹配的多个匹配车辆，并向远程驾驶实体提供该多个匹配车辆的信息。该远程驾驶实体可显示服务器所提供的多个匹配车辆的车辆信息，如与驾驶员的驾照、驾驶经验等级等匹配的多个车辆的信息；驾驶员也可从该多个匹配车辆中选择目标车辆，该远程驾驶实体检测到驾驶员对该多个匹配车辆中目标车辆的选择操作，向服务器发送对目标车辆的远程驾驶请求。
该第二驾驶请求用于请求分配远程驾驶的车辆。对应于方式二，步骤201的实现方式可包括：远程驾驶实体检测到在信息录入页面中的录入操作时，基于所录入的驾驶员信息触发第二驾驶请求；响应于该第二驾驶请求，显示该第二环境图像。该远程驾驶实体可向服务器发送该第二驾驶请求；例如，该第二驾驶请求可携带当前登录信息，如驾驶员ID、驾驶员的驾照ID、登录名、登录账号等，以使服务器基于该当前登录信息获取关联的驾驶员信息；又例如，该第二驾驶请求可携带驾驶员信息。
在一些实施例中，该第一位置可以是启动远程驾驶时该目标车辆的初始位置。例如，该初始位置可以是目标车辆在最近一次历史驾驶过程的终点位置，或者，还可以是预先配置的默认起始位置。在另一些实施例中，该第一位置还可以是已启动远程驾驶后、在目标车辆行驶过程中的位置；例如，启动远程驾驶后，该远程驾驶实体可按照预配置周期更新所显示的环境图像，该第一环境图像可以是上一周期对应显示的环境图像。
在步骤201中，响应于第一驾驶请求或第二驾驶请求，该远程驾驶实体从服务器中接收目标环境的全局场景数据或第一场景数据中的至少一项，该第一场景数据是与该第一位置对应的至少部分环境的局部场景数据；该远程驾驶实体基于所接收的全局场景数据或第一场景数据，显示第一环境图像。
在一些实施例中，该第一环境图像可包括第一位置对应的至少部分环境的图像。该第一位置对应的至少部分环境包括：该第一位置对应的目标环境、或者第一位置在目标环境对应的部分环境中的至少一项。也即是，第一环境图像可显示有第一位置对应的目标环境的全局场景元素、或者至少部分环境的局部场景元素。
例如，该远程驾驶实体可基于该第一场景数据，渲染得到至少部分环境中的场景元素的图像渲染数据，并基于该图像渲染数据显示该第一位置对应的至少部分环境的图像。又例如，该远程驾驶实体也可基于该全局场景数据，渲染得到该第一位置所在的目标环境中全局场景元素的图像渲染数据，并基于该图像渲染数据显示该第一环境图像。
示例性的，该远程驾驶实体可显示以目标车辆为视角的至少部分环境的图像。以目标车辆为视角，是指从目标车辆所在位置的角度所看到的周围至少部分环境；例如，以第一位置出发目光所及之处为目标车辆的周围环境。该第一环境图像中各个环境元素可基于与目标车辆之间的相对位置、按照一定的规则排列，如近大远小的规则排列。
在一些实施例中，该至少部分环境可以是基于第一位置得到的一定区域范围内的环境；例如，该第一位置对应的至少部分环境可包括该第一位置的周围区域，如目标环境内以第一位置为中心的预设距离范围内的空间区域，如目标车辆10米、30米或100米范围内的环境区域。例如，该至少部分环境可以是从指定的角度范围内的环境，例如，该目标车辆的前方的环境、左右两侧的周围环 境或者以目标车辆为中心的指定的270°范围内的周围环境、或周围360°全景环境等。
本申请实施例中，该远程驾驶实体还可在第一环境图像中显示周围车辆、环境的天气、光照等状态数据。相应的，步骤201的实现方式，包括以下方式一至方式四中的至少一种：
方式一、响应于第一驾驶请求或第二驾驶请求，显示第一图像。
其中，该第一环境图像可以是第一图像。该第一图像包括第一位置对应的至少部分环境、以及目标车辆的各个周边车辆。例如，在第一图像中显示周围环境中的道路、建筑物、交通指示牌等场景元素，还可显示有周围环境中的周边车辆，如停驻的周边车辆或行驶过程中的周边车辆等，例如，可在第一图像中还原显示周边车辆的实际形状、颜色、车牌、车型、行驶状态等信息，行驶状态如后车灯闪烁即将转弯、减速行驶、即将停靠等。
相应的，在步骤201可包括：响应于第一驾驶请求或第二驾驶请求，该远程驾驶实体从服务器中接收目标环境所对应的各个车辆的位置信息，基于该各个车辆的位置信息，确定该目标车辆的各个周边车辆；并基于全局场景数据或第一场景数据中的至少一项，以及基于各个周边车辆的位置信息，显示第一图像。在该第一图像中的对应位置显示有各个周边车辆，所述目标环境对应的各个车辆可以为所述目标环境中的车辆，可以包括目标车辆以及目标车辆的各个周边车辆。
该远程驾驶实体可基于包括目标车辆在内的各个车辆的位置信息，在第二图像中显示该目标车辆的周边环境、以及在对应周边环境的对应位置显示周边车辆。需要说明的是，目标车辆可向该远程驾驶实体发送自己的位置信息，例如通过目标车辆上安装的驾驶控制器进行定位，并向服务器发送目标车辆的位置信息，服务器将目标车辆的位置信息同步给该远程驾驶实体。对于目标车辆之外的其他车辆，可采用与目标车辆同理的方式，各个其他车辆可向对应关联的其他远程驾驶实体发送自己的位置信息；该远程驾驶实体可从各个其他车辆对应的其他远程驾驶实体中，获取其他车辆的位置信息。
方式二、响应于第一驾驶请求或第二驾驶请求，显示第二图像。
其中，该第一环境图像可以是第二图像。该第二图像包括第一位置对应的至少部分环境、以及各个周边车辆和各个周边车辆与该目标车辆之间的相对位置信息。
相应的，在步骤201可包括：响应于第一驾驶请求或第二驾驶请求，该远程驾驶实体从服务器中接收目标环境所对应的各个车辆的行驶状态和位置信息，基于该各个车辆的位置信息和行驶状态，确定该各个周边车辆与该目标车辆的相对位置信息和相对行驶状态。并基于全局场景数据或第一场景数据中的至少一项，以及基于各个周边车辆与该目标车辆的相对位置信息和相对行驶状态，显示第二图像。
示例性的，该远程驾驶实体还可在第二图像中显示目标车辆和各个周边车辆之间的相对位置信息和相对行驶状态。例如，在第二图像中标注有目标车辆和周边车辆的相对距离，如目标车辆距离前方车辆10米、距离后方车辆20米等，还可标注有周边车辆相对于目标车辆，车速是否更慢或更快、周边车辆是否即将拐弯、停靠等相对行驶状态。
方式三、响应于第二驾驶请求，显示第三图像。
其中，该第一环境图像可以是第三图像。该第三图像包括第一位置对应的至少部分环境、以及服务器所分配的目标车辆的车辆信息。
相应的，步骤201可包括：响应于第二驾驶请求，该远程驾驶实体从服务器中接收所分配的目标车辆的车辆信息；并基于全局场景数据或第一场景数据中的至少一项，以及基于目标车辆的车辆信息，显示第三图像。
示例性的，若该目标车辆是由服务器基于驾驶员信息所分配的车辆，该远程驾驶实体还可在第三图像中显示所分配的目标车辆的车辆信息，以使驾驶员及时了解远程操作驾驶的车辆的情况。
方式四、响应于第一驾驶请求或第二驾驶请求，显示第四图像。
其中，该第一环境图像可以是第四图像。该第四图像包括第一位置对应的至少部分环境、以及该目标环境的状态数据，该状态数据包括该第一位置对应的至少部分环境的气象数据、光照强度或目标环境中状态可变对象的当前状态中的至少一项。
相应的，步骤201可包括：响应于第一驾驶请求或第二驾驶请求，该远程驾驶实体从服务器中接收目标环境的状态数据，该状态数据包括该第一位置的气象数据、光照强度或目标环境中状态可变的第一对象的当前状态中的至少一项；该远程驾驶实体基于全局场景数据或第一场景数据中的至少一项，以及基于目标环境的状态数据，显示第四图像。
