WO2022121247A1 - 汽车协同决策方法、装置、电子设备及计算机存储介质 - Google Patents

汽车协同决策方法、装置、电子设备及计算机存储介质 Download PDF

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WO2022121247A1
WO2022121247A1 PCT/CN2021/096316 CN2021096316W WO2022121247A1 WO 2022121247 A1 WO2022121247 A1 WO 2022121247A1 CN 2021096316 W CN2021096316 W CN 2021096316W WO 2022121247 A1 WO2022121247 A1 WO 2022121247A1
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Prior art keywords
vehicle
decision
making
information
road
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PCT/CN2021/096316
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English (en)
French (fr)
Inventor
李克强
褚文博
熊秋池
乌尼日·其其格
黄冠富
杜孝平
Original Assignee
国汽(北京)智能网联汽车研究院有限公司
清华大学
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Application filed by 国汽(北京)智能网联汽车研究院有限公司, 清华大学 filed Critical 国汽(北京)智能网联汽车研究院有限公司
Priority to US17/623,550 priority Critical patent/US20230252895A1/en
Publication of WO2022121247A1 publication Critical patent/WO2022121247A1/zh

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    • 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
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
    • G06V20/54—Surveillance or monitoring of activities, e.g. for recognising suspicious objects of traffic, e.g. cars on the road, trains or boats
    • G—PHYSICS
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    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
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    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
    • G06V20/588—Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G08G1/0116—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from roadside infrastructure, e.g. beacons
    • G—PHYSICS
    • G08—SIGNALLING
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    • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
    • G08G1/0141—Measuring and analyzing of parameters relative to traffic conditions for specific applications for traffic information dissemination
    • G—PHYSICS
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    • G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow control
    • G—PHYSICS
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    • 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
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    • G08G1/096811—Systems involving transmission of navigation instructions to the vehicle where the transmitted instructions are used to compute a route where the route is computed offboard
    • H—ELECTRICITY
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    • H—ELECTRICITY
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    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H04W4/44—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]

Definitions

  • the present application belongs to the technical field of intelligent transportation, and in particular, relates to a vehicle collaborative decision-making method, device, electronic device and computer storage medium.
  • the vehicle collaborative decision-making method in related technologies mainly relies on image recognition to judge the driving intention and vehicle status of other unmanned vehicles around, but the existing delay and inaccuracy lead to low road usage and low safety level.
  • Embodiments of the present application provide a vehicle collaborative decision-making method, device, electronic device, and computer storage medium, which can improve road usage rate and safety level.
  • an embodiment of the present application provides a vehicle collaborative decision-making method, which is applied to a cloud server, including:
  • the collaborative decision-making request is a request sent by the roadside device after determining that the road is congested based on the acquired real-time information of the road;
  • the corresponding decision planning scheme is sent to each car, so that each car performs corresponding driving operations according to the respective decision planning scheme.
  • a multi-vehicle-oriented decision-making planning scheme including:
  • a multi-vehicle-oriented decision-making planning scheme is calculated using a preset scene collaborative decision-making model.
  • obtain real-time vehicle status information including:
  • obtain real-time vehicle status information including:
  • the road constraint information includes the allowable speed range of the vehicle, the number of lanes, the width of the lanes, and the feasible sections of the road.
  • the preset scenario collaborative decision-making model is a time slot allocation model designed based on a game theory matching method.
  • the decision planning scheme includes at least one of a driving priority allocation scheme, a vehicle route change scheme, and a vehicle next-state driving operation scheme.
  • an embodiment of the present application provides a vehicle collaborative decision-making device, which is applied to a cloud server, including:
  • a receiving module configured to receive a collaborative decision-making request sent by a roadside device, wherein the collaborative decision-making request is a request sent by the roadside device after determining that the road is congested based on the acquired real-time information of the road;
  • an acquisition module used to acquire the road scene information included in the collaborative decision-making request
  • the calculation module is used to calculate the multi-vehicle-oriented decision-making planning scheme based on the type of the congestion scene and using the preset scene collaborative decision-making model;
  • the sending module is used for respectively sending the corresponding decision planning scheme to each car, so that each car can perform the corresponding driving operation according to the respective decision planning scheme.
  • the calculation module includes:
  • an acquisition unit used to acquire road constraint information and vehicle real-time status information corresponding to the congestion scene type
  • the computing unit is used for computing a multi-vehicle-oriented decision planning scheme by using a preset scene collaborative decision-making model based on road constraint information and vehicle real-time state information.
  • the acquisition unit includes:
  • the first receiving subunit is used to receive at least one of vehicle speed, acceleration, angular velocity, wheel steering, braking information, relative distance to the front/rear vehicle, destination information, and planned route travel ratio sent by each vehicle. kind of information.
  • the acquisition unit includes:
  • the second receiving subunit is configured to receive at least one kind of information from traffic light status information, vehicle front and rear relative sequence information, and pedestrian location information in the road scene sent by the roadside device.
  • the road constraint information includes the allowable speed range of the vehicle, the number of lanes, the width of the lanes, and the feasible sections of the road.
  • the preset scenario collaborative decision-making model is a time slot allocation model designed based on a game theory matching device.
  • the decision planning scheme includes at least one of a driving priority allocation scheme, a vehicle route change scheme, and a vehicle next-state driving operation scheme.
  • an embodiment of the present application provides an electronic device, including: a processor and a memory storing computer program instructions;
  • the vehicle collaborative decision-making method as shown in the first aspect is implemented.
  • an embodiment of the present application provides a computer storage medium, where computer program instructions are stored thereon, and when the computer program instructions are executed by a processor, the vehicle collaborative decision-making method shown in the first aspect is implemented.
  • the vehicle collaborative decision-making method, device, electronic device, and computer storage medium of the embodiments of the present application can improve road usage rate and safety level.
  • the vehicle collaborative decision-making method, applied to a cloud server includes: receiving a collaborative decision-making request sent by a roadside device, wherein the collaborative decision-making request is a request sent by the roadside device after determining that the road is congested based on the acquired real-time information of the road; obtaining The road scene information included in the collaborative decision-making request; based on the road scene information, determine the type of the congestion scene; based on the type of the congestion scene, use the preset scene collaborative decision-making model to calculate the multi-vehicle-oriented decision-making plan; send the corresponding decision-making plan to each car respectively scheme, so that each car performs the corresponding driving operation according to its own decision planning scheme.
  • the cloud server is used to generate a multi-vehicle-oriented decision-making planning scheme, and the corresponding decision-making planning scheme is sent to each car, so that each car can perform the corresponding driving operation according to the respective decision-making planning scheme, so the road usage can be improved. rate and safety level.
