WO2020258277A1 - 一种智能驾驶车辆让行方法、装置及车载设备 - Google Patents

一种智能驾驶车辆让行方法、装置及车载设备 Download PDF

Info

Publication number
WO2020258277A1
WO2020258277A1 PCT/CN2019/093809 CN2019093809W WO2020258277A1 WO 2020258277 A1 WO2020258277 A1 WO 2020258277A1 CN 2019093809 W CN2019093809 W CN 2019093809W WO 2020258277 A1 WO2020258277 A1 WO 2020258277A1
Authority
WO
WIPO (PCT)
Prior art keywords
vehicle
intelligent driving
driving vehicle
information
road
Prior art date
Application number
PCT/CN2019/093809
Other languages
English (en)
French (fr)
Inventor
赵世杰
马万里
周小成
Original Assignee
驭势科技（北京）有限公司
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by 驭势科技（北京）有限公司 filed Critical 驭势科技（北京）有限公司
Priority to CN201980001028.8A priority Critical patent/CN110603181B/zh
Priority to PCT/CN2019/093809 priority patent/WO2020258277A1/zh
Publication of WO2020258277A1 publication Critical patent/WO2020258277A1/zh

Links

Images

Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G08G1/166—Anti-collision systems for active traffic, e.g. moving vehicles, pedestrians, bikes
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G08G1/167—Driving aids for lane monitoring, lane changing, e.g. blind spot detection
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • 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]
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W2050/0001—Details of the control system
    • B60W2050/0002—Automatic control, details of type of controller or control system architecture
    • B60W2050/0004—In digital systems, e.g. discrete-time systems involving sampling
    • B60W2050/0005—Processor details or data handling, e.g. memory registers or chip architecture

Definitions

  • the embodiments of the present disclosure relate to the field of intelligent driving, and in particular to a method, device and vehicle-mounted equipment for an intelligent driving vehicle to yield.
  • Vehicle-road collaboration technology uses advanced wireless network technology (including cellular network communication, wireless communication, 4G and 5G and other communication technologies) for data transmission to realize real-time data exchange between road-cloud-vehicles, thereby realizing active safety control of vehicles , To fully realize effective coordination between vehicles and vehicles, and between vehicles and roads, thereby improving traffic efficiency and ensuring traffic safety.
  • advanced wireless network technology including cellular network communication, wireless communication, 4G and 5G and other communication technologies
  • the road is a single lane, but it can drive in both directions. In the case of unreasonable planning, congestion or congestion may occur. Therefore, special planning is required for this special road condition, and the prior art does not provide any effective solution for this special road condition.
  • At least one embodiment of the present application provides a method, device and vehicle-mounted device for an intelligent driving vehicle to yield.
  • an embodiment of the present disclosure proposes a method for giving way to a smart-driving vehicle, wherein the smart-driving vehicle is driving on a special road, and the special road is configured with multiple road monitoring units, and the method includes:
  • the embodiments of the present disclosure also propose a vehicle-mounted device, including:
  • the processor is used to execute the steps of the method described in the first aspect by calling the program or instruction stored in the memory.
  • the embodiments of the present disclosure also propose a non-transitory computer-readable storage medium, the non-transitory computer-readable storage medium stores a program or instruction, and the program or instruction causes a computer to execute as described in the first aspect Method steps.
  • an embodiment of the present disclosure also proposes an intelligent driving vehicle yielding device.
  • the intelligent driving vehicle to which the intelligent driving vehicle yielding device belongs runs on a special road, and the special road is configured with multiple road monitoring units, so
  • the smart driving vehicle yielding device includes:
  • a determining unit configured to determine a corresponding road monitoring unit based on the state information of the intelligent driving vehicle
  • the avoidance unit is configured to determine whether there is a passing vehicle that needs to avoid, based on the monitoring information; when there is a passing vehicle that needs to avoid, control the intelligent driving vehicle to avoid.
  • an embodiment of the present disclosure also proposes a vehicle yielding system, including: a server, the smart driving vehicle yielding device as described in any embodiment of the fourth aspect, and multiple road monitoring units configured on the road ;
  • the road monitoring unit interacts with the server, and the server interacts with the intelligent driving vehicle yielding device.
  • the corresponding road monitoring unit is obtained through the state information of the intelligent driving vehicle, and according to the monitoring information of the road monitoring unit, it is determined whether there is a passing vehicle that needs to avoid.
  • the intelligent driving vehicle can be controlled to avoid giving way, and then it can take the initiative to give way in advance to improve the efficiency of traffic and ensure traffic safety.
  • the decision is made based on the monitoring information of the road monitoring unit, that is, through the effective coordination between the vehicle and the road, not only the sensor of the intelligent driving vehicle is used for sensing, thereby improving the traffic efficiency and ensuring traffic safety.
  • FIG. 1 is a scene diagram of an intelligent driving vehicle driving provided by an embodiment of the present disclosure
  • Figure 2 is an overall architecture diagram of an intelligent driving vehicle provided by an embodiment of the present disclosure
  • 3A is a block diagram of an intelligent driving system provided by an embodiment of the present disclosure.
  • 3B is a block diagram of a yield module provided by an embodiment of the present disclosure.
  • FIG. 4 is a block diagram of a vehicle-mounted device provided by an embodiment of the present disclosure.