示例性的，该状态可变对象可包括目标环境中的动态元素，如交通指示灯、建筑塔钟等；例如，交通指示灯的当前状态为当前所指示的交通灯是红灯、绿灯或是黄灯。
步骤202、响应于驾驶员针对该目标车辆的车辆行驶操作，远程驾驶实体显示该目标车辆对应的第二环境图像。
其中，该第二环境图像包括该目标车辆当前所在位置对应的目标环境的至少部分环境的图像。
该车辆行驶操作可以是驾驶员在远程驾驶实体上控制目标车辆行驶的驾驶操作。例如，对驾驶模拟舱中的方向盘的转动操作、对制动踏板或加速踏板的踩动操作等。本步骤中，该远程驾驶实体可基于当前所在位置，获取目标环境中当前所在位置所对应的至少部分环境的第二场景数据；该远程驾驶实体可基于全局场景数据或第二场景数据，渲染得到该当前所在位置对应的图像渲染数据，并基于所得到的图像渲染数据在显示屏中显示该第二环境图像。
示例性的，该第二环境图像中，也可包括但不限于以下至少一项：周边车辆、周边车辆与目标车辆之间的相对位置、下一时刻的环境位置对应的状态数据；相应的，该第二环境图像中显示该至少一项信息的实现方式，是与上述步骤201中方式一、方式二或方式四中对应方式同理的过程，此处不再一一赘述。
在一些实施例中，该远程驾驶实体还可预测目标车辆的行驶情况，并向驾驶员展示所预测的行驶情况。该远程驾驶实体可对当前的行驶情况、所预测的行驶情况进行分屏展示。
示例性的，该远程驾驶实体至少包括第一分屏和第二分屏；相应的，步骤202中显示该第二环境图像的过程可包括：在该第一分屏中显示该第二环境图像。该预测以及对预测情况的展示过程可通过以下步骤B1-步骤B2实现：
步骤B1、远程驾驶实体基于该当前所在位置和该目标车辆的行驶状态，预测该目标车辆在下一时刻的环境位置；
步骤B2、该远程驾驶实体在该第二分屏中，显示该目标车辆在该下一时刻的环境位置所对应的第三环境图像。
其中，该行驶状态可包括目标车辆的行驶速度和行驶方向。该远程驾驶实体可基于当前时刻的所在位置、行驶速度点和行驶方向，预测下一时刻该目标车辆所行驶到的环境位置。该远程驾驶实体可基于下一时刻的环境位置，获取下一时刻的环境位置所对应的至少部分环境的第三场景数据；该远程驾驶实体可基于全局场景数据或第三场景数据，渲染得到该下一时刻的环境位置对应的图像渲染数据，并基于所得到的图像渲染数据在第二分屏中显示该第三环境图像。当然，该第三环境图像也可包括但不限于以下至少一项：：周边车辆、周边车辆与目标车辆之间的相对位置、下一时刻的环境位置对应的状态数据；该过程是与上述步骤201中第一环境图像的显示过程同理的方式，此处不再一一赘述。
需要说明的是，该第一分屏和第二分屏，可以是一个物理屏幕中的不同屏幕显示区域，或者，也可以是独立的两个物理显示屏，本申请对此不做限定。
在一些实施例中，该远程驾驶实体可在获取到来自目标车辆发送的位置信息之前，提前预测目标车辆的位置以提前生成显示环境图像时所采用的图像渲染数据。基于此，从目标车辆中所获取的当前所在位置，可用于对所预测的位置的验证，以结合验证结果来进行显示。
示例性的，在步骤202之前，提前预测位置并提前生成所预测位置对应的图像渲染数据的过程，可通过以下步骤C1-步骤C3实现：
步骤C1、基于该目标环境和已获取的该目标车辆的行进状态信息，对该目标车辆的当前时刻的位置进行预测，得到预测位置；
步骤C2、基于该预测位置，获取该全局场景数据中与该预测位置对应的局部场景数据、以及获取该预测位置对应的至少部分环境的状态数据；
步骤C3、基于该预测位置对应的局部场景数据和状态数据，渲染得到该预测位置对应的渲染后的图像数据。
示例性的，该行进状态信息可包括目标车辆在行驶过程中的速度、方向和所到达的位置等信息，例如，可获取目标车辆在至少一个历史时刻的速度、方向和历史位置，预测当前时刻可到达的位置，得到预测位置。例如，在当前时刻之前的5s内的每1s的速度、方向以及达到的位置，预测第11s，也即是当前时刻之后的第11s的位置。
示例性的，该行进状态信息还可包括以下至少一项：目标车辆在行驶过程中的油耗、电量、行进轨迹、在指定作业路线中所对应的待行驶路线等信息。该远程驾驶实体还可结合该至少一项信息和速度、方向以及来获取该预测位置。示例性的，该远程驾驶实体可通过预先配置的目标算法或神 经网络模型，来实现对目标车辆的位置预测。
例如，该远程驾驶实体可基于该预测位置，从全局场景数据中获取与该预测位置对应的至少部分环境的局部场景数据。又例如，该远程驾驶实体还可基于该预测位置，获取该目标车辆的周边车辆；又例如，该远程驾驶实体还可基于该预测位置，获取周边车辆与目标车辆之间的相对位置信息、相对行驶状态等信息。该远程驾驶实体可基于所获取的局部场景数据、周边车辆、周边车辆与目标车辆之间的相对位置信息和相对行驶状态，渲染得到该预测位置所对应的渲染后的图像数据，也即是图像渲染数据。
在一些实施例中，若该远程驾驶实体提前在目标车辆所实际传输到的当前位置到来之前，提前生成了渲染所使用的图像渲染数据。则该远程驾驶实体可基于实际传输的位置对预测位置进行验证，以在可行时使用该提前生成的渲染数据来显示图像。
相应的，步骤202的实现方式可包括以下两种情况：
情况1、若该预测位置与从目标车辆中获取的当前所在位置匹配，基于该预测位置对应的渲染后的图像数据显示该第二环境图像；
情况2、若该当前环境位置与从目标车辆中获取的当前所在位置不匹配，获取该当前所在位置所对应的局部场景数据和状态数据，并基于该当前所在位置所对应的局部场景数据和状态数据，渲染得到该第二环境图像。
示例性的，若验证通过，也即是，所预测的位置与实际传输的实际当前位置相匹配，则可直接利用提前生成的图像渲染数据来显示第二环境图像。当然，若验证没有沟通过，也即是，预测位置与实际位置不符，则以实际的当前所在位置为准，基于当前所在位置对应的局部场景数据和状态数据，渲染得到该第二环境图像。
需要说明的是，通过上述步骤C1-步骤C3，在步骤202时分情况执行，能够从步骤流程上，解除了接收车辆实际传输位置和生成显示所需渲染数据的先后顺序，支持提前生成显示所需的渲染数据，无需在获取到实际位置之后才执行渲染数据生成步骤，减少了图像展示所需时间，保证了环境图像显示的流畅性，进而节省了显示过程实际所需的显示时间。
在一些实施例中，该目标环境中行驶有与该目标车辆具备关联作业关系的关联作业车辆；该远程驾驶实体还可基于该关联作业车辆的位置信息，对该目标车辆与关联作业车辆的协同作业情况进行展示。
在一些实施例中，本申请还包括以下步骤D：
步骤D、远程驾驶实体显示驾驶辅助信息；
其中，该驾驶辅助信息包括以下至少一项：
所述关联作业车辆与所述目标车辆之间的相对位置信息；
所述关联作业车辆的作业状态和所述目标车辆的作业状态；
所述关联作业车辆与所述目标车辆之间的相对作业进度；
所述关联作业车辆与所述目标车辆之间的相对工况信息。
示例性的，该驾驶辅助信息是用于辅助目标车辆与关联作业车辆之间进行协同作业的信息。
例如，该远程驾驶实体可在第二目标图像中显示该驾驶辅助信息，例如，可在第二目标图像上方的对应位置上叠加驾驶辅助卡片，以在该驾驶辅助卡片中显示该驾驶辅助信息。又例如，该远程驾驶实体还可以在单独的页面中显示该驾驶辅助信息，例如，该远程驾驶实体可显示第四环境图像，并在该第四环境图像中显示有该驾驶辅助信息。又例如，该远程驾驶实体还可在环境地图中呈现该驾驶辅助信息，例如，在目标环境的全局地图或局部地图中显示目标车辆和关联作业车辆之间的相对位置信息。