  • FIG. 1 is a schematic flowchart of a vehicle collaborative decision-making method provided by an embodiment of the present application
  • FIG. 2 is a schematic diagram of a process of collaborative decision-making for vehicles provided by an embodiment of the present application
  • FIG. 3 is a schematic diagram of an example of a system architecture to which the vehicle collaborative decision-making method provided by an embodiment of the present application can be applied;
  • FIG. 4 is a schematic diagram of another example of a system architecture to which the vehicle collaborative decision-making method provided by the embodiment of the present application can be applied;
  • FIG. 5 is a schematic structural diagram of a vehicle collaborative decision-making device provided by an embodiment of the present application.
  • FIG. 6 is a schematic structural diagram of an electronic device provided by an embodiment of the present application.
  • the vehicle collaborative decision-making method in related technologies mainly relies on image recognition to judge the driving intention and vehicle status of other unmanned vehicles around, but the existing delay and inaccuracy lead to low road usage and low safety level.
  • the embodiments of the present application provide a method, apparatus, electronic device, and computer storage medium for collaborative decision-making of automobiles.
  • the vehicle collaborative decision-making method provided by the embodiments of the present application will be introduced below.
  • FIG. 1 shows a schematic flowchart of a vehicle collaborative decision-making method provided by an embodiment of the present application.
  • the vehicle collaborative decision-making method is applied to the cloud server, which can include:
  • S101 Receive a collaborative decision-making request sent by a roadside device, where the collaborative decision-making request is a request sent by the roadside device after determining that the road is congested based on the acquired real-time information of the road;
  • S104 Based on the congestion scenario type, use a preset scenario collaborative decision-making model to calculate a multi-vehicle-oriented decision-making planning scheme;
  • S105 Send the corresponding decision planning scheme to each vehicle, so that each vehicle performs the corresponding driving operation according to the respective decision planning scheme.
  • a preset scenario collaborative decision-making model to calculate a multi-vehicle-oriented decision-making planning scheme, including:
  • a multi-vehicle-oriented decision-making planning scheme is calculated using a preset scene collaborative decision-making model.
  • acquiring real-time vehicle status information includes:
  • acquiring real-time vehicle status information includes:
  • the road constraint information includes the allowable speed range of the vehicle, the number of lanes, the width of the lane, and the feasible section of the road.
  • the preset scenario collaborative decision-making model is a time slot allocation model designed based on a game theory matching method.
  • the decision planning scheme includes at least one of a driving priority allocation scheme, a vehicle routing scheme, and a vehicle next-state driving operation scheme.
  • the vehicle collaborative decision-making method applied to a cloud server, includes: receiving a collaborative decision-making request sent by a roadside device, wherein the collaborative decision-making request is a request sent by the roadside device after determining that the road is congested based on the acquired real-time information of the road; obtaining The road scene information included in the collaborative decision-making request; based on the road scene information, determine the type of the congestion scene; based on the type of the congestion scene, use the preset scene collaborative decision-making model to calculate the multi-vehicle-oriented decision-making plan; send the corresponding decision-making plan to each car respectively scheme, so that each car performs the corresponding driving operation according to its own decision planning scheme.
  • this method uses the cloud server to generate a decision-making planning scheme for multiple vehicles, and sends the corresponding decision-making planning scheme to each car, so that each car can perform the corresponding driving operation according to its own decision-making planning scheme, so it can improve the road utilization rate and Security Level.
  • FIG. 2 is a schematic diagram of a process of vehicle collaborative decision-making provided by an embodiment of the present application.
  • the process of collaborative decision-making for vehicles may include: roadside equipment perceives real-time road information, determines that the road is congested, and sends a collaborative decision-making request to the cloud server; the cloud server receives the decision request and obtains the first scene information, Determine the type of the congestion scenario; obtain the required decision-making information according to the type of the congestion scenario, use the preset collaborative decision-making model to calculate the multi-vehicle decision-making planning scheme; distribute the decision-making planning scheme to the corresponding vehicle to complete the multi-vehicle collaborative decision-making suggestion.
  • the process of the vehicle collaborative decision-making may include the following steps:
  • the roadside equipment senses the real-time information of the road, analyzes the real-time information of the road to determine that the road is congested, and sends a collaborative decision-making request to the cloud server.
  • the cloud server side receives the collaborative decision-making request, obtains the first information of the scene, analyzes the road model, and specifies the scene decision-making model for decision-making.
  • the scene decision model obtains road constraint information and real-time vehicle data, and calculates a multi-vehicle-oriented decision-making plan according to the preset scene collaborative decision-making model.
  • Vehicle real-time data consists of real-time status information obtained from the vehicle end, road end and the cloud, wherein the information obtained by the vehicle end includes but is not limited to the vehicle speed, acceleration, angular velocity, Wheel steering, braking information, relative distance to the front/rear vehicle, destination information, driving ratio of the planned route, etc.
  • the information obtained by the roadside includes but is not limited to the traffic lights uploaded to the cloud by all roadside ends within the scope of the scene state, the relative order of vehicles before and after, the position of pedestrians in the road scene, etc.
  • the road constraint information includes, but is not limited to, the allowable speed range of the vehicle, the number and width of lanes, and feasible sections of the road.
  • decision-making models include but are not limited to decision-making rules based on expert rules and experience using fuzzy logic, multi-agent decision-making training models based on reinforcement learning, time slot allocation models designed based on game theory matching methods, etc. .
  • the specific content of the decision-making planning scheme includes but is not limited to the driving priority allocation scheme of all or some vehicles within the scene area, the vehicle path change scheme, and the best driving actions of the vehicle in the next state such as shifting, braking, lane changing, etc. , the specific presentation form of the scheme is not limited.
  • the decision-making planning scheme is distributed to the vehicle end to provide decision-making suggestions.
  • the roadside equipment perceives the real-time road condition information of the road, and sends a collaborative decision-making request to the cloud according to the road congestion situation.
  • the cloud matches the collaborative decision-making scene according to the road model, and then obtains the multi-vehicle driving data and uses the corresponding collaborative decision-making model. Analyze and plan the multi-vehicle decision-making planning scheme, and finally distribute the decision-making planning scheme to the corresponding vehicle end.
  • This process can complete unified decision-making and planning in the cloud based on the multi-vehicle driving data, and provide decision-making suggestions for all vehicles in the area, thereby reducing the computing performance requirements on the vehicle and road ends, while improving the traffic efficiency and the safety of ICVs. Level, suitable for intelligent networked vehicle group decision-making scenarios.
  • FIG. 3 An example of a system architecture to which the vehicle collaborative decision-making method provided by the embodiments of the present application can be applied is shown in FIG. 3 .
  • the server 301 and the intelligent roadside equipment 302 can communicate with each other in wired and wireless ways, the cloud server 301 and the intelligent networked vehicles 303 and 304 can communicate with each other in wired and wireless ways, and the roadside equipment 302 can communicate through various The sensors perceive the driving situation of the intelligent networked vehicles 303 and 304 .
  • the cloud server includes one or more information transceiver devices, computing processing devices, and storage devices, and communication between the devices can be performed through wired or wireless connections.