  • FIG. 5 is a flowchart of a method for giving way for an intelligent driving vehicle provided by an embodiment of the present disclosure
  • FIG. 6 is a signaling diagram of a method for giving way for an intelligent driving vehicle provided by an embodiment of the present disclosure
  • FIG. 7A is a scene diagram of another intelligent driving vehicle driving provided by an embodiment of the present disclosure.
  • FIG. 7B to 7E are schematic diagrams of giving way according to the intelligent driving vehicle in the scene shown in FIG. 7A.
  • embodiments of the present disclosure provide a solution for intelligently driving vehicles to give way to driving on special roads.
  • the intelligent driving vehicle can be controlled to avoid it, and then the initiative can be taken in advance to improve traffic efficiency and ensure traffic safety.
  • the embodiments of the present disclosure provide an intelligent driving vehicle yielding solution, which can be applied to intelligent driving vehicles and various scenarios. For example, when a smart driving vehicle and some emergency vehicles (for example, an ambulance, a fire engine, etc.) are driving head-on, the smart driving vehicle needs to avoid the emergency vehicle.
  • the intelligent driving vehicle when the intelligent driving vehicle is driving on a special road, it may also encounter situations where it is necessary to avoid or yield. For example, when the special road is a two-way single-lane road, it is necessary to avoid passing vehicles (such as fire trucks) that are driving in the same direction and located behind the driving direction of the intelligent driving vehicle, or it is necessary to avoid oncoming vehicles (such as rescue vehicles) driving oncoming traffic. Take avoidance.
  • the road monitoring unit may be a device configured on both sides of the road to collect monitoring information within the monitoring range.
  • the road monitoring unit may also be embedded in other devices, such as on a traffic light device, a camera, or other road signs.
  • Figure 1 is a scene of a smart driving vehicle driving in some embodiments of the present disclosure.
  • the scene includes a cloud server 001, a road side unit (RSU: Road Side Unit) 002, and a smart driving vehicle 003 And traffic vehicles 004.
  • the cloud server 001 may be used to obtain information about the road monitoring unit 002 and the intelligent driving vehicle 003, and may send information to the intelligent driving vehicle 003.
  • the cloud server 001 may send the monitoring information corresponding to the smart driving vehicle 003 in the road monitoring unit 002 to the smart driving vehicle 003 according to the information of the smart driving vehicle 003.
  • the cloud server 001 may be a server or a server group.
  • the server group can be centralized or distributed.
  • the server can be local or remote.
  • the road monitoring unit 002 can be used to collect road monitoring information.
  • the road monitoring unit 002 may be an environmental sensing sensor, such as a camera, a lidar, etc., or a road device, such as a V2X device, a roadside traffic light device, etc.
  • the road monitoring unit 002 may monitor the road conditions subordinate to the corresponding road monitoring unit 002, for example, the type, speed, priority level, etc. of passing vehicles. After the road monitoring unit 002 collects the road monitoring information, it can send the road monitoring information to the cloud server, or to the intelligent driving vehicles passing the road.
  • the intelligent driving vehicle 003 is used to generate control information according to the surrounding environment and control the driving of the vehicle.
  • the intelligent driving vehicle 003 may send request information to a cloud server for obtaining relevant information of the cloud server.
  • the request information includes, but is not limited to, the current vehicle pose, corresponding road monitoring unit information corresponding to the vehicle, and the like.
  • the intelligent driving vehicle 003 may receive feedback information from the cloud server 001, where the feedback information includes, but is not limited to, road monitoring information of a corresponding road monitoring unit.
  • the intelligent driving vehicle 003 can realize the planning control information of the intelligent driving vehicle 003 according to the road monitoring information of the corresponding road monitoring unit, for example, avoiding some passing vehicles on special roads, thereby improving special
  • the traffic efficiency of vehicles on the road also ensures traffic safety.
  • Traffic vehicles 004 are various vehicles that travel on the road.
  • the traffic vehicle 004 may be a smart driving vehicle, a manual driving vehicle, or an autonomous driving vehicle of different levels.
  • the traffic vehicle 004 may also be a vehicle including but not limited to a small car, a medium-sized car, a large-sized car, a cargo car, an ambulance, a fire engine, etc.
  • different vehicles have different priorities. For example, the priority of an ambulance or a fire truck is higher than that of a normal vehicle.
  • FIG. 2 is an overall architecture diagram of an intelligent driving vehicle in some embodiments of the present disclosure.
  • the intelligent driving vehicle includes: a sensor group, an intelligent driving system 100, a vehicle bottom-level execution system, and others that can be used to drive intelligent driving Vehicles and components that control the operation of intelligent driving vehicles.
  • the sensor group is used to collect the data of the external environment of the intelligent driving vehicle and to detect the position data of the intelligent driving vehicle.
  • the sensor group includes, but is not limited to, at least one of a camera, a lidar, a millimeter wave radar, a GPS (Global Positioning System, global positioning system), and an IMU (Inertial Measurement Unit), for example.
  • the sensor group is also used to collect dynamics data of the vehicle.
  • the sensor group includes, but is not limited to, at least one of a wheel speed sensor, a speed sensor, an acceleration sensor, a steering wheel angle sensor, and a front wheel angle sensor.
  • the intelligent driving system 100 is used to obtain data of a sensor group, and all sensors in the sensor group transmit data at a higher frequency during the driving of the intelligent driving vehicle.
  • the intelligent driving system is also used for wireless communication with the cloud server to exchange various information.
  • the intelligent driving system is also used for wireless communication with the road monitoring unit to exchange various information.