示例性的，该相对位置信息可以包括但不限于：同时在全局或局部地图中对比显示的目标车辆和关联作业车辆各自对应的位置、目标车辆和关联作业车辆之间的相对距离、目标车辆和关联作业车辆之间的相对已行驶路线等。该远程驾驶实体可基于目标车辆和关联作业车辆各自的位置信息获取该相对位置信息。
示例性的，作业状态是指关联作业车辆在生产工业流程中所处的生产环节、完成状态等。例如，作业状态可以是准备状态、挖掘机的搬运状态、吊车机器的起吊状态等。例如，可通过路侧感知实体，采集该关联作业车辆和目标车辆的图像数据，以从所采集的图像数据中得到关联作业车辆和目标车辆的作业状态。需要说明的是，该路侧感知实体可部署在目标环境中，如部署在目标环境中的特定作业区域、或者道路两侧等。路侧感知实体可以是具备图像数据采集功能的设备，如摄像机、 传感器、检测设备等。
示例性的，该作业进度可以是所完成的作业量、做完成的作业量在总量中所占比例、所完成的作业环节的数量等。当然，也可通过路侧感知实体来获取该作业进度；或者，还可从目标车辆、关联作业车辆中获取各自的作业进度。另外，该远程驾驶实体可基于所获取的各个车辆的作业进度，来统计目标车辆与各个关联作业车辆之间的相对进度。
示例性的，车辆的工况信息可表示车辆在工业生产过程中的作业情况。例如，该工况信息可包括车辆的油耗、车辆的剩余燃料等信息。
例如，该远程驾驶实体可从全局环境的角度来显示相互关联作业的车辆之间的相对作业情况。通过上述步骤D，可实现对相互关联作业的车辆之间相对的行驶过程、移动过程、车辆自身的工作状况等信息进行对比，以使驾驶员能清楚、明确的迅速捕捉作业情况，有助于驾驶员在作业过程中的迅速规划作业，有效提高驾驶员的作业效率。
示例性的，该辅助驾驶信息还包括目标车辆和关联作业车辆在各自的作业行驶路线中对应的行驶进度。该作业行驶路线是该关联作业车辆在作业时需行驶的路线。该远程驾驶实体可基于目标车辆和关联作业车辆在目标环境的位置信息，实时更新目标车辆和关联作业车辆在全局或局部地图中的对应展示位置，以呈现这两个车辆在全局环境或局部环境中对比移动的动态过程。
示例性的，该关联作业车辆可以是由其他远程驾驶实体控制的车辆。具备关联作业关系的两个车辆可以是，各自对应的作业流程之间在时间或空间上存在关联，或者各自的作业操作需相互配合、需相互协助等情况的两个车辆。对于包括A1、A2两个子流程的作业流程A，车辆a在行驶过程中来完成子流程A1部分，由车辆b在A1基础上再在行驶过程中完成子流程A2部分。驾驶员在控制车辆b行驶过程中，远程驾驶实体除了向驾驶员提供车辆的周围环境和周边车辆等信息，还可向驾驶员提供目标车辆与所关联的作业车辆a之间的相对位置变化情况、作业状态变化情况、相对的作业进度、相对工况信息等；例如，当前时刻车辆a、车辆b各自的位置、或者相对而言的行驶速度，以便于驾驶员可控制车辆b加速行驶至车辆a所在位置，以在车辆a基础上继续完成作业流程A2；或者，以便于驾驶员控制车辆b去车辆a未覆盖的作业区域继续开启作业。又或者，若车辆a在行驶过程中抛锚，则驾驶员可控制车辆b中断行驶、或将所关联的作业车辆更新为新的车辆c等。
在一些实施例中，该远程驾驶实体用于远程控制多个被控车辆的行驶，该目标车辆是该远程驾驶实体所控制的多个被控车辆中的任一个；目标车辆自身可具备自动驾驶功能，该远程驾驶实体可实时统计周围的路况，若路况简单则交由自动驾驶来进行控制；若路况复杂则交由远程驾驶实体进行远程控制。相应的，该过程可通过以下步骤E1-步骤E3实现：
步骤E1、获取所述目标环境中非被控对象的位置信息、以及目标车辆的周边被控车辆的位置信息；
例如，该非被控对象可以是不受任一远程驾驶实体远程控制的对象，例如，若目标环境不是封闭的环境，其中还包括一些不使用远程驾驶功能的对象；如行人、普通自行车、不使用远程驾驶的传统汽车等。远程驾驶实体通过路侧感知实体，获取各个非被控对象的位置信息，还可获取非被控对象的速度、方向等行驶状态。
步骤E2、基于所述各个非被控对象的位置信息、以及该目标车辆的周边被控车辆的位置信息，统计目标车辆的周围环境的路况信息；
周边被控车辆是受任意远程驾驶实体控制的各个被控车辆中、位于目标车辆周围的被控车辆。
步骤E3、响应于所述目标车辆的路况信息符合预设条件，显示提示信息，所述提示信息用于提示符合自动驾驶情况；
步骤E4、响应于接收到针对目标车辆的自动驾驶启动操作，启动所述目标车辆的自动驾驶功能。
该路况信息可以包括目标车辆所行驶路段的拥堵程度、所行驶路段的非被控对象的数量、车流量、车流速度等信息。该预设条件可以是用于衡量是否适合切换为自动驾驶功能的条件，如预设条件可包括但不限于：拥堵程度较低、车流量低于一定车流阈值、车流速度低于一定速度阈值等。该远程驾驶实体可结合预设条件判断目标车辆是否适合自动驾驶，若符合预设条件，也即是，目标车辆所行驶路段的路况较简单，非被控对象、车流量等均较少，则可交由自动驾驶功能来进行控制目标车辆行驶。否则，若不符合预设条件，说明是包括较多车辆、行人或车流速度较低的复杂拥堵路段，还需远程控制。
该自动驾驶启动操作，可以是对提示信息的确认操作、或取确认切换操作等，例如，远程驾驶 实体可显示提示信息，并在提示信息页面还提供有确认切换为自动驾驶功能的确认按钮、或者取消切换按钮；自动驾驶启动操作可以是对确认按钮的点击操作。或者，该自动驾驶启动操作，还可以是驾驶员在操作台切换按钮所触发的切换指令；例如，操作台可配置有自动驾驶切换按键，驾驶员可通过触发自动驾驶切换按键来触发切换。通过启动自动驾驶功能，可停止对目标车辆的远程驾驶，使得目标车辆可基于自动驾驶功能进行行驶。
通过上述步骤E1-E4，可适时根据预设条件切换关联车辆的驾驶功能，如远程驾驶或自动驾驶等，从而实现少数远程驾驶员可实时、灵活管理多个被控制车辆，提高了管理的灵活性以及提升驾驶员的实际驾驶效率，提高对所关联车辆的管理效率。
本申请提供的远程驾驶方法，通过显示包括第一位置对应的至少部分环境的第一环境图像，由于预先基于目标环境预先构建有全局场景数据；因此，可直接基于第一位置对应的局部场景数据生成第一环境图像；当有对目标车辆的车辆行驶操作时，可利用当前所在位置对应的局部场景数据，生成并显示当前所在位置所在环境的第二环境图像；通过利用局部场景数据结合车辆位置，即可实现车辆周边环境的展示；无需目标车辆实时传输拍摄的周围环境的视频，大幅降低了远程驾驶过程中数据传输占用的带宽；有效解决了基于实时视频传输画面进行远程驾驶所导致的带宽占用过高的问题；降低对网络带宽需求，有助于提高远程驾驶的稳定性，使得驾驶员可稳定、低延时地控制车辆，提高实际驾驶效率。
图6是本申请提供的一种远程驾驶方法的信令交互示意图。该方法的可由远程驾驶实体和服务器之间交互实现。如图6所示，该方法包括以下步骤。
步骤301、远程驾驶实体响应于驾驶员触发的远程驾驶请求操作，向服务器发送远程驾驶请求。
示例性的，该服务器可获取由各个远程驾驶实体所控制的各个被控车辆的车辆信息，该服务器可关联存储各个车辆及其车辆信息。例如，各个车辆通过基站向服务器上传以下至少一项信息：车辆的位置信息、速度、姿态、行驶状态等。服务器关联存储各个车辆以及各个车辆的至少一项信息。例如，车辆ID相关联地存储该车辆的多项实时信息。例如，预先关联存储各个车辆ID与该车辆ID如表1中所示的各项数据。