  • the cloud server calculates a set of optimal decision-making plans for all vehicles in the decision-making scene by receiving collaborative decision-making requests and vehicle-side information sent by roadside equipment, and provides decision-making advice services for each ICV.
  • the roadside equipment obtains the driving situation of all vehicles in the area through various sensors, calculates the congestion coefficient in the area through the calculation and processing device, and uses the information transceiver device to send a collaborative decision request to the cloud according to the congestion coefficient.
  • Both the ICV 303 and ICV 304 are equipped with an on-board intelligent computer to transmit driving information such as their own vehicle speed and planned route to the cloud server, and at the same time receive decision suggestions from the cloud server.
  • the in-vehicle intelligent computer can also plan the driving decision-making plan of the vehicle according to the perception information of the vehicle and the decision-making suggestions of the cloud server.
  • FIG. 3 The number of ICVs and the number of roadside devices in the above-mentioned FIG. 3 is only illustrative, and there may be any number of ICVs and roadside devices according to implementation needs.
  • FIG. 4 another example of the system architecture to which the vehicle collaborative decision-making method provided by the embodiment of the present application can be applied is shown in FIG. 4 , there are n intelligent networked vehicles in the intersection entrance scene; A device, a data processing device and an information sending device; the cloud server includes an information acquiring device, a scene judging device, n scene decision-making units and a decision-making distribution device.
  • Step 1 The roadside equipment at the intersection of the intersection obtains the real-time information of the main road and the intersection of the road, analyzes the road congestion coefficient, and sends a collaborative decision-making request.
  • the roadside equipment includes a sensing device, a data processing device and an information sending device pre-installed on it.
  • the sensing device at least includes sensors such as lidar and camera to collect the number and position of vehicles in the main road and the intersection of the road
  • the data processing device obtains the road congestion factor by calculating the vehicle density in the road section and the average speed of multi-vehicle driving.
  • the roadside equipment data processing device determines that the congestion coefficient reaches a preset threshold, and sends a collaborative decision request to the cloud server through the information sending device.
  • the information sending device can send a request to the cloud server through wired connection or wireless connection, and the specific connection forms include but are not limited to 3G/4G/5G connection, WIFI connection, cable or optical cable connection, and other known or Future development of wireless connectivity.
  • Step 2 The cloud server side receives the collaborative decision request, obtains the first information of the scene to which the roadside equipment belongs, and matches the scene decision unit to perform decision calculation.
  • the cloud server side includes an information acquisition device, a scene discrimination device, a plurality of scene decision units suitable for different road scenes, and a decision distribution device.
  • the information acquisition device can receive various types of information from the vehicle end or the roadside end in a wired connection or wireless connection, and the information acquisition device responds to the collaborative decision request received from the roadside equipment at the intersection,
  • the side terminal receives the scene first information, and transmits it to the scene discrimination device through wired connection or wireless connection.
  • the first information of the scene includes the number of lanes of the main road, the latitude and longitude of the location where the roadside device is located, etc., and is obtained from the roadside device through a wired or wireless connection.
  • the scene discriminating device stores a plurality of preset collaborative decision-making scene models and road network map models with road section annotations, and the scene discriminating device performs the pre-set scene model and the road network map model according to the first scene information.
  • the calculation and decision-making instructions are sent to the scene decision-making unit suitable for processing the collaborative decision-making of intersections and vehicles through wireless connection or limited connection.
  • Step 3 The scene decision unit responds to the calculation decision instruction, obtains the information required for the decision, and analyzes and calculates the multi-vehicle decision planning scheme.
  • the scene decision unit communicates with the above-mentioned information acquisition device through wired connection or wireless connection, and acquires required road restriction information and vehicle real-time data through the information acquisition device.
  • the required road restriction information includes the speed limit information of the main road and the speed limit information of the interchange lane received from the roadside end by the information acquisition device;
  • the required real-time vehicle information includes the perception range from the roadside equipment by the information acquisition device Real-time data such as vehicle planning path, driving speed, acceleration, and angular velocity obtained by each vehicle terminal in the system.
  • the scene decision-making unit includes a preset collaborative decision-making model suitable for the intersection entry scene, analyzes the real-time information of the vehicle under the condition that the road constraint information is satisfied, and calculates the multi-points in the intersection entry scene according to the collaborative decision-making model.
  • the vehicle decision planning scheme is transmitted to the decision distribution device.
  • the multi-vehicle decision planning scheme includes suggestions for driving behaviors such as acceleration, deceleration, braking, lane change, etc. for each vehicle in the main road and the merge lane within the sensing range of the roadside equipment.
  • Step 4 The multi-vehicle collaborative import decision-making planning scheme is distributed to the corresponding vehicle end to provide decision-making suggestions.
  • the decision distribution device receives the decision planning scheme from the scene decision unit through wired connection or wireless connection, decomposes the decision planning scheme according to the vehicle characteristics of the decision flow in the scheme, forms a single vehicle decision suggestion and distributes it to the corresponding On the vehicle side, complete the multi-vehicle collaborative decision-making process in the scene of the intersection.
  • the present application further provides a vehicle collaborative decision-making device.
  • the vehicle collaborative decision-making device 500 is applied to the cloud server, including:
  • a receiving module 501 configured to receive a collaborative decision-making request sent by a roadside device, wherein the collaborative decision-making request is a request sent by the roadside device after determining that the road is congested based on the acquired real-time information of the road;
  • an obtaining module 502 configured to obtain road scene information included in the collaborative decision-making request
  • a determination module 503, configured to determine the congestion scene type based on the road scene information
  • a calculation module 504 configured to calculate a multi-vehicle-oriented decision planning scheme by using a preset scene collaborative decision-making model based on the congestion scene type;
  • the sending module 505 is configured to send the corresponding decision planning scheme to each car respectively, so that each car performs the corresponding driving operation according to the respective decision planning scheme.
  • the computing module 504 includes:
  • an acquisition unit used to acquire road constraint information and vehicle real-time status information corresponding to the type of congestion scene
  • the computing unit is used for computing a multi-vehicle-oriented decision planning scheme by using a preset scene collaborative decision-making model based on road constraint information and vehicle real-time state information.
  • the acquisition unit includes:
  • the first receiving sub-unit is used to receive at least one of vehicle speed, acceleration, angular velocity, wheel steering, braking information, relative distance to the front/rear vehicle, destination information, and planned route travel ratio sent by each vehicle. kind of information.
  • the acquisition unit includes:
  • the second receiving subunit is configured to receive at least one kind of information from traffic light status information, vehicle front and rear relative sequence information, and pedestrian location information in the road scene sent by the roadside device.
  • the road constraint information includes the allowable speed range of the vehicle, the number of lanes, the width of the lanes, and the feasible sections of the road.
  • the preset scenario collaborative decision-making model is a time slot allocation model designed based on a game theory matching device.