  • the intelligent driving system 100 is also used for environmental perception and vehicle positioning based on the data of the sensor group, path planning and decision-making based on environmental perception information and vehicle positioning information, and generating vehicle control instructions based on the planned path, thereby controlling the vehicle according to the plan Route driving.
  • the intelligent driving system 100 is further configured to determine a corresponding road monitoring unit based on the state information of the intelligent driving vehicle; receive monitoring information of the corresponding road monitoring unit; determine whether there is a passing vehicle that needs to be avoided based on the monitoring information; For passing vehicles that need to avoid, control intelligent driving vehicles to avoid.
  • the intelligent driving system 100 may be a software system, a hardware system, or a combination of software and hardware.
  • the intelligent driving system 100 is a software system running on an operating system
  • the on-board hardware system is a hardware system supporting the operation of the operating system.
  • the intelligent driving system of the present disclosure may be a component in an in-vehicle device or an in-vehicle control device of an intelligently driving vehicle, or an in-vehicle device or an in-vehicle control device of an intelligently driving vehicle.
  • the bottom-level execution system of the vehicle is used to receive vehicle control instructions and realize the control of the intelligent driving vehicle.
  • the bottom-level execution system of the vehicle includes but is not limited to: steering system, braking system and drive system.
  • the steering system, braking system, and driving system are mature structures in the vehicle field, and will not be repeated here.
  • the intelligent driving vehicle may further include a vehicle CAN bus not shown in FIG. 2, and the vehicle CAN bus is connected to the underlying execution system of the vehicle.
  • the information interaction between the intelligent driving system 100 and the underlying execution system of the vehicle is transmitted through the vehicle CAN bus.
  • the intelligent driving vehicle can be controlled by the driver and the intelligent driving system 100 to control the vehicle.
  • the driver drives the vehicle by operating a device that controls the travel of the vehicle.
  • the devices that control the travel of the vehicle include, but are not limited to, a brake pedal, a steering wheel, and an accelerator pedal.
  • the device for controlling the driving of the vehicle can directly operate the execution system at the bottom of the vehicle to control the driving of the vehicle.
  • the intelligent driving vehicle may also be an unmanned vehicle, and the driving control of the intelligent driving vehicle is executed by the intelligent driving system.
  • FIG. 3A is a block diagram of an intelligent driving system 200 provided by an embodiment of the disclosure.
  • the smart driving system 200 may be implemented as the smart driving system 100 or a part of the smart driving system 100 in FIG. 2 for controlling the driving of the vehicle.
  • the intelligent driving system 200 includes, but is not limited to: a perception module 201, a planning module 202, a control module 203, a yield module 204, and other modules that can be used for intelligent driving.
  • the sensing module 201 is used for acquired sensor data, V2X (Vehicle to X) data, high-precision maps and other data. In some embodiments, the sensing module 201 is configured to perform environment perception and positioning based on at least one of acquired sensor data, V2X (Vehicle to X, vehicle wireless communication) data, and high-precision maps. In some embodiments, the perception module 201 is used to generate perception positioning information to realize obstacle perception, recognition of the drivable area of camera images, and vehicle positioning.
  • Environmental perception can be understood as the ability to understand the scene of the environment, such as the location of obstacles, the detection of road signs/marks, the detection of pedestrians/vehicles, and the semantic classification of data.
  • environment perception can be realized by fusing data from multiple sensors such as cameras, lidars, millimeter wave radars, etc.
  • Localization is a part of perception, which is the ability to determine the position of the intelligent driving vehicle relative to the environment.
  • Positioning can be: GPS positioning, GPS positioning accuracy is tens of meters to centimeters, high positioning accuracy; positioning can also use GPS and inertial navigation system (Inertial Navigation System) positioning method. Positioning can also use SLAM (Simultaneous Localization And Mapping, simultaneous positioning and map construction). The goal of SLAM is to construct a map while using the map for positioning. SLAM uses the observed environmental features to determine the current vehicle's position and current observation features s position.
  • V2X is the key technology of the intelligent transportation system, which enables the communication between vehicles, vehicles and base stations, base stations and base stations, so as to obtain a series of traffic information such as real-time road conditions, road information, pedestrian information, and improve the safety of intelligent driving. Congestion, improve traffic efficiency, provide on-board entertainment information, etc.
  • High-precision maps are geographic maps used in the field of intelligent driving. Compared with traditional maps, the differences are: 1) High-precision maps include a large amount of driving assistance information, for example, relying on the accurate three-dimensional representation of the road network: including intersections and The location of road signs, etc.; 2) The high-precision map also includes a lot of semantic information, such as reporting the meaning of different colors on traffic lights, and for example indicating the speed limit of the road, and the position of the left turn lane; 3) The high-precision map can reach centimeters Class accuracy ensures the safe driving of intelligent driving vehicles.
  • the planning module 202 is used for path planning and decision-making based on the perception positioning information generated by the perception positioning module.
  • the planning module 202 is used to perform a route based on the perception positioning information generated by the perception positioning module, combined with at least one of V2X data, high-precision maps and other data, road monitoring unit information, and cloud server information. Planning and decision-making.
  • the planning module 202 is used to plan routes and make decisions: behaviors (for example, including but not limited to following, overtaking, stopping, detouring, etc.), vehicle heading, vehicle speed, desired acceleration of the vehicle, desired steering wheel angle And so on, generate planning decision information.
  • behaviors for example, including but not limited to following, overtaking, stopping, detouring, etc.
  • vehicle heading for example, including but not limited to following, overtaking, stopping, detouring, etc.