该服务器可预先存储多个车辆以及车辆所关联的远程驾驶实体之间的关联关系。该多个车辆可包括与目标车辆同在目标环境中行驶的车辆，还包括在除了目标环境之外的其他环境中行驶的其他车辆。其中，本申请仅以各个车辆中的目标车辆的远程驾驶过程为例进行说明。
各个远程驾驶实体在控制对应的各个车辆行驶过程中，各个车辆可通过基站向服务器发送位置信息。
需要说明的是，该位置信息可以包括高精度的定位信息，如车道级定位的信息，该车道级定位信息中包括目标车辆的位置坐标、以及该目标车辆所在的车道、相邻的车道线等信息。该位置信息可包括行进中的地理位置和驻停时的地理位置，驻停包括但不限于：完全停车、交通信号灯指示下的短暂停车、发生故障或事故下的稍长时停车等的任一种或多种。当然，该位置信息也可包括普通精度的定位信息。
其中，该位置信息中的位置坐标可以是在世界坐标系中的坐标，如WGS84世界大地坐标系。
需要说明的是，该各个车辆可以是支持网联功能的车辆，网联功能是指车辆通过移动通信网络与远程驾驶实体进行通信的功能。移动通信网络包括但不限于：4G、5G、蜂窝网络车联网(Cellular-V2X，C-V2X)、专用短程通信技术(Dedicated Short Range Communications，DSRC)等。例如，可基于V2X通信来支持各个车辆于云端的远程驾驶实体的通信，以实现进行远程遥控驾驶。
各个车辆通过网络所传输的位置信息，是符合想目标通信协议标准的结构化数据，例如，符合5G-V2X通信协议标准的结构化的数据。
本申请，通过基于车辆提供的高精度定位信息和预先构建的环境三维可视化模型，如全局场景数据，使得车辆端只需要通过网络将定位信息发送到远程驾驶实体即可，如驾驶模拟舱；由于定位信息是结构化数据，占用带宽小，低于0.1KByte，远程驾驶实体即可根据数据量很小的定位信息，利用提前构建的全局场景数据，渲染出车辆及车辆周围环境信息，大幅度降低对网络带宽需求，并能提供多视角的环境信息，可帮助远程驾驶员稳定精准并且低延时地控制车辆。
该服务器可预先获取并存储其他环境以及基于其他环境所预先构建的全局场景数据。示例性的，该服务器可采用上述步骤A1-A2的过程来获取对应环境的全局场景数据。此处不再赘述。
在一些实施例中，该远程驾驶实体可向服务器发送第一驾驶请求或第二驾驶请求，该过程与上 述步骤201、202中的远程驾驶请求触发过程同理，此处不再赘述。
步骤302、服务器响应于接收到远程驾驶实体的远程驾驶请求，向该远程驾驶实体发送目标车辆的第一位置以及第一位置对应的局部场景数据。
该第一位置对应的局部场景数据是目标车辆在第一位置时对应于目标环境的至少部分环境的场景数据。
需要说明的是，目标车辆通过所安装的远程驾驶控制器，实时通过基站向服务器传输位置信息，当然，还可传输速度、方向、姿态等行驶状态。该服务器可基于目标车辆发送的信息，向远程驾驶实体进行实时同步。
步骤303、远程驾驶实体接收第一位置以及第一位置对应的局部场景数据，显示目标车辆对应的第一环境图像。
步骤304、远程驾驶实体响应于检测到驾驶员针对该目标车辆的车辆行驶操作，向服务器发送行驶指令。
其中，该行驶指令是基于在远程驾驶实体中针对目标车辆的车辆行驶操作对应的指令。
步骤305、服务器响应于接收到远程驾驶实体的行驶指令，向目标车辆发送该行驶指令。
该目标车辆可基于该行驶指令所指示的车辆行驶操作进行行驶，并在行驶过程中实时发送所在位置。
在一些实施例中，目标车辆中的远程驾驶控制器可通过基站接收服务器发送的行驶指令。该远程驾驶控制器可通过车辆的CAN(Controller Area Network，控制器局域网总线)与车辆的控制系统之间进行实时通信。该远程驾驶控制器可通过CAN总线获取车辆在行驶过程中的车速、方向盘转角、油耗等信息；该远程驾驶控制器具备定位功能，可利用该定位功能实时定位车辆的位置；该远程驾驶控制可将车辆的实时位置以及行驶过程中的车速、方向盘转角、油耗等信息，实时发送给服务器。
示例性的，该远程驾驶控制器可通过车辆的CAN(Controller Area Network，控制器局域网总线)与车辆的控制系统之间进行实时通信，例如，该远程驾驶控制器可通过CAN总线与车辆的ECU(Electronic Control Unit，电子控制单元)、VCU(Vehicle Control Unit电动汽车整车控制器)、或者MCU(Microcontroller Unit，微控制单元)等进行通信，以控制车辆在行驶过程中的降速、增速、转弯、驻车等，以实现按照行驶指令进行行驶的过程。
步骤306、响应于接收到目标车辆在基于该行驶指令行驶过程中发送的当前所在位置，向远程驾驶实体发送该目标车辆的当前所在位置。
步骤307、远程驾驶实体响应于接收到该目标车辆的当前所在位置，显示第二环境图像。
该目标车辆在基于行驶指令进行行驶过程中，可按照一定的周期向服务器实时反馈目标车辆的位置信息，该过程可基于安装在目标车辆上的远程驾驶控制器来实现。当然，该目标车辆还可向服务器发送速度、方向、姿态、行驶状态等行驶过程中的信息。
该服务器实时将目标车辆的当前所在位置同步给远程驾驶实体。以使该远程驾驶实体及时显示第二环境图像。
下面以图7所示的流程，对本申请的远程驾驶过程进一步介绍。如图7所示，以目标环境为封闭道路环境、远程驾驶实体为驾驶模拟舱、服务器为远程控制云为例，该远程驾驶的整个流程可包括以下步骤：
1、对封闭道路环境进行三维建模，得到该封闭道路环境的全局场景数据，可包括但不限于：道路信息、建筑信息、周边环境信息等。由远程控制云获取并存储。
需要说明的是，如图5所示，本申请中进行三维建模的是相对而言固定的环境。例如，当三维建模更新周期以天为单位时，本申请中的固定环境是指在一天内不会变动的环境设备，例如临时路障等。如果在环境变化较为频繁的场景，例如建设工地，则需要提高三维建模更新频率，相应地，被纳入固定环境的物体范围也会变化。
2、在远程控制云上预先录入所有被控车辆的静态信息，包括但不限于上述表1中的被控车辆ID/序号、车身尺寸，颜色，VIN码，最大方向盘转角等信息。
3、所有被控车辆通过基站向远程控制云实时上传实时信息，包括定位状态，位置，速度，姿态，行驶状态等。
4、远程控制云接收并与被控车辆ID相关联地存储所有被控车辆的实时信息。
5、远程驾驶员通过驾驶舱主机的远程配置功能，查看所有被控车辆的静态信息，选择目标被控车辆并启动远程驾驶。另一种方案是，在存在多名远程驾驶员时，由系统根据驾驶员输入的所持 驾照等级和系统内存储的被控车辆所需驾驶执照信息，自动分配该驾驶员需要远程控制的车辆，并且向该驾驶员的驾驶舱主机提供与所分配车辆ID相关联的静态信息。
6、远程配置功能启动数据收发存储功能，向远程控制云发送索取所有被控车辆实时信息及目标被控车辆附近三维模型信息指令，等待远程控制云反馈。
7、数据收发存储功能持续接收所有被控车辆实时信息及目标被控车辆附近三维模型，远程配置功能启动数据渲染功能，实时渲染目标车辆附近环境信息及其他车辆信息，可根据需要调整数据渲染视角，包括，跟车视角，驾驶员视角，俯瞰视角，自有视角等。需要说明的是，由于是在封闭场景内，因此这里的“其他车辆信息”是指其他被控制的车辆，这些车辆的渲染是基于远程控制云所存储的、这些车辆的静态信息和实时信息(例如车辆的尺寸、颜色、位置、朝向等)来进行的。因此，避免了对于直接来自被控车辆的感知信息，节省了通信带宽。
8、驾驶员通过驾驶员输入单元进行操作，数据收发存储功能接收操作信息并存储及发送给远程控制云，远程控制云将相关指令下发给目标被控车辆的远程驾驶控制器。