  • the decision planning scheme includes at least one of a driving priority allocation scheme, a vehicle route change scheme, and a vehicle next-state driving operation scheme.
  • Each module/unit in the device shown in FIG. 5 has the function of implementing each step in FIG. 1 and can achieve its corresponding technical effect, and for the sake of brevity, it will not be repeated here.
  • FIG. 6 shows a schematic structural diagram of an electronic device provided by an embodiment of the present application.
  • the electronic device may include a processor 601 and a memory 602 storing computer program instructions.
  • the above-mentioned processor 601 may include a central processing unit (CPU), or a specific integrated circuit (Application Specific Integrated Circuit, ASIC), or may be configured to implement one or more integrated circuits of the embodiments of the present application.
  • CPU central processing unit
  • ASIC Application Specific Integrated Circuit
  • Memory 602 may include mass storage for data or instructions.
  • memory 602 may include a Hard Disk Drive (HDD), a floppy disk drive, a flash memory, an optical disk, a magneto-optical disk, a magnetic tape, or a Universal Serial Bus (USB) drive or two or more A combination of more than one of the above.
  • Memory 602 may include removable or non-removable (or fixed) media, where appropriate.
  • Memory 602 may be internal or external to the electronic device, where appropriate. In certain embodiments, memory 602 may be non-volatile solid state memory.
  • the memory 602 may be a read only memory (ROM).
  • the ROM may be a mask programmed ROM, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), electrically rewritable ROM (EAROM), or flash memory or both A combination of one or more of the above.
  • the processor 601 reads and executes the computer program instructions stored in the memory 602 to implement any one of the vehicle collaborative decision-making methods in the foregoing embodiments.
  • the electronic device may also include a communication interface 603 and a bus 610 .
  • the processor 601 , the memory 602 , and the communication interface 603 are connected through the bus 610 and complete the mutual communication.
  • the communication interface 603 is mainly used to implement communication between modules, apparatuses, units and/or devices in the embodiments of the present application.
  • the bus 610 includes hardware, software, or both, coupling the components of the online data flow metering device to each other.
  • the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, Enhanced Industry Standard Architecture (EISA) bus, Front Side Bus (FSB), HyperTransport (HT) Interconnect, Industry Standard Architecture (ISA) Bus, Infiniband Interconnect, Low Pin Count (LPC) Bus, Memory Bus, Microchannel Architecture (MCA) Bus, Peripheral Component Interconnect (PCI) Bus, PCI-Express (PCI-X) Bus, Serial Advanced Technology Attachment (SATA) bus, Video Electronics Standards Association Local (VLB) bus or other suitable bus or a combination of two or more of these.
  • Bus 610 may include one or more buses, where appropriate. Although embodiments herein describe and illustrate a particular bus, this application contemplates any suitable bus or interconnect.
  • embodiments of the present application may be implemented by providing a computer storage medium.