  • vehicle speed for example, including but not limited to following, overtaking, stopping, detouring, etc.
  • desired acceleration of the vehicle for example, including but not limited to following, overtaking, stopping, detouring, etc.
  • desired steering wheel angle And so on generate planning decision information.
  • the control module 203 is configured to perform path tracking, trajectory tracking, or path avoidance based on the planning decision information generated by the planning module.
  • control module 203 is used to generate control instructions for the bottom-level execution system of the vehicle, and issue control instructions so that the bottom-level execution system of the vehicle controls the vehicle to travel along a desired path, for example, by controlling the steering wheel, brakes, and accelerator to control the vehicle. Horizontal and vertical control.
  • control module 203 is also used to calculate the front wheel angle based on the path tracking algorithm.
  • the desired path curve in the path tracking process has nothing to do with time parameters.
  • tracking control it can be assumed that the intelligent driving vehicle is moving at a constant speed at the current speed, and the driving path is approached to the desired path at a certain cost rule; and the trajectory
  • the expected path curve is related to time and space, and the intelligent driving vehicle is required to reach a preset reference path point within a specified time.
  • Path tracking is different from trajectory tracking. It is not subject to time constraints and only needs to track the desired path within a certain error range.
  • the yield module 204 is configured to determine the corresponding road monitoring unit based on the state information of the intelligent driving vehicle; receive the monitoring information of the corresponding road monitoring unit; determine whether there is a passing vehicle that needs to be avoided based on the monitoring information; if there is a need to avoid
  • the passing vehicle is used to plan and generate the avoidance path of the intelligent driving vehicle, and control the intelligent driving vehicle to follow the avoidance path. For example, the intelligent driving vehicle is controlled to follow an avoiding path on a two-way single lane.
  • the function of the yield module 204 can be integrated into the perception module 201, the planning module 202 or the control module 203, or it can be configured as a module independent of the intelligent driving system.
  • the yield module 204 can be a software module, Hardware module or a combination of software and hardware.
  • the yield module 204 is a software module running on an operating system
  • the vehicle-mounted hardware system is a hardware system that supports the running of the operating system.
  • FIG. 3B is a block diagram of an intelligent driving vehicle yielding device 300 provided by an embodiment of the disclosure.
  • the smart driving vehicle yielding device 300 may be implemented as the yielding module 204 or a part of the yielding module 204 in FIG. 3A.
  • the smart driving vehicle yielding device 300 includes a determining unit 301, a receiving unit 302, an avoiding unit 303, and some other units that can be used to perform avoiding operations.
  • the determining unit 301 is configured to determine the corresponding road monitoring unit based on the state information of the intelligent driving vehicle; in some embodiments, the state information of the intelligent driving vehicle includes, but is not limited to: position information and heading, speed, driving destination and driving At least one of the states. In some embodiments, the determining unit 301 is further configured to obtain corresponding road monitoring unit information based on the location information and high-precision map of the intelligent driving vehicle. Wherein, the high-precision map also includes location information of the road monitoring unit. In some embodiments, the determining unit 301 is also configured to obtain corresponding road monitoring unit information based on position information, heading, speed, and high-precision maps.
  • the corresponding road monitoring unit information includes, but is not limited to, at least one of road monitoring unit location information, road monitoring unit identification, and the number of road monitoring units.
  • the monitoring information of the road monitoring unit may include, but is not limited to, at least one of passing vehicle information, passing vehicle priority, passing vehicle location, passing vehicle heading, and passing vehicle type.
  • the receiving unit 302 is configured to receive the monitoring information of the corresponding road monitoring unit; in some embodiments, the receiving unit 302 may also be configured to send an acquisition request to a cloud server, the acquisition request including at least the corresponding road monitoring unit information ; Receive a response from the cloud server, the response at least including the monitoring information of each road monitoring unit in the corresponding road monitoring unit.
  • the road monitoring unit acquires monitoring information in real time or periodically, and sends the acquired monitoring information to the cloud server in real time or periodically. After the cloud server receives the acquisition request, it will obtain the corresponding road monitoring unit information in the acquisition request. The latest monitoring information of the corresponding road monitoring unit or the monitoring information within a preset time period is screened, and the screened monitoring information is sent to the receiving unit 302.
  • the smart driving vehicle yielding device of the embodiments of the present disclosure directly wirelessly communicates with the corresponding road monitoring unit to obtain the latest monitoring information monitored by the corresponding road monitoring unit.
  • the avoidance unit 303 is configured to determine whether there is a passing vehicle that needs to be avoided based on the monitoring information; when there is a passing vehicle that needs to avoid, control the intelligent driving vehicle to avoid. In some embodiments, the avoidance unit 303 determines whether there is a passing vehicle that needs to avoid based on at least one of the priority of the passing vehicle, the location of the passing vehicle, and the heading of the passing vehicle. In some embodiments, when the priority of the passing vehicle is higher than the priority of the intelligent driving vehicle, the intelligent driving vehicle needs to avoid.
  • the priority of ambulances or fire trucks is usually higher than that of other vehicles on the road, such as large vehicles, intelligent driving vehicles, and small cars.
  • high-priority vehicles are not limited to ambulances and fire trucks.
  • the priority of passing vehicles can be set and adjusted through the intelligent driving system/on-board equipment of the passing vehicles. In some embodiments, when the priority of the passing vehicle is the same as the priority of the intelligent driving vehicle, or is lower than that of the intelligent driving vehicle, the intelligent driving vehicle does not perform an avoidance operation.