9、被控车辆的远程驾驶控制器，接收远程控制云指令，并依据指令控制被控车辆执行响应动作。
10、远程驾驶员可通过驾驶舱主机的远程配置功能关闭并停止远程控制。
本申请的远程驾驶方法，涉及云技术、智慧交通、自动驾驶、远程驾驶等技术领域。例如，可利用云技术中的云存储技术创建逻辑卷，以实现对各个环境的全局场景数据进行结构化存储。又例如，可利用本申请的远程驾驶方法，可应用在智能交通系统、智能车路协同系统等交通系统。
可以理解的是，智能交通系统(Intelligent Traffic System，ITS)又称智能运输系统(Intelligent Transportation System)，是将先进的科学技术(信息技术、计算机技术、数据通信技术、传感器技术、电子控制技术、自动控制理论、运筹学、人工智能等)有效地综合运用于交通运输、服务控制和车辆制造，加强车辆、道路、使用者三者之间的联系，从而形成一种保障安全、提高效率、改善环境、节约能源的综合运输系统。或者；
可以理解的是，智能车路协同系统(Intelligent Vehicle Infrastructure Cooperative Systems，IVICS)，简称车路协同系统，是智能交通系统(ITS)的一个发展方向。车路协同系统是采用先进的无线通信和新一代互联网等技术，全方位实施车车、车路动态实时信息交互，并在全时空动态交通信息采集与融合的基础上开展车辆主动安全控制和道路协同管理，充分实现人车路的有效协同，保证交通安全，提高通行效率，从而形成的安全、高效和环保的道路交通系统。
可以理解的是，云计算(cloud computing)是一种计算模式，它将计算任务分布在大量计算机构成的资源池上，使各种应用系统能够根据需要获取计算力、存储空间和信息服务。提供资源的网络被称为“云”。“云”中的资源在使用者看来是可以无限扩展的，并且可以随时获取，按需使用，随时扩展，按使用付费。
可以理解的是，按照逻辑功能划分,在IaaS(Infrastructure as a Service，基础设施即服务)层上可以部署PaaS(Platform as a Service,平台即服务)层，PaaS层之上再部署SaaS(Software as a Service,软件即服务)层，也可以直接将SaaS部署在IaaS上。PaaS为软件运行的平台，如数据库、web容器等。SaaS为各式各样的业务软件，如web门户网站、短信群发器等。一般来说，SaaS和PaaS相对于IaaS是上层。
云存储(cloud storage)是在云计算概念上延伸和发展出来的一个新的概念，分布式云存储系统(以下简称存储系统)是指通过集群应用、网格技术以及分布存储文件系统等功能，将网络中大量各种不同类型的存储设备(存储设备也称之为存储节点)通过应用软件或应用接口集合起来协同工作，共同对外提供数据存储和业务访问功能的一个存储系统。
目前，存储系统的存储方法为：创建逻辑卷，在创建逻辑卷时，就为每个逻辑卷分配物理存储空间，该物理存储空间可能是某个存储设备或者某几个存储设备的磁盘组成。客户端在某一逻辑卷上存储数据，也就是将数据存储在文件系统上，文件系统将数据分成许多部分，每一部分是一个对象，对象不仅包含数据而且还包含数据标识(ID，ID entity)等额外的信息，文件系统将每个对象分别写入该逻辑卷的物理存储空间，且文件系统会记录每个对象的存储位置信息，从而当客户端请求访问数据时，文件系统能够根据每个对象的存储位置信息让客户端对数据进行访问。
存储系统为逻辑卷分配物理存储空间的过程，具体为：按照对存储于逻辑卷的对象的容量估量(该估量往往相对于实际要存储的对象的容量有很大余量)和独立冗余磁盘阵列(RAID，Redundant Array of Independent Disk)的组别，预先将物理存储空间划分成分条，一个逻辑卷可以理解为一 个分条，从而为逻辑卷分配了物理存储空间。
图8为本申请实施例提供的一种远程驾驶装置的结构示意图。该装置应用于远程驾驶实体，如图8所示，该装置包括：
第一显示模块801，用于响应于远程驾驶请求，显示目标车辆对应的第一环境图像，该第一环境图像包括该目标车辆在第一位置时对应的目标环境的至少部分环境的图像；该第一环境图像是基于预先构建的目标环境的全局场景数据中第一位置对应的局部场景数据生成的；
第二显示模块802，用于响应于驾驶员针对该目标车辆的车辆行驶操作，显示该目标车辆对应的第二环境图像，该第二环境图像包括该目标车辆当前所在位置对应的目标环境的至少部分环境的图像。
在一可能实现方式中，该装置还包括全局场景数据获取模块，该全局场景数据获取模块，用于：
通过目标扫描设备预先对该目标环境进行扫描，得到该目标环境的点云数据；
基于扫描得到的点云数据，对该目标环境进行三维建模，将建模得到的环境模型的模型数据作为该全局场景数据。
在一个可能实现方式中，该装置还包括以下至少一项：
第三显示模块，用于响应于第一驾驶触发操作，显示远程配置页面，接收针对至少一个候选车辆中目标车辆的选择操作；该远程配置页面中显示有该至少一个候选车辆的车辆信息；该远程驾驶请求是基于选择操作所触发的第一驾驶请求；
第四显示模块，用于响应于第二驾驶触发操作，显示信息录入页面，接收基于录入控件触发的驾驶员信息录入操作；该信息录入页面中显示有用于录入驾驶员信息的录入控件；该远程驾驶请求是基于驾驶员信息录入操作所触发的第二驾驶请求。
在一个可能实现方式中，该第一显示模块，用于以下至少一项：
响应于第一驾驶请求或第二驾驶请求，显示第一图像，该第一图像包括第一位置对应的至少部分环境、以及目标车辆的各个周边车辆；
响应于第一驾驶请求或第二驾驶请求，显示第二图像，该第二图像包括第一位置对应的至少部分环境、以及各个周边车辆和各个周边车辆与该目标车辆之间的相对位置信息；
响应于第二驾驶请求，显示第三图像，该第三图像包括第一位置对应的至少部分环境、以及服务器所分配的目标车辆的车辆信息；
响应于第一驾驶请求或第二驾驶请求，显示第四图像，该第四图像包括第一位置对应的至少部分环境、以及该目标环境的状态数据，该状态数据包括该第一位置对应的至少部分环境的气象数据、光照强度或目标环境中状态可变对象的当前状态中的至少一项。
在一个可能实现方式中，该第一显示模块，响应于远程驾驶请求，在显示第一环境图像之前，还用于以下至少一项：
响应于第一驾驶请求或第二驾驶请求，从服务器中接收目标环境的全局场景数据或第一场景数据中的至少一项，该第一场景数据是与该第一位置对应的至少部分环境的局部场景数据；
响应于第一驾驶请求或第二驾驶请求，从服务器中接收目标环境所对应的各个车辆的位置信息，基于该各个车辆的位置信息，确定该目标车辆的各个周边车辆；
响应于第一驾驶请求或第二驾驶请求，从服务器中接收目标环境所对应的各个车辆的行驶状态和位置信息，基于该各个车辆的位置信息和行驶状态，确定该各个周边车辆与该目标车辆的相对位置信息和相对行驶状态；
响应于第二驾驶请求，从服务器中接收所分配的目标车辆的车辆信息；
响应于第一驾驶请求或第二驾驶请求，从服务器中接收目标环境的状态数据，该状态数据包括该第一位置的气象数据、光照强度或目标环境中状态可变的第一对象的当前状态中的至少一项。
在一个可能实现方式中，该远程驾驶实体至少包括第一分屏和第二分屏；
该第二显示模块，用于：