  • Computer program instructions are stored on the computer storage medium; when the computer program instructions are executed by the processor, any one of the vehicle collaborative decision-making methods in the foregoing embodiments is implemented.
  • Examples of computer storage media include non-transitory computer-readable storage media, such as ROM, random access memory (Random Access Memory, RAM), magnetic or optical disks, and the like.
  • the functional modules shown in the above-mentioned structural block diagrams can be implemented as hardware, software, firmware, or a combination thereof.
  • it may be, for example, an electronic circuit, an application specific integrated circuit (ASIC), suitable firmware, a plug-in, a function card, or the like.
  • ASIC application specific integrated circuit
  • elements of the present application are programs or code segments used to perform the required tasks.
  • the program or code segments may be stored in a machine-readable medium or transmitted over a transmission medium or communication link by a data signal carried in a carrier wave.
  • a "machine-readable medium” may include any medium that can store or transmit information.
  • machine-readable media examples include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, and the like.
  • the code segments may be downloaded via a computer network such as the Internet, an intranet, or the like.
  • processors may be, but are not limited to, general purpose processors, special purpose processors, application specific processors, or field programmable logic circuits. It will also be understood that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can also be implemented by special purpose hardware for performing the specified functions or actions, or by special purpose hardware and/or A combination of computer instructions is implemented.

Abstract

本申请提供了一种汽车协同决策方法、装置、电子设备及计算机存储介质。该汽车协同决策方法，应用于云服务器端，包括：接收路侧设备发送的协同决策请求，其中，协同决策请求是路侧设备基于获取到的道路实时信息确定道路发生拥堵后发送的请求；获取协同决策请求中附带的道路场景信息；基于道路场景信息，确定拥堵场景类型；基于拥堵场景类型，利用预设场景协同决策模型计算面向多车的决策规划方案；向各个汽车分别发送对应的决策规划方案，以使各个汽车依据各自的决策规划方案执行对应的驾驶操作。根据本申请实施例，能够提高道路使用率和安全等级。

Description

汽车协同决策方法、装置、电子设备及计算机存储介质
相关申请的交叉引用
本申请主张在2020年12月11日在中国提交的中国专利申请号202011439868.3的优先权，其全部内容通过引用包含于此。
技术领域
本申请属于智能交通技术领域，尤其涉及一种汽车协同决策方法、装置、电子设备及计算机存储介质。
背景技术
目前，相关技术中的汽车协同决策方法主要是车辆依靠图像识别判断周围其他无人驾驶车行车意图及车辆状况，但所存在的延时和不准确，导致道路使用率低和安全等级低。
因此，如何提高道路使用率和安全等级是本领域技术人员亟需解决的技术问题。
发明内容
本申请实施例提供一种汽车协同决策方法、装置、电子设备及计算机存储介质，能够提高道路使用率和安全等级。
第一方面，本申请实施例提供一种汽车协同决策方法，应用于云服务器端，包括：
接收路侧设备发送的协同决策请求，其中，协同决策请求是路侧设备基于获取到的道路实时信息确定道路发生拥堵后发送的请求；
获取协同决策请求中包括的道路场景信息；
基于道路场景信息，确定拥堵场景类型；
基于拥堵场景类型，利用预设场景协同决策模型计算面向多车的决策 规划方案；
向各个汽车分别发送对应的决策规划方案，以使各个汽车依据各自的决策规划方案执行对应的驾驶操作。
可选的，基于拥堵场景类型，利用预设场景协同决策模型计算面向多车的决策规划方案，包括：
获取拥堵场景类型对应的道路约束信息和车辆即时状态信息；
基于道路约束信息和车辆即时状态信息，利用预设场景协同决策模型计算面向多车的决策规划方案。
可选的，获取车辆即时状态信息，包括：
接收各个汽车发送的车辆速度、加速度、角速度、车轮转向、制动信息、与前/后车之间的相对距离、目的地信息、规划路径行驶比率中的至少一种信息。
可选的，获取车辆即时状态信息，包括：
接收路侧设备发送的交通灯状态信息、车辆前后相对次序信息、行人在道路场景中所处位置信息中的至少一种信息。
可选的，道路约束信息包括车辆允许速度范围、车道数、车道宽度、道路可行路段。
可选的，预设场景协同决策模型为基于博弈论匹配方法设计的时隙分配模型。
可选的，决策规划方案包括行驶优先级分配方案、车辆路径变更方案、车辆下一状态行驶操作方案中的至少一种方案。
第二方面，本申请实施例提供了一种汽车协同决策装置，应用于云服务器端，包括：
接收模块，用于接收路侧设备发送的协同决策请求，其中，协同决策请求是路侧设备基于获取到的道路实时信息确定道路发生拥堵后发送的请求；
获取模块，用于获取协同决策请求中包括的道路场景信息；
确定模块，用于基于道路场景信息，确定拥堵场景类型；
计算模块，用于基于拥堵场景类型，利用预设场景协同决策模型计算 面向多车的决策规划方案；
发送模块，用于向各个汽车分别发送对应的决策规划方案，以使各个汽车依据各自的决策规划方案执行对应的驾驶操作。
可选的，计算模块包括：
获取单元，用于获取拥堵场景类型对应的道路约束信息和车辆即时状态信息；
计算单元，用于基于道路约束信息和车辆即时状态信息，利用预设场景协同决策模型计算面向多车的决策规划方案。
可选的，获取单元包括：
第一接收子单元，用于接收各个汽车发送的车辆速度、加速度、角速度、车轮转向、制动信息、与前/后车之间的相对距离、目的地信息、规划路径行驶比率中的至少一种信息。
可选的，获取单元包括：
第二接收子单元，用于接收路侧设备发送的交通灯状态信息、车辆前后相对次序信息、行人在道路场景中所处位置信息中的至少一种信息。