  • the intelligent driving vehicle if the intelligent driving vehicle receives at least one of the passing vehicles through vehicle-to-vehicle communication that the priority of at least one passing vehicle is increased and is higher than the priority of the intelligent driving vehicle, the intelligent driving vehicle needs to avoid.
  • the avoidance unit 303 is also used to plan and generate an avoidance path of the intelligent driving vehicle; and control the intelligent driving vehicle to drive along the avoidance path.
  • the smart driving vehicle yielding device 300 further includes a path planning unit not shown in the figure, for generating a planned path based on the destination and the current position of the smart driving vehicle after the avoidance path is completed based on the avoiding path.
  • the destination is the destination of the driving route of the intelligent driving system before the avoidance route is generated.
  • the destination may also be a destination updated by the user.
  • the current position of the intelligent driving vehicle is the same as the position of the avoiding point/giving point in the avoiding path.
  • the sensor data of the intelligent driving vehicle is not only relied on, thereby improving the traffic efficiency and ensuring traffic safety.
  • the division of each unit in the smart driving vehicle yielding device 300 is only a logical function division, and there may be other divisions in actual implementation, for example, the determining unit 301, the receiving unit 302, and the avoiding unit 303 can be implemented. It is a unit; the determining unit 301, the receiving unit 302, or the avoiding unit 303 can also be divided into multiple sub-units. It can be understood that each unit or sub-unit can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software depends on the specific application and design constraint conditions of the technical solution. Professional technicians can use different methods for each specific application to achieve the described functions.
  • Fig. 4 is a schematic structural diagram of a vehicle-mounted device provided by an embodiment of the present disclosure.
  • On-board equipment can support the operation of the intelligent driving system.
  • the vehicle-mounted device includes: at least one processor 401, at least one memory 402, and at least one communication interface 403.
  • the various components in the vehicle-mounted device are coupled together through the bus system 404.
  • the communication interface 403 is used for information transmission with external devices. It can be understood that the bus system 404 is used to implement connection and communication between these components.
  • the bus system 404 also includes a power bus, a control bus, and a status signal bus. However, for the sake of clarity, various buses are marked as the bus system 404 in FIG. 4.
  • the memory 402 in this embodiment may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memory.
  • the memory 402 stores the following elements, executable units or data structures, or a subset of them, or an extended set of them: operating systems and applications.
  • the operating system includes various system programs, such as a framework layer, a core library layer, and a driver layer, which are used to implement various basic services and process hardware-based tasks.
  • Application programs including various application programs, such as Media Player, Browser, etc., are used to implement various application services.
  • a program that implements the method of giving way for an intelligent driving vehicle provided by an embodiment of the present disclosure may be included in an application program.
  • the processor 401 calls a program or instruction stored in the memory 402, specifically, it may be a program or instruction stored in an application program.
  • the processor 401 is used for the steps of the various embodiments of the method for giving way to a smart driving vehicle. .
  • the smart driving vehicle yielding method provided by the embodiment of the present disclosure may be applied to the processor 401 or implemented by the processor 401.
  • the processor 401 may be an integrated circuit chip with signal processing capability. In the implementation process, the steps of the foregoing method can be completed by hardware integrated logic circuits in the processor 401 or instructions in the form of software.
  • the aforementioned processor 401 may be a general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a ready-made programmable gate array (Field Programmable Gate Array, FPGA) or other Programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.
  • the general-purpose processor may be a microprocessor or the processor may also be any conventional processor or the like.
  • the steps of the intelligent driving vehicle yield method provided by the embodiments of the present disclosure can be directly embodied as execution and completion by a hardware decoding processor, or by a combination of hardware and software units in the decoding processor.
  • the software unit may be located in a mature storage medium in the field such as random access memory, flash memory, read-only memory, programmable read-only memory or electrically erasable programmable memory, registers.
  • the storage medium is located in the memory 402, and the processor 401 reads the information in the memory 402 and completes the steps of the method in combination with its hardware.
  • Fig. 5 is an exemplary flow chart of a method for giving way for an intelligent driving vehicle shown in some embodiments of the present application.
  • the execution body of the method is a vehicle-mounted device.
  • the execution body of the method is an intelligent driving system supported by the vehicle-mounted device.
  • the method for giving way for a smart driving vehicle can be applied to a vehicle giving way scheme for a smart driving vehicle on a special road during the driving process.
  • the special reasoning includes, but is not limited to, a two-way single lane.
  • multiple road monitoring units obtain monitoring information of the special road.
  • multiple road monitoring units are arranged on special roads at intervals.
  • each road monitoring unit can be installed separately or embedded in other equipment such as traffic light equipment or road signs. The embodiments of the present disclosure do not limit the specific equipment structure of the road monitoring unit. Devices with unit functions are within the scope of this application.
  • the intelligent driving vehicle (for example, the in-vehicle equipment or vehicle control device of the intelligent driving vehicle, or the intelligent driving system supported by the in-vehicle equipment) is based on the state information of the intelligent driving vehicle Determine the corresponding road monitoring unit.
  • the state information of the smart driving vehicle includes, but is not limited to, position information, heading, speed, and driving state of the smart driving vehicle.
  • the location information of the intelligent driving vehicle may be obtained through the sensing module of the intelligent driving system.
  • the heading, speed, and driving state of the intelligent driving vehicle can be obtained through the sensing module of the intelligent driving system.