在该第一分屏中显示该第二环境图像；
该装置还包括：
第一预测模块，用于基于该当前所在位置和该目标车辆的行驶状态，预测该目标车辆在下一时刻的环境位置；
第五显示模块，用于在该第二分屏中，显示该目标车辆在该下一时刻的环境位置所对应的第三环境图像。
在一个可能实现方式中，该装置还包括：
第二预测模块，用于基于该目标环境和已获取的该目标车辆的行进状态信息，对该目标车辆的当前时刻的位置进行预测，得到预测位置；
获取模块，用于基于该预测位置，获取该全局场景数据中与该预测位置对应的局部场景数据、以及获取该预测位置对应的至少部分环境的状态数据；
渲染模块，用于基于该预测位置对应的局部场景数据和状态数据，渲染得到该预测位置对应的渲染后的图像数据。
在一个可能实现方式中，该第二显示模块，用于：
若该所预测位置与从目标车辆中获取的当前所在位置匹配，基于该预测位置对应的渲染后的图像数据显示该第二环境图像；
若该当前环境位置与从目标车辆中获取的当前所在位置不匹配，获取该当前所在位置所对应的局部场景数据和状态数据，并基于该当前所在位置所对应的局部场景数据和状态数据渲染得到该第二环境图像。
在一个可能实现方式中，该目标环境中行驶有与该目标车辆具备关联作业关系的关联作业车辆；
该装置还包括：
第六显示模块，用于显示驾驶辅助信息，该驾驶辅助信息包括以下至少一项：
该关联作业车辆与该目标车辆之间的相对位置信息；
该关联作业车辆的作业状态和该目标车辆的作业状态；
该关联作业车辆与该目标车辆之间的相对作业进度；
该关联作业车辆与该目标车辆之间的相对工况信息。
在一个可能实现方式中，该目标车辆是该远程驾驶实体控制的多个被控车辆中的任一个；
该装置还包括：
位置信息获取模块，用于获取该目标环境中非被控对象的位置信息、以及目标车辆的周边被控车辆的位置信息；
路况统计模块，用于基于该各个非被控对象的位置信息、以及该目标车辆的周边被控车辆的位置信息，统计目标车辆的周围环境的路况信息；
提示模块，用于响应于该目标车辆的路况信息符合预设条件，显示提示信息，该提示信息用于提示符合自动驾驶情况
自动驾驶启动模块，用于响应于接收到针对目标车辆的自动驾驶启动操作，启动该目标车辆的自动驾驶功能。
本申请提供的远程驾驶方法，通过显示包括第一位置对应的至少部分环境的第一环境图像，由于预先基于目标环境预先构建有全局场景数据；因此，可直接基于第一位置对应的局部场景数据生成第一环境图像；当有对目标车辆的车辆行驶操作时，可利用当前所在位置对应的局部场景数据，生成并显示当前所在位置所在环境的第二环境图像；通过利用局部场景数据结合车辆位置，即可实现车辆周边环境的展示；无需目标车辆实时传输拍摄的周围环境的视频，大幅降低了远程驾驶过程中数据传输占用的带宽；有效解决了基于实时视频传输画面进行远程驾驶所导致的带宽占用过高的问题；降低对网络带宽需求，有助于提高远程驾驶的稳定性，使得驾驶员可稳定、低延时地控制车辆，提高实际驾驶效率。
图9为本申请实施例提供的一种远程驾驶装置结构示意图。该装置应用于服务器，如图9所示，该装置包括：
第一发送模块901，用于响应于接收到远程驾驶实体的远程驾驶请求，向该远程驾驶实体发送目标车辆的第一位置和第一位置对应的局部场景数据，该第一位置对应的局部场景数据是目标车辆在第一位置时对应于目标环境的至少部分环境的场景数据；
第二发送模块902，用于响应于接收到该远程驾驶实体的行驶指令，向目标车辆发送该行驶指令，该行驶指令是基于在远程驾驶实体中针对目标车辆的车辆行驶操作对应的指令；
第三发送模块903，用于响应于接收到目标车辆基于该行驶指令行驶过程中的当前所在位置，向该远程驾驶实体发送该目标车辆的当前所在位置。
本申请提供的远程驾驶方法，通过显示包括第一位置对应的至少部分环境的第一环境图像，由于预先基于目标环境预先构建有全局场景数据；因此，可直接基于第一位置对应的局部场景数据生成第一环境图像；当有对目标车辆的车辆行驶操作时，可利用当前所在位置对应的局部场景数据， 生成并显示当前所在位置所在环境的第二环境图像；通过利用局部场景数据结合车辆位置，即可实现车辆周边环境的展示；无需目标车辆实时传输拍摄的周围环境的视频，大幅降低了远程驾驶过程中数据传输占用的带宽；有效解决了基于实时视频传输画面进行远程驾驶所导致的带宽占用过高的问题；降低对网络带宽需求，有助于提高远程驾驶的稳定性，使得驾驶员可稳定、低延时地控制车辆，提高实际驾驶效率。
本申请实施例的装置可执行本申请实施例所提供的方法，其实现原理相类似，本申请各实施例的装置中的各模块所执行的动作是与本申请各实施例的方法中的步骤相对应的，对于装置的各模块的详细功能描述具体可以参见前文中所示的对应方法中的描述，此处不再赘述。
图10是本申请实施例中提供了一种电子设备的结构示意图。如图10所示，该电子设备包括：存储器、处理器及存储在存储器上的计算机程序，该处理器执行上述计算机程序以实现远程驾驶方法的步骤，与相关技术相比可实现：
本申请提供的远程驾驶方法，通过显示包括第一位置对应的至少部分环境的第一环境图像，由于预先基于目标环境预先构建有全局场景数据；因此，可直接基于第一位置对应的局部场景数据生成第一环境图像；当有对目标车辆的车辆行驶操作时，可利用当前所在位置对应的局部场景数据，生成并显示当前所在位置所在环境的第二环境图像；通过利用局部场景数据结合车辆位置，即可实现车辆周边环境的展示；无需目标车辆实时传输拍摄的周围环境的视频，大幅降低了远程驾驶过程中数据传输占用的带宽；有效解决了基于实时视频传输画面进行远程驾驶所导致的带宽占用过高的问题；降低对网络带宽需求，有助于提高远程驾驶的稳定性，使得驾驶员可稳定、低延时地控制车辆，提高实际驾驶效率。
本申请实施例中提供了一种电子设备，如图10所示，图10所示的电子设备1000包括：处理器1001和存储器1003。其中，处理器1001和存储器1003相连，如通过总线1002相连。电子设备1000还可以包括收发器1004，收发器1004可以用于该电子设备与其他电子设备之间的数据交互，如数据的发送和/或数据的接收等。需要说明的是，实际应用中收发器1004不限于一个，该电子设备1000的结构并不构成对本申请实施例的限定。
处理器1001可以是CPU(Central Processing Unit，中央处理器)，通用处理器，DSP(Digital Signal Processor，数据信号处理器)，ASIC(Application Specific Integrated Circuit，专用集成电路)，FPGA(Field Programmable Gate Array，现场可编程门阵列)或者其他可编程逻辑器件、晶体管逻辑器件、硬件部件或者其任意组合。其可以实现或执行结合本申请公开内容所描述的各种示例性的逻辑方框，模块和电路。处理器1001也可以是实现计算功能的组合，例如包含一个或多个微处理器组合，DSP和微处理器的组合等。
总线1002可包括一通路，在上述组件之间传送信息。总线1002可以是PCI(Peripheral Component Interconnect，外设部件互连标准)总线或EISA(Extended Industry Standard Architecture，扩展工业标准结构)总线等。总线1002可以分为地址总线、数据总线、控制总线等。为便于表示，图10中仅用一条粗线表示，但并不表示仅有一根总线或一种类型的总线。