可选的，道路约束信息包括车辆允许速度范围、车道数、车道宽度、道路可行路段。
可选的，预设场景协同决策模型为基于博弈论匹配装置设计的时隙分配模型。
可选的，决策规划方案包括行驶优先级分配方案、车辆路径变更方案、车辆下一状态行驶操作方案中的至少一种方案。
第三方面，本申请实施例提供了一种电子设备，包括：处理器以及存储有计算机程序指令的存储器；
处理器执行计算机程序指令时实现如第一方面所示的汽车协同决策方法。
第四方面，本申请实施例提供了一种计算机存储介质，计算机存储介质上存储有计算机程序指令，计算机程序指令被处理器执行时实现如第一方面所示的汽车协同决策方法。
本申请实施例的汽车协同决策方法、装置、电子设备及计算机存储介 质，能够提高道路使用率和安全等级。该汽车协同决策方法，应用于云服务器端，包括：接收路侧设备发送的协同决策请求，其中，协同决策请求是路侧设备基于获取到的道路实时信息确定道路发生拥堵后发送的请求；获取协同决策请求中包括的道路场景信息；基于道路场景信息，确定拥堵场景类型；基于拥堵场景类型，利用预设场景协同决策模型计算面向多车的决策规划方案；向各个汽车分别发送对应的决策规划方案，以使各个汽车依据各自的决策规划方案执行对应的驾驶操作。可见，本申请实施例利用云服务器生成面向多车的决策规划方案，向各个汽车分别发送对应的决策规划方案，以使各个汽车依据各自的决策规划方案执行对应的驾驶操作，故能够提高道路使用率和安全等级。
附图说明
为了更清楚地说明本申请实施例的技术方案，下面将对本申请实施例中所需要使用的附图作简单的介绍，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。
图1是本申请实施例提供的汽车协同决策方法的流程示意图；
图2是本申请实施例提供的汽车协同决策的过程示意图；
图3是可应用本申请实施例提供的汽车协同决策方法的系统架构的一种示例的示意图；
图4是可应用本申请实施例提供的汽车协同决策方法的系统架构的另一种示例的示意图；
图5是本申请实施例提供的汽车协同决策装置的结构示意图；
图6是本申请实施例提供的电子设备的结构示意图。
具体实施方式
下面将详细描述本申请的各个方面的特征和示例性实施例，为了使本申请的目的、技术方案及优点更加清楚明白，以下结合附图及具体实施例，对本申请进行进一步详细描述。应理解，此处所描述的具体实施例仅意在解释本申请，而不是限定本申请。对于本领域技术人员来说，本申请可以 在不需要这些具体细节中的一些细节的情况下实施。下面对实施例的描述仅仅是为了通过示出本申请的示例来提供对本申请更好的理解。
需要说明的是，在本文中，诸如第一和第二等之类的关系术语仅仅用来将一个实体或者操作与另一个实体或操作区分开来，而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。而且，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者设备不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者设备所固有的要素。在没有更多限制的情况下，由语句“包括……”限定的要素，并不排除在包括所述要素的过程、方法、物品或者设备中还存在另外的相同要素。
目前，相关技术中的汽车协同决策方法主要是车辆依靠图像识别判断周围其他无人驾驶车行车意图及车辆状况，但所存在的延时和不准确，导致道路使用率低和安全等级低。
为了解决现有技术问题，本申请实施例提供了一种汽车协同决策方法、装置、电子设备及计算机存储介质。下面首先对本申请实施例所提供的汽车协同决策方法进行介绍。
图1示出了本申请实施例提供的汽车协同决策方法的流程示意图。如图1所示，该汽车协同决策方法应用于云服务器端，可以包括：
S101：接收路侧设备发送的协同决策请求，其中，协同决策请求是路侧设备基于获取到的道路实时信息确定道路发生拥堵后发送的请求；
S102：获取协同决策请求中包括的道路场景信息；
S103：基于道路场景信息，确定拥堵场景类型；
S104：基于拥堵场景类型，利用预设场景协同决策模型计算面向多车的决策规划方案；
S105：向各个汽车分别发送对应的决策规划方案，以使各个汽车依据各自的决策规划方案执行对应的驾驶操作。
在一个实施例中，基于拥堵场景类型，利用预设场景协同决策模型计算面向多车的决策规划方案，包括：
获取拥堵场景类型对应的道路约束信息和车辆即时状态信息；
基于道路约束信息和车辆即时状态信息，利用预设场景协同决策模型计算面向多车的决策规划方案。
在一个实施例中，获取车辆即时状态信息，包括：
接收各个汽车发送的车辆速度、加速度、角速度、车轮转向、制动信息、与前/后车之间的相对距离、目的地信息、规划路径行驶比率中的至少一种信息。
在一个实施例中，获取车辆即时状态信息，包括：
接收路侧设备发送的交通灯状态信息、车辆前后相对次序信息、行人在道路场景中所处位置信息中的至少一种信息。
在一个实施例中，道路约束信息包括车辆允许速度范围、车道数、车道宽度、道路可行路段。
在一个实施例中，预设场景协同决策模型为基于博弈论匹配方法设计的时隙分配模型。
在一个实施例中，决策规划方案包括行驶优先级分配方案、车辆路径变更方案、车辆下一状态行驶操作方案中的至少一种方案。
该汽车协同决策方法，应用于云服务器端，包括：接收路侧设备发送的协同决策请求，其中，协同决策请求是路侧设备基于获取到的道路实时信息确定道路发生拥堵后发送的请求；获取协同决策请求中包括的道路场景信息；基于道路场景信息，确定拥堵场景类型；基于拥堵场景类型，利用预设场景协同决策模型计算面向多车的决策规划方案；向各个汽车分别发送对应的决策规划方案，以使各个汽车依据各自的决策规划方案执行对应的驾驶操作。可见，该方法利用云服务器生成面向多车的决策规划方案，向各个汽车分别发送对应的决策规划方案，以使各个汽车依据各自的决策规划方案执行对应的驾驶操作，故能够提高道路使用率和安全等级。
图2是本申请实施例提供的汽车协同决策的过程示意图。如图2所示，该汽车协同决策的过程可以包括：路侧设备感知道路实时信息，确定道路发生拥堵，向云服务器端发送协同决策请求；云服务器端接收决策请求，获取场景第一信息，判断拥堵场景类型；根据拥堵场景类型，获取所需决 策信息，使用预设协同决策模型，计算多车决策规划方案；将决策规划方案分发至对应车端，完成多车协同决策建议。具体地，该汽车协同决策的过程可以包括以下步骤：
1)路侧设备感知道路实时信息，根据道路实时信息分析确定道路发生拥堵，向云服务器端发送协同决策请求。
2)云服务器端接收协同决策请求，获取场景第一信息，分析道路模型，指定场景决策模型进行决策。
3)场景决策模型获取道路约束信息和车辆即时数据，依照预设场景协同决策模型，计算面向多车的决策规划方案。
3-1)车辆即时数据由从车端、路端和云端获取的即时状态信息组成，其中，车端获取信息包括但不限于场景范围内的所有车辆上传至云端的车辆速度、加速度、角速度、车轮转向、制动信息、与前/后车之间的相对距离、目的地信息、规划路径行驶比率等，路端获取信息包括但不限于场景范围内的所有路侧端上传至云端的交通灯状态、车辆前后相对次序、行人在道路场景中所处位置等。
3-2)道路约束信息包括但不限于车辆允许速度范围、车道数和车道宽度、道路可行路段等。
3-3)决策模型的具体形式包括但不限于使用模糊逻辑法基于专家规则和经验制定的决策规则，基于强化学习的多智能体决策训练模型，基于博弈论匹配方法设计的时隙分配模型等。
3-4)决策规划方案的具体内容包括但不限于场景区域范围内所有或部分车辆的行驶优先级分配方案、车辆路径变更方案、车辆下一状态最佳行驶动作如变速、刹车、变道等，方案具体展现形式不做限制。
4)决策规划方案分发至车端，提供决策建议。
该过程通过路侧设备感知道路实时路况信息，根据道路拥堵情况向云端发送协同决策请求，云端接收协同决策请求后根据道路模型进行协同决策场景匹配，然后获取多车行驶数据，使用对应协同决策模型分析规划多车决策规划方案，最后将决策规划方案分发至对应车端。该过程可以根据多车行驶数据在云端完成统一决策规划，对区域内所有车辆提供决策建议， 从而降低了对车端和路端的计算性能要求，同时提高了交通通行效率和智能网联汽车的安全等级，适用于智能网联车辆群体决策场景。