  • the intelligent driving vehicle obtains corresponding road monitoring unit information based on the location information of the intelligent driving vehicle and the high-precision map. In some embodiments, the intelligent driving vehicle obtains corresponding road monitoring unit information based on the position information, speed, heading, and high-precision map of the intelligent driving vehicle. In some embodiments, the high-precision map includes, but is not limited to, location information, road network information, and road network identification information of the road monitoring unit. In some embodiments, the high-precision map of the embodiment of the present disclosure may be the high-precision map used in the intelligent driving field described in the foregoing content. Through the use of high-precision maps, the corresponding road monitoring unit information can be obtained accurately and in real time. In some embodiments, the determination of the corresponding road monitoring unit by the intelligent driving vehicle can be understood as acquiring the identification and location information of the required road monitoring unit.
  • the intelligent driving system receives monitoring information of the corresponding road monitoring unit.
  • the intelligent driving system may send an acquisition request to the server, wherein the acquisition request includes at least the corresponding road monitoring unit information.
  • the intelligent driving system may receive a response from the server, wherein the response includes at least monitoring information of each road monitoring unit in the corresponding road monitoring unit.
  • the server receives the monitoring information reported by the road monitoring unit periodically or in real time. After receiving the acquisition request, the server screens the latest monitoring information of the corresponding road monitoring unit according to the corresponding road monitoring unit information in the acquisition request.
  • the server may be a cloud server.
  • the acquisition request includes at least the location information of the corresponding road monitoring unit or the identification of the corresponding road monitoring unit.
  • the response includes but is not limited to: the monitoring information of the corresponding road monitoring unit, the location information of the corresponding road monitoring unit, the identification of the corresponding road monitoring unit, the status information of the corresponding road monitoring unit, the priority of the vehicle in the monitoring information of the road monitoring unit, etc. .
  • the state information of the road monitoring unit may include, but is not limited to, the normal state of the road monitoring unit and the off state of the road monitoring unit.
  • the monitoring information includes, but is not limited to, at least one of passing vehicle information, passing vehicle priority, passing vehicle location, passing vehicle heading, and passing vehicle type.
  • the server may receive the information reported by the road monitoring unit in real time, and send the monitoring information of the road monitoring unit to intelligent driving vehicles within the road range in real time or periodically.
  • the intelligent driving vehicle can obtain the monitoring information of the road monitoring unit through the server without omission, so as to ensure the accuracy and real-time nature of the monitoring information obtained by the intelligent driving vehicle.
  • the monitoring information obtained by the intelligent driving vehicle may be the monitoring information recognized and processed by a server such as a cloud server, or it may be the monitoring information of a road monitoring unit directly forwarded by a server such as a cloud server.
  • the priority of the passing vehicles in the monitoring information may be that the road monitoring unit marks the priority of the passing vehicles according to the pre-priority judgment rules after monitoring the passing vehicles, so as to obtain the vehicles of the passing vehicles within the monitoring range of the road monitoring unit. priority.
  • the priority of the passing vehicle in the monitoring information can be understood as the server marking the priority of the passing vehicle according to the pre-priority judgment rule, so that the monitoring information sent to the vehicle-mounted device carries the monitoring range of the road monitoring unit Vehicle priority of passing vehicles.
  • the intelligent driving system determines whether there is a passing vehicle that needs to avoid based on the monitoring information. In some embodiments, it is determined whether there is a passing vehicle that needs to be avoided based on at least one of the passing vehicle priority, the passing vehicle location, and the passing vehicle heading. In some embodiments, the intelligent driving system determines whether there is a passing vehicle that needs to avoid based on the priority of passing vehicles in the monitoring information. In some embodiments, the intelligent driving system determines whether there is a passing vehicle that needs to avoid based on the priority of the passing vehicle and the heading of the passing vehicle in the monitoring information.
  • the heading of the intelligent driving vehicle is opposite to the heading of the passing vehicle, and the priority of the passing vehicle is higher than the priority of the intelligent driving vehicle, and the intelligent driving vehicle needs to avoid.
  • the heading of the intelligent driving vehicle is the same as the heading of the passing vehicle, and the priority of the passing vehicle is higher than the priority of the intelligent driving vehicle.
  • the intelligent driving vehicle Need to avoid.
  • the intelligent driving system controls the intelligent driving vehicle to avoid when there is a passing vehicle that needs to avoid.
  • the priority of the passing vehicle is higher than the priority of the intelligent driving vehicle, and the intelligent driving vehicle needs to avoid.
  • the intelligent driving system plans to generate an avoidance path of the intelligent driving vehicle, and controls the intelligent driving vehicle to drive along the avoidance path.
  • the intelligent driving system can periodically or in real time determine the corresponding road monitoring unit, and receive the monitoring information of the corresponding road monitoring unit, and then determine whether there is a passing vehicle that needs to be avoided based on the monitoring information of the road monitoring unit If it exists, the intelligent driving vehicle can be controlled to avoid, and then take the initiative to give way in advance to improve the efficiency of traffic and ensure traffic safety.
  • Fig. 6 is a signaling diagram of yet another method for giving way for an intelligent driving vehicle according to some embodiments of the present application.
  • the execution body of the method shown in FIG. 6 is the vehicle-mounted device.
  • the execution body of the method may be the intelligent driving system supported by the vehicle-mounted device.
  • the execution body of the method may be the vehicle-mounted device. Supported smart driving vehicle yield device.
  • the intelligent driving system obtains corresponding road monitoring unit information based on the position information of the intelligent driving vehicle and the high-precision map.