存储器1003可以是ROM(Read Only Memory，只读存储器)或可存储静态信息和指令的其他类型的静态存储设备，RAM(Random Access Memory，随机存取存储器)或者可存储信息和指令的其他类型的动态存储设备，也可以是EEPROM(Electrically Erasable Programmable Read Only Memory，电可擦可编程只读存储器)、CD-ROM(Compact Disc Read Only Memory，只读光盘)或其他光盘存储、光碟存储(包括压缩光碟、激光碟、光碟、数字通用光碟、蓝光光碟等)、磁盘存储介质\其他磁存储设备、或者能够用于携带或存储计算机程序并能够由计算机读取的任何其他介质，在此不做限定。
存储器1003用于存储执行本申请实施例的计算机程序，并由处理器1001来控制执行。处理器1001用于执行存储器1003中存储的计算机程序，以实现前述方法实施例所示的步骤。
其中，电子设备包括但不限于：服务器、终端或云计算中心设备、远程驾驶实体、驾驶模拟舱等。
本申请实施例提供了一种计算机可读存储介质，该计算机可读存储介质上存储有计算机程序，计算机程序被处理器执行时可实现前述方法实施例的步骤及相应内容。
本申请实施例还提供了一种计算机程序产品，包括计算机程序，计算机程序被处理器执行时可实现前述方法实施例的步骤及相应内容。
本技术领域技术人员可以理解，除非特意声明，这里使用的单数形式“一”、“一个”、“所述”和“该”也可包括复数形式。本申请实施例所使用的术语“包括”以及“包含”是指相应特征可以实现为所呈现的特征、信息、数据、步骤、操作，但不排除实现为本技术领域所支持其他特征、信息、数据、步骤、操作等。
本申请的说明书和权利要求书及上述附图中的术语“第一”、“第二”、“第三”、“第四”、“1”、“2”等(如果存在)是用于区别类似的对象，而不必用于描述特定的顺序或先后次序。应该理解这样使用的数据在适当情况下可以互换，以便这里描述的本申请的实施例能够以除图示或文字描述以外的顺序实施。
应该理解的是，虽然本申请实施例的流程图中通过箭头指示各个操作步骤，但是这些步骤的实施顺序并不受限于箭头所指示的顺序。除非本文中有明确的说明，否则在本申请实施例的一些实施场景中，各流程图中的实施步骤可以按照需求以其他的顺序执行。此外，各流程图中的部分或全部步骤基于实际的实施场景，可以包括多个子步骤或者多个阶段。这些子步骤或者阶段中的部分或全部可以在同一时刻被执行，这些子步骤或者阶段中的每个子步骤或者阶段也可以分别在不同的时刻被执行。在执行时刻不同的场景下，这些子步骤或者阶段的执行顺序可以根据需求灵活配置，本申请实施例对此不限制。
以上所述仅是本申请部分实施场景的实施方式，应当指出，对于本技术领域的普通技术人员来说，在不脱离本申请的方案技术构思的前提下，采用基于本申请技术思想的其他类似实施手段，同样属于本申请实施例的保护范畴。

Claims (20)

  1. 一种远程驾驶方法，所述方法应用于远程驾驶实体，所述方法包括：
    响应于远程驾驶请求，显示目标车辆对应的第一环境图像，所述第一环境图像包括所述目标车辆在第一位置时对应的目标环境的至少部分环境的图像；所述第一环境图像是基于预先构建的目标环境的全局场景数据中第一位置对应的局部场景数据生成的；
    响应于驾驶员针对所述目标车辆的车辆行驶操作，显示所述目标车辆对应的第二环境图像，所述第二环境图像包括所述目标车辆当前所在位置对应的目标环境的至少部分环境的图像。
  2. 根据权利要求1所述的方法，其中，所述方法还包括以下至少一项：
    响应于第一驾驶触发操作，显示远程配置页面，接收针对至少一个候选车辆中目标车辆的选择操作；所述远程配置页面中显示有所述至少一个候选车辆的车辆信息；所述远程驾驶请求是基于选择操作所触发的第一驾驶请求；
    响应于第二驾驶触发操作，显示信息录入页面，接收基于录入控件触发的驾驶员信息录入操作；所述信息录入页面中显示有用于录入驾驶员信息的所述录入控件；所述远程驾驶请求是基于驾驶员信息录入操作所触发的第二驾驶请求。
  3. 根据权利要求1或2所述的方法，其中，所述响应于远程驾驶请求，显示目标车辆对应的第一环境图像，包括以下至少一项：
    响应于第一驾驶请求或第二驾驶请求，显示第一图像，所述第一图像包括第一位置对应的至少部分环境、以及目标车辆的各个周边车辆；
    响应于第一驾驶请求或第二驾驶请求，显示第二图像，所述第二图像包括第一位置对应的至少部分环境、以及各个周边车辆和各个周边车辆与所述目标车辆之间的相对位置信息；
    响应于第二驾驶请求，显示第三图像，所述第三图像包括第一位置对应的至少部分环境、以及服务器所分配的目标车辆的车辆信息；
    响应于第一驾驶请求或第二驾驶请求，显示第四图像，所述第四图像包括第一位置对应的至少部分环境、以及所述目标环境的状态数据，所述状态数据包括所述第一位置对应的至少部分环境的气象数据、光照强度或目标环境中状态可变对象的当前状态中的至少一项。
  4. 根据权利要求1或2所述的方法，其中，所述响应于远程驾驶请求，在显示第一环境图像之前，执行以下至少一项：
    响应于第一驾驶请求或第二驾驶请求，从服务器中接收目标环境的全局场景数据或第一场景数据中的至少一项，所述第一场景数据是与所述第一位置对应的至少部分环境的局部场景数据；
    响应于第一驾驶请求或第二驾驶请求，从服务器中接收目标环境所对应的各个车辆的位置信息，基于所述各个车辆的位置信息，确定所述目标车辆的各个周边车辆；
    响应于第一驾驶请求或第二驾驶请求，从服务器中接收目标环境所对应的各个车辆的行驶状态和位置信息，基于所述各个车辆的位置信息和行驶状态，确定所述各个周边车辆与所述目标车辆的相对位置信息和相对行驶状态；
    响应于第二驾驶请求，从服务器中接收所分配的目标车辆的车辆信息；
    响应于第一驾驶请求或第二驾驶请求，从服务器中接收目标环境的状态数据，所述状态数据包括所述第一位置的气象数据、光照强度或目标环境中状态可变的第一对象的当前状态中的至少一项。
  5. 根据权利要求1所述的方法，其中，所述远程驾驶实体至少包括第一分屏和第二分屏；
    所述显示所述目标车辆对应的第二环境图像，包括：
    在所述第一分屏中显示所述第二环境图像；
    所述方法还包括：
    基于所述当前所在位置和所述目标车辆的行驶状态，预测所述目标车辆在下一时刻的环境位置；
    在所述第二分屏中，显示所述目标车辆在所述下一时刻的环境位置所对应的第三环境图像。
  6. 根据权利要求1所述的方法，其中，所述响应于驾驶员针对所述目标车辆的车辆行驶操作， 显示所述目标车辆对应的第二环境图像之前，所述方法还包括：
    基于所述目标环境和已获取的所述目标车辆的行进状态信息，对所述目标车辆的当前时刻的位置进行预测，得到预测位置；
    基于所述预测位置，获取所述全局场景数据中与所述预测位置对应的局部场景数据、以及获取所述预测位置对应的至少部分环境的状态数据；
    基于所述预测位置对应的局部场景数据和状态数据，渲染得到所述预测位置对应的渲染后的图像数据。
  7. 根据权利要求6所述的方法，其中，所述响应于驾驶员针对所述目标车辆的车辆行驶操作，显示所述目标车辆对应的第二环境图像，包括：
    若所述所预测位置与从目标车辆中获取的当前所在位置匹配，基于所述预测位置对应的渲染后的图像数据，显示所述第二环境图像；