可应用本申请实施例提供的汽车协同决策方法的系统架构的一种示例如图3所示，系统可以包括云服务器301、路侧设备302、智能网联汽车303、智能网联汽车304，云服务器301和智能路侧设备302之间可以以有线、无线连接方式进行通信，云服务器301和智能网联汽车303、304之间可以以有线、无线方式进行通信，路侧设备302可以通过各类传感器对智能网联汽车303、304行驶态势进行感知。
云服务器包括一个或多个信息收发装置、计算处理装置、存储装置，各装置之间可以通过有线、无线连接方式进行通信。云服务器通过接收路侧设备发送的协同决策请求以及车端信息，对决策场景内所有车辆计算一套最优决策规划方案，为每辆智能网联汽车提供决策建议服务。
道路场景内可以有多个路侧设备，路侧设备上设置有各种传感器、信息收发装置、计算处理装置，各装置之间可以通过有线、无线连接方式进行通信。路侧设备通过各种传感器获取区域内所有车辆的行驶态势，通过计算处理装置计算区域范围内拥堵系数，根据拥堵系数使用信息收发装置向云端发送协同决策请求。
智能网联汽车303和智能网联汽车304上均设置车载智能电脑，用以向云服务器传输自身车速、规划路径等行驶信息，同时接收来自云服务器的决策建议。车载智能电脑还可根据本车感知信息以及云服务器的决策建议规划本车行驶决策规划方案。
上述图3中智能网联汽车数目和路侧设备数目仅仅是示意性的，根据实现需要可以有任意数目的智能网联汽车以及路侧设备。
在一个实施例中，可应用本申请实施例提供的汽车协同决策方法的系统架构的另一种示例如图4所示，路口汇入场景中有n辆智能网联汽车；路侧设备包括感知装置、数据处理装置和信息发送装置；云服务器包括信息获取装置、场景判别装置、n个场景决策单元和决策分发装置。
下面以一个具体实施例对上述技术方案进行说明。
步骤1：汇车路口路侧设备获取主干道与汇车道的道路实时信息，分 析道路拥堵系数，发送协同决策请求。
在本实施例中，路侧设备包括预先设置在其上的感知装置和数据处理装置和信息发送装置。其中，感知装置至少包括激光雷达、摄像头等传感器用以采集道路主干道和汇车道内车辆数量和车辆位置，数据处理装置通过计算路段内车辆密度和多车行驶平均速度获得道路拥堵系数。
路侧设备数据处理装置判断拥堵系数到达预设阈值，通过信息发送装置向云服务器端发送协同决策请求。
其中，信息发送装置可以通过有线连接或无线连接的通信方式向云服务器端发送请求，具体连接形式包括但不限于3G/4G/5G连接、WIFI连接、电缆或光缆连接，以及其他现在已知或将来开发的无线连接方式。
步骤2：云服务器端接收协同决策请求，获取路侧设备所属场景的第一信息，匹配场景决策单元进行决策计算。
在本实施例中，云服务器端包括信息获取装置、场景判别装置、适用于不同道路场景的多个场景决策单元、以及决策分发装置。
在本实施例中，信息获取装置可以有线连接或无线连接方式接收从车端或路侧端各类信息，信息获取装置响应从所述汇车路口路侧设备接收到的协同决策请求，从路侧端接收场景第一信息，并通过有线连接或无线连接方式将其传输给场景判别装置。
在本实施例中，场景第一信息包括主干道车道数，路侧设备所处位置经纬度等，从路侧设备处通过有线或无线连接方式获得。
在本实施例中，场景判别装置内存储有多个预设的协同决策场景模型和带路段标注的路网地图模型，场景判别装置根据场景第一信息与预设场景模型和路网地图模型进行匹配，识别到该道路场景为路口汇车协同决策模型后，通过无线连接或有限连接方式发送计算决策指令给适用于处理路口汇车协同决策的场景决策单元。
步骤3：场景决策单元响应于计算决策指令，获取决策所需信息，分析计算多车决策规划方案。
在本实施例中，场景决策单元通过有线连接或无线连接的方式与上述信息获取装置进行通信，通过信息获取装置获取所需道路约束信息和车辆 即时数据。
在本实施例中，所需道路约束信息包括通过信息获取装置从路侧端接收的主干道限速信息、汇车道限速信息；所需车辆即时信息包括通过信息获取装置从路侧设备感知范围内的每台车辆端获取的车辆规划路径、行车速度、加速度、角速度等即时数据。
在本实施例中，场景决策单元包含预设的适用于路口汇入场景协同决策模型，在满足道路约束信息的条件下对车辆即时信息进行分析，依照协同决策模型计算路口汇入场景下的多车决策规划方案，传输至决策分发装置。
在本实施例中，多车决策规划方案包括对路侧设备感知范围内主干道和汇车道内每台车辆的加速、减速、制动、变道等驾驶行为建议。
步骤4：多车协同汇入决策规划方案分发至对应车端，提供决策建议。
在本实施例中，决策分发装置通过有线连接或无线连接方式接收来自场景决策单元的决策规划方案，根据方案内决策流的车辆特征，将决策规划方案进行分解，形成单车决策建议并分发至对应车辆端，完成路口汇车场景下多车协同决策过程。
如图5所示，本申请还提供一种汽车协同决策装置，该汽车协同决策装置500，应用于云服务器端，包括：
接收模块501，用于接收路侧设备发送的协同决策请求，其中，协同决策请求是路侧设备基于获取到的道路实时信息确定道路发生拥堵后发送的请求；
获取模块502，用于获取协同决策请求中包括的道路场景信息；
确定模块503，用于基于道路场景信息，确定拥堵场景类型；
计算模块504，用于基于拥堵场景类型，利用预设场景协同决策模型计算面向多车的决策规划方案；
发送模块505，用于向各个汽车分别发送对应的决策规划方案，以使各个汽车依据各自的决策规划方案执行对应的驾驶操作。
可选的，计算模块504包括：
获取单元，用于获取拥堵场景类型对应的道路约束信息和车辆即时状 态信息；
计算单元，用于基于道路约束信息和车辆即时状态信息，利用预设场景协同决策模型计算面向多车的决策规划方案。
可选的，获取单元包括：
第一接收子单元，用于接收各个汽车发送的车辆速度、加速度、角速度、车轮转向、制动信息、与前/后车之间的相对距离、目的地信息、规划路径行驶比率中的至少一种信息。
可选的，获取单元包括：
第二接收子单元，用于接收路侧设备发送的交通灯状态信息、车辆前后相对次序信息、行人在道路场景中所处位置信息中的至少一种信息。
可选的，道路约束信息包括车辆允许速度范围、车道数、车道宽度、道路可行路段。
可选的，预设场景协同决策模型为基于博弈论匹配装置设计的时隙分配模型。
可选的，决策规划方案包括行驶优先级分配方案、车辆路径变更方案、车辆下一状态行驶操作方案中的至少一种方案。
图5所示装置中的各个模块/单元具有实现图1中各个步骤的功能，并能达到其相应的技术效果，为简洁描述，在此不再赘述。
图6示出了本申请实施例提供的电子设备的结构示意图。
电子设备可以包括处理器601以及存储有计算机程序指令的存储器602。
具体地，上述处理器601可以包括中央处理器(CPU)，或者特定集成电路(Application Specific Integrated Circuit，ASIC)，或者可以被配置成实施本申请实施例的一个或多个集成电路。
存储器602可以包括用于数据或指令的大容量存储器。举例来说而非限制，存储器602可包括硬盘驱动器(Hard Disk Drive，HDD)、软盘驱动器、闪存、光盘、磁光盘、磁带或通用串行总线(Universal Serial Bus，USB)驱动器或者两个或更多个以上这些的组合。在合适的情况下，存储器602可包括可移除或不可移除(或固定)的介质。在合适的情况下，存储 器602可在电子设备的内部或外部。在特定实施例中，存储器602可以是非易失性固态存储器。
在一个实例中，存储器602可以是只读存储器(Read Only Memory，ROM)。在一个实例中，该ROM可以是掩模编程的ROM、可编程ROM(PROM)、可擦除PROM(EPROM)、电可擦除PROM(EEPROM)、电可改写ROM(EAROM)或闪存或者两个或更多个以上这些的组合。
处理器601通过读取并执行存储器602中存储的计算机程序指令，以实现上述实施例中的任意一种汽车协同决策方法。
在一个示例中，电子设备还可包括通信接口603和总线610。其中，如图6所示，处理器601、存储器602、通信接口603通过总线610连接并完成相互间的通信。
通信接口603，主要用于实现本申请实施例中各模块、装置、单元和/或设备之间的通信。