  • the corresponding road monitoring unit information includes: a device for collecting road information on the road where the intelligent driving vehicle is traveling, for example, cameras periodically installed on both sides of the road, or periodically installed Lidar.
  • the road information collected by the road monitoring unit includes, but is not limited to, the type of the passing vehicle, the state of the passing vehicle, the direction of the passing vehicle, and other information of the passing vehicle such as color, license plate number, and so on.
  • the intelligent driving system directly wirelessly communicates with the obtained corresponding road monitoring unit to obtain the monitoring information of the corresponding road monitoring unit.
  • the embodiment of the present disclosure provides a way to obtain the monitoring information of the corresponding road monitoring unit by means of a cloud server.
  • the intelligent driving system sends an acquisition request including the information of the corresponding road monitoring unit to the cloud server, and receives the response of the road monitoring unit monitoring information returned by the cloud server.
  • the corresponding road monitoring unit information in the acquisition request includes, but is not limited to: the identification of the road monitoring unit, the name of the road monitoring unit, the location information of the road monitoring unit, and the like.
  • the response includes the monitoring information of the corresponding road monitoring unit forwarded by the cloud server, or the monitoring information after the cloud server processes the monitoring information of the corresponding road monitoring unit.
  • the monitoring information includes but is not limited to at least one of passing vehicle information, passing vehicle priority, passing vehicle location, passing vehicle heading, and passing vehicle type.
  • the corresponding road monitoring unit may include all road monitoring units within a preset range in front of the driving direction of the smart driving vehicle, and all road monitoring units within a preset range behind the driving direction of the smart driving vehicle. In the embodiment of the present disclosure, each road monitoring unit periodically or in real time uploads the monitoring information within the monitoring range to the cloud server.
  • the intelligent driving system determines whether there is a passing vehicle that needs to avoid based on the monitoring information.
  • the passing vehicles may be fire trucks, ambulances, and other vehicles with higher priority than smart driving vehicles.
  • the priority of the vehicle is increased and the corresponding road monitoring unit is notified.
  • the passing vehicle may be another vehicle, such as a large truck, Or unmanned vehicles and so on. If there is a priority of a passing vehicle in the monitoring information that is higher than the priority of an intelligent driving vehicle of the current intelligent driving system, it is necessary to make an avoidance, and step 605 is executed. If the priority of the passing vehicles in the monitoring information of all the corresponding road monitoring units is not higher than the priority of the current intelligent driving system, there is no need to avoid, and the process of obtaining the corresponding road monitoring unit in step 601 is repeated.
  • the intelligent driving system plans to generate an avoidance path of the intelligent driving vehicle.
  • the intelligent driving system plans and generates an avoidance path based on the current position information of the intelligent driving vehicle and the information of the allowable area.
  • the yieldable area is an area where temporary parking is possible on the current driving road marked on the high-precision map.
  • the intelligent driving system controls the intelligent driving vehicle to drive along the avoidance path.
  • the intelligent driving system controls the intelligent driving vehicle to drive quickly based on the constraints (such as the maximum speed limit) on the current driving road.
  • the avoidance path planned by the intelligent driving system is the path with the shortest distance from the current position. In some embodiments, if the intelligent driving vehicle is driving on a two-way single lane, the avoidance path is the path that is consistent with the current direction of the intelligent driving vehicle and is the shortest distance from the current position of the intelligent driving vehicle.
  • the intelligent driving system In step 607, the intelligent driving system generates a planned route based on the destination and the current position of the intelligent driving vehicle after completing the avoidance based on the avoidance path.
  • the avoidance path is the previously planned avoidance path, and the destination can be understood as the destination before the planned avoidance path.
  • the corresponding road monitoring unit is obtained through the state information of the intelligent driving vehicle, and according to the monitoring information of the road monitoring unit, it is determined whether there is a passing vehicle that needs to be avoided, and when it exists, the avoidance path is planned and generated, and then
  • the intelligent driving vehicle can be controlled to evade according to the evasive path, which realizes the active evasive action taken in advance, improves the traffic efficiency and ensures the traffic safety.
  • the two sides in FIGS. 7A to 7E are two-way two-lane, and the middle section is two-way single-lane. Different avoidance points are set on the two-way two-lane and the two-way single-lane.
  • the passing vehicles in the embodiments of the present disclosure are fire trucks with higher priority than smart driving vehicles.
  • the smart driving vehicles are at points A and B. Runs back and forth between, where,
  • Section I is a two-way two-lane
  • Section II is a one-way two-lane
  • Section III is a two-way two-lane
  • RSU-A, RSU-B, and RSU-C are respectively installed in the positions shown in Fig. 7A, and RSU-A, RSU-B, and RSU-C respectively monitor the traffic conditions of vehicles within a certain range of their respective positions.
  • RSU can be a camera, lidar or other device that can sense the position, speed, heading, and type of vehicles in the monitoring range;
  • S1 to S5 in Fig. 7A to Fig. 7E are respectively temporary stops.
  • Section II is a two-way single-lane, when RSU detects that there are high-priority fire trucks in this area, intelligent driving vehicles need to take the initiative to avoid.
  • the specific yielding scenarios can be divided into the following four types:
  • the demand for yielding in the above four situations is that the smart driving system of the smart driving vehicle makes decisions based on the monitoring information of the road monitoring unit and plans the avoidance path to realize the avoidance operation of high-priority vehicles and ensure traffic safety.