    若所述当前环境位置与从目标车辆中获取的当前所在位置不匹配，获取所述当前所在位置所对应的局部场景数据和状态数据，并基于所述当前所在位置所对应的局部场景数据和状态数据渲染得到所述第二环境图像。
  8. 根据权利要求1所述的方法，其中，所述目标环境中行驶有与所述目标车辆具备关联作业关系的关联作业车辆；
    所述方法还包括：
    显示驾驶辅助信息，所述驾驶辅助信息包括以下至少一项：
    所述关联作业车辆与所述目标车辆之间的相对位置信息；
    所述关联作业车辆的作业状态和所述目标车辆的作业状态；
    所述关联作业车辆与所述目标车辆之间的相对作业进度；
    所述关联作业车辆与所述目标车辆之间的相对工况信息。
  9. 根据权利要求1所述的方法，其中，所述目标车辆是所述远程驾驶实体控制的多个被控车辆中的任一个；
    所述方法还包括：
    获取所述目标环境中非被控对象的位置信息、以及目标车辆的周边被控车辆的位置信息；
    基于所述各个非被控对象的位置信息、以及该目标车辆的周边被控车辆的位置信息，统计目标车辆的周围环境的路况信息；
    响应于所述目标车辆的路况信息符合预设条件，显示提示信息，所述提示信息用于提示符合自动驾驶情况；
    响应于接收到针对目标车辆的自动驾驶启动操作，启动所述目标车辆的自动驾驶功能。
  10. 一种远程驾驶方法，所述方法应用于服务器，所述方法包括：
    响应于接收到远程驾驶实体的远程驾驶请求，向所述远程驾驶实体发送目标车辆的第一位置和第一位置对应的局部场景数据，所述第一位置对应的局部场景数据是目标车辆在第一位置时对应于目标环境的至少部分环境的场景数据；
    响应于接收到所述远程驾驶实体的行驶指令，向目标车辆发送所述行驶指令，所述行驶指令是基于在远程驾驶实体中针对目标车辆的车辆行驶操作对应的指令；
    响应于接收到目标车辆基于所述行驶指令行驶过程中的当前所在位置，向所述远程驾驶实体发送所述目标车辆的当前所在位置。
  11. 一种远程驾驶装置，所述装置应用于远程驾驶实体，所述装置包括：
    第一显示模块，用于响应于远程驾驶请求，显示目标车辆对应的第一环境图像，所述第一环境图像包括所述目标车辆在第一位置时对应的目标环境的至少部分环境的图像；所述第一环境图像是基于预先构建的目标环境的全局场景数据中第一位置对应的局部场景数据生成的；
    第二显示模块，用于响应于驾驶员针对所述目标车辆的车辆行驶操作，显示所述目标车辆对应的第二环境图像，所述第二环境图像包括所述目标车辆当前所在位置对应的目标环境的至少部分环境的图像。
  12. 根据权利要求11所述的装置，其中，所述装置还包括以下至少一项：
    第三显示模块，用于响应于第一驾驶触发操作，显示远程配置页面，接收针对至少一个候选车辆中目标车辆的选择操作；所述远程配置页面中显示有所述至少一个候选车辆的车辆信息；所述远程驾驶请求是基于选择操作所触发的第一驾驶请求；
    第四显示模块，用于响应于第二驾驶触发操作，显示信息录入页面，接收基于录入控件触发的驾驶员信息录入操作；所述信息录入页面中显示有用于录入驾驶员信息的所述录入控件；所述远程驾驶请求是基于驾驶员信息录入操作所触发的第二驾驶请求。
  13. 根据权利要求11或12所述的装置，其中，所述第一显示模块，用于以下至少一项：
    响应于第一驾驶请求或第二驾驶请求，显示第一图像，所述第一图像包括第一位置对应的至少部分环境、以及目标车辆的各个周边车辆；
    响应于第一驾驶请求或第二驾驶请求，显示第二图像，所述第二图像包括第一位置对应的至少部分环境、以及各个周边车辆和各个周边车辆与所述目标车辆之间的相对位置信息；
    响应于第二驾驶请求，显示第三图像，所述第三图像包括第一位置对应的至少部分环境、以及服务器所分配的目标车辆的车辆信息；
    响应于第一驾驶请求或第二驾驶请求，显示第四图像，所述第四图像包括第一位置对应的至少部分环境、以及所述目标环境的状态数据，所述状态数据包括所述第一位置对应的至少部分环境的气象数据、光照强度或目标环境中状态可变对象的当前状态中的至少一项。
  14. 根据权利要求11或12所述的装置，其中，所述第一显示模块，响应于远程驾驶请求，在显示第一环境图像之前，执行以下至少一项：
    响应于第一驾驶请求或第二驾驶请求，从服务器中接收目标环境的全局场景数据或第一场景数据中的至少一项，所述第一场景数据是与所述第一位置对应的至少部分环境的局部场景数据；
    响应于第一驾驶请求或第二驾驶请求，从服务器中接收目标环境所对应的各个车辆的位置信息，基于所述各个车辆的位置信息，确定所述目标车辆的各个周边车辆；
    响应于第一驾驶请求或第二驾驶请求，从服务器中接收目标环境所对应的各个车辆的行驶状态和位置信息，基于所述各个车辆的位置信息和行驶状态，确定所述各个周边车辆与所述目标车辆的相对位置信息和相对行驶状态；
    响应于第二驾驶请求，从服务器中接收所分配的目标车辆的车辆信息；
    响应于第一驾驶请求或第二驾驶请求，从服务器中接收目标环境的状态数据，所述状态数据包括所述第一位置的气象数据、光照强度或目标环境中状态可变的第一对象的当前状态中的至少一项。
  15. 根据权利要求11所述的装置，其中，所述远程驾驶实体至少包括第一分屏和第二分屏；
    所述第二显示模块，用于：在所述第一分屏中显示所述第二环境图像；
    所述装置还包括：
    第一预测模块，用于基于所述当前所在位置和所述目标车辆的行驶状态，预测所述目标车辆在下一时刻的环境位置；
    第五显示模块，用于在所述第二分屏中，显示所述目标车辆在所述下一时刻的环境位置所对应的第三环境图像。
  16. 一种远程驾驶装置，所述装置应用于服务器，所述装置包括：
    第一发送模块，用于响应于接收到远程驾驶实体的远程驾驶请求，向所述远程驾驶实体发送目标车辆的第一位置和第一位置对应的局部场景数据，所述第一位置对应的局部场景数据是目标车辆在第一位置时对应于目标环境的至少部分环境的场景数据；
    第二发送模块，用于响应于接收到所述远程驾驶实体的行驶指令，向目标车辆发送所述行驶指令，所述行驶指令是基于在远程驾驶实体中针对目标车辆的车辆行驶操作对应的指令；
    第三发送模块，用于响应于接收到目标车辆基于所述行驶指令行驶过程中的当前所在位置，向所述远程驾驶实体发送所述目标车辆的当前所在位置。
  17. 一种远程驾驶实体，所述远程驾驶实体，包括处理器和显示器；
    其中，所述显示器用于实现权利要求1-3、5-9中任一项所述的远程驾驶方法；所述处理器用 于实现权利要求1至9中任一项所述的远程驾驶方法。
  18. 一种电子设备，包括存储器、处理器及存储在存储器上的计算机程序，所述处理器执行所述计算机程序以实现权利要求1至10中任一项所述的远程驾驶方法。
  19. 一种计算机可读存储介质，其上存储有计算机程序，所述计算机程序被处理器执行时实现权利要求1至10中任一项所述的远程驾驶方法。
  20. 一种计算机程序产品，包括计算机程序，所述计算机程序被处理器执行时实现权利要求1至10中任一项所述的远程驾驶方法。
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