总线610包括硬件、软件或两者，将在线数据流量计费设备的部件彼此耦接在一起。举例来说而非限制，总线可包括加速图形端口(AGP)或其他图形总线、增强工业标准架构(EISA)总线、前端总线(FSB)、超传输(HT)互连、工业标准架构(ISA)总线、无限带宽互连、低引脚数(LPC)总线、存储器总线、微信道架构(MCA)总线、外围组件互连(PCI)总线、PCI-Express(PCI-X)总线、串行高级技术附件(SATA)总线、视频电子标准协会局部(VLB)总线或其他合适的总线或者两个或更多个以上这些的组合。在合适的情况下，总线610可包括一个或多个总线。尽管本申请实施例描述和示出了特定的总线，但本申请考虑任何合适的总线或互连。
另外，本申请实施例可提供一种计算机存储介质来实现。该计算机存储介质上存储有计算机程序指令；该计算机程序指令被处理器执行时实现上述实施例中的任意一种汽车协同决策方法。
计算机存储介质的示例包括非暂态计算机可读存储介质，如ROM、随机存取存储器(Random Access Memory，RAM)、磁碟或者光盘等。
需要明确的是，本申请并不局限于上文所描述并在图中示出的特定配 置和处理。为了简明起见，这里省略了对已知方法的详细描述。在上述实施例中，描述和示出了若干具体的步骤作为示例。但是，本申请的方法过程并不限于所描述和示出的具体步骤，本领域的技术人员可以在领会本申请的精神后，作出各种改变、修改和添加，或者改变步骤之间的顺序。
以上所述的结构框图中所示的功能模块可以实现为硬件、软件、固件或者它们的组合。当以硬件方式实现时，其可以例如是电子电路、专用集成电路(ASIC)、适当的固件、插件、功能卡等等。当以软件方式实现时，本申请的元素是被用于执行所需任务的程序或者代码段。程序或者代码段可以存储在机器可读介质中，或者通过载波中携带的数据信号在传输介质或者通信链路上传送。“机器可读介质”可以包括能够存储或传输信息的任何介质。机器可读介质的例子包括电子电路、半导体存储器设备、ROM、闪存、可擦除ROM(EROM)、软盘、CD-ROM、光盘、硬盘、光纤介质、射频(RF)链路，等等。代码段可以经由诸如因特网、内联网等的计算机网络被下载。
还需要说明的是，本申请中提及的示例性实施例，基于一系列的步骤或者装置描述一些方法或系统。但是，本申请不局限于上述步骤的顺序，也就是说，可以按照实施例中提及的顺序执行步骤，也可以不同于实施例中的顺序，或者若干步骤同时执行。
上面参考根据本申请的实施例的方法、装置(系统)和计算机程序产品的流程图和/或框图描述了本申请的各方面。应当理解，流程图和/或框图中的每个方框以及流程图和/或框图中各方框的组合可以由计算机程序指令实现。这些计算机程序指令可被提供给通用计算机、专用计算机、或其它可编程数据处理装置的处理器，以产生一种机器，使得经由计算机或其它可编程数据处理装置的处理器执行的这些指令使能对流程图和/或框图的一个或多个方框中指定的功能/动作的实现。这种处理器可以是但不限于是通用处理器、专用处理器、特殊应用处理器或者现场可编程逻辑电路。还可理解，框图和/或流程图中的每个方框以及框图和/或流程图中的方框的组合，也可以由执行指定的功能或动作的专用硬件来实现，或可由专用硬件和计算机指令的组合来实现。
以上所述，仅为本申请的具体实施方式，所属领域的技术人员可以清楚地了解到，为了描述的方便和简洁，上述描述的系统、模块和单元的具体工作过程，可以参考前述方法实施例中的对应过程，在此不再赘述。应理解，本申请的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本申请揭露的技术范围内，可轻易想到各种等效的修改或替换，这些修改或替换都应涵盖在本申请的保护范围之内。

Claims (16)

  1. 一种汽车协同决策方法，应用于云服务器端，包括：
    接收路侧设备发送的协同决策请求，其中，所述协同决策请求是所述路侧设备基于获取到的道路实时信息确定道路发生拥堵后发送的请求；
    获取所述协同决策请求中包括的道路场景信息；
    基于所述道路场景信息，确定拥堵场景类型；
    基于所述拥堵场景类型，利用预设场景协同决策模型计算面向多车的决策规划方案；
    向各个汽车分别发送对应的决策规划方案，以使各个所述汽车依据各自的所述决策规划方案执行对应的驾驶操作。
  2. 根据权利要求1所述的汽车协同决策方法，其中，所述基于所述拥堵场景类型，利用预设场景协同决策模型计算面向多车的决策规划方案，包括：
    获取所述拥堵场景类型对应的道路约束信息和车辆即时状态信息；
    基于所述道路约束信息和所述车辆即时状态信息，利用所述预设场景协同决策模型计算面向多车的所述决策规划方案。
  3. 根据权利要求2所述的汽车协同决策方法，其中，获取所述车辆即时状态信息，包括：
    接收各个所述汽车发送的车辆速度、加速度、角速度、车轮转向、制动信息、与前/后车之间的相对距离、目的地信息、规划路径行驶比率中的至少一种信息。
  4. 根据权利要求2所述的汽车协同决策方法，其中，获取所述车辆即时状态信息，包括：
    接收所述路侧设备发送的交通灯状态信息、车辆前后相对次序信息、行人在道路场景中所处位置信息中的至少一种信息。
  5. 根据权利要求2所述的汽车协同决策方法，其中，所述道路约束信息包括车辆允许速度范围、车道数、车道宽度、道路可行路段。
  6. 根据权利要求1所述的汽车协同决策方法，其中，所述预设场景协同决策模型为基于博弈论匹配方法设计的时隙分配模型。
  7. 根据权利要求1所述的汽车协同决策方法，其中，所述决策规划方案包括行驶优先级分配方案、车辆路径变更方案、车辆下一状态行驶操作方案中的至少一种方案。
  8. 一种汽车协同决策装置，应用于云服务器端，包括：
    接收模块，用于接收路侧设备发送的协同决策请求，其中，所述协同决策请求是所述路侧设备基于获取到的道路实时信息确定道路发生拥堵后发送的请求；
    获取模块，用于获取所述协同决策请求中包括的道路场景信息；
    确定模块，用于基于所述道路场景信息，确定拥堵场景类型；
    计算模块，用于基于所述拥堵场景类型，利用预设场景协同决策模型计算面向多车的决策规划方案；
    发送模块，用于向各个汽车分别发送对应的决策规划方案，以使各个所述汽车依据各自的所述决策规划方案执行对应的驾驶操作。
  9. 根据权利要求8所述的汽车协同决策装置，其中，所述计算模块包括：
    获取单元，用于获取所述拥堵场景类型对应的道路约束信息和车辆即时状态信息；
    计算单元，用于基于所述道路约束信息和所述车辆即时状态信息，利用所述预设场景协同决策模型计算面向多车的所述决策规划方案。
  10. 根据权利要求9所述的汽车协同决策装置，其中，所述获取单元包括：
    第一接收子单元，用于接收各个所述汽车发送的车辆速度、加速度、角速度、车轮转向、制动信息、与前/后车之间的相对距离、目的地信息、规划路径行驶比率中的至少一种信息。
  11. 根据权利要求9所述的汽车协同决策装置，其中，所述获取单元包括：
    第二接收子单元，用于接收所述路侧设备发送的交通灯状态信息、车辆前后相对次序信息、行人在道路场景中所处位置信息中的至少一种信息。
  12. 根据权利要求9所述的汽车协同决策装置，其中，所述道路约束信息包括车辆允许速度范围、车道数、车道宽度、道路可行路段。
  13. 根据权利要求8所述的汽车协同决策装置，其中，所述预设场景协同决策模型为基于博弈论匹配方法设计的时隙分配模型。
  14. 根据权利要求8所述的汽车协同决策装置，其中，所述决策规划方案包括行驶优先级分配方案、车辆路径变更方案、车辆下一状态行驶操作方案中的至少一种方案。
  15. 一种电子设备，包括：处理器以及存储有计算机程序指令的存储器，所述处理器执行所述计算机程序指令时实现如权利要求1-7任意一项所述的汽车协同决策方法。
  16. 一种计算机存储介质，所述计算机存储介质上存储有计算机程序指令，所述计算机程序指令被处理器执行时实现如权利要求1-7任意一项所述的汽车协同决策方法。
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