  • the intelligent driving system makes decisions based on the monitoring information of the road monitoring unit, that is, through effective collaboration between vehicles and roads, not only the sensor of the intelligent driving vehicle is used for sensing, thereby improving traffic efficiency and ensuring traffic safety.
  • the embodiments of the present disclosure provide a vehicle yielding system, including: a server, the vehicle control device/intelligent driving system/vehicle equipment/smart driving vehicle yielding device mentioned in any of the above disclosed embodiments, and multiple Road monitoring unit;
  • the road monitoring unit interacts with the server, and the server interacts with the on-board control device/intelligent driving system/on-board equipment/intelligent driving vehicle yielding device.
  • the smart driving system is a system supported by the on-board equipment
  • the smart driving vehicle yield device is a component/module in the smart driving system
  • the smart driving system is a component/module in the on-board control device.
  • the road monitoring unit is used to monitor the passing vehicles within the monitoring range of the road on which the monitoring results include, but are not limited to, the location, speed, heading, type of passing vehicles, etc., and the monitoring results.
  • the location information of the road monitoring unit and the status information of the road monitoring unit are uploaded to the server/cloud server in a fixed time period.
  • the road monitoring unit may be a camera, a lidar, a traffic light, or other equipment capable of acquiring monitoring information within a monitoring range on the road.
  • the server is used to receive the monitoring information reported by the road monitoring unit in each location, and filter the monitoring information of the corresponding road monitoring unit and send it to the on-board equipment/smart driving system according to the corresponding road monitoring unit reported by the smart driving system;
  • the intelligent driving system is used to obtain the information of the corresponding road monitoring unit required by the current position and the position information of each road monitoring unit recorded in the high-precision map, and report it to the server.
  • the intelligent driving system updates whether there are high-priority vehicles in the corresponding area according to the acquired monitoring information, so as to make the corresponding avoidance decision and generate the corresponding avoidance path.
  • the priority of the passing vehicle may be adjusted or set by the intelligent driving system of the passing vehicle, or the priority of the passing vehicle is determined by the road monitoring unit according to the type of the passing vehicle.
  • the intelligent driving system may be a program running in an in-vehicle device/vehicle control device or a component in an in-vehicle device/vehicle control device.
  • the server is a background server or a control platform or a cloud server.
  • the embodiments of the present disclosure are applied to special roads. Due to the limited sensing distance of the own vehicle’s sensors, it is necessary to give way to high-priority vehicles in advance based on the monitoring information provided by the road monitoring unit. Through effective coordination between vehicles and roads, Improve traffic efficiency and ensure traffic safety.
  • the vehicle yielding system of the embodiment of the present disclosure can improve the traffic efficiency on the road where the intelligent driving vehicle is located, while ensuring the safety of the vehicle.
  • the embodiments of the present disclosure also provide a non-transitory computer-readable storage medium, which stores a program or instruction, and the program or instruction causes a computer to execute, for example, various embodiments of the intelligent driving vehicle yield method To avoid repeating the description, I won’t repeat them here.
  • the method according to the above embodiment can be implemented by means of software plus the necessary general hardware platform, of course, it can also be implemented by hardware, but in many cases the former is Better implementation.
  • the technical solutions of the embodiments of the present disclosure can be embodied in the form of software products in essence or the parts that contribute to the prior art.
  • the computer software products are stored in a storage medium (such as ROM/RAM, magnetic A disc or an optical disc) includes several instructions to make a terminal device (which can be a mobile phone, a computer, a server, or a network device, etc.) execute the methods described in the various embodiments of the present application.
  • a yielding action is taken in advance for vehicles that need to evade, through effective coordination between vehicles and roads, traffic efficiency is improved, traffic safety is ensured, and it has industrial applicability.

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Human Computer Interaction (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Traffic Control Systems (AREA)

Abstract

一种智能驾驶车辆（003）让行方法、装置及车载设备，智能驾驶车辆（003）行驶在特殊道路，特殊道路配置有多个道路监测单元（002），方法包括：基于智能驾驶车辆（003）的状态信息确定相应道路监测单元（002）；接收相应道路监测单元（002）的监测信息；基于监测信息确定是否存在需要避让的通行车辆（004）；若存在需要避让的通行车辆（004），控制智能驾驶车辆（003）进行避让，通过车与路之间有效协同，提高通行效率，保证交通安全。

Description

一种智能驾驶车辆让行方法、装置及车载设备 技术领域
本公开实施例涉及智能驾驶领域，具体涉及一种智能驾驶车辆让行方法、装置及车载设备。
背景技术
随着车辆智能化、网联化技术的发展，基于车路协同的无人车自动驾驶技术逐渐成为智能交通研究领域的一个热点。
车路协同技术采用先进的无线网络技术(包括蜂窝网通信、无线通信、4G和5G等通信技术)进行数据传输，实现道路-云端-车辆之间的实时数据交换，从而实现车辆的主动安全控制，充分实现车与车、车与路之间有效协同，从而提高通行效率，保证交通安全。
而在某些特殊场景下，由于道路本身的特性，例如道路是单车道，但可以双向行驶，在规划不合理的情况下，会出现拥堵或者堵塞的情况。因此针对这种特殊路况需要特殊规划，现有技术中并没有针对这种特殊路况提供任何有效的解决方案。
上述对问题的发现过程的描述，仅用于辅助理解本发明的技术方案，并不代表承认上述内容是现有技术。
发明内容
为了解决现有技术存在的至少一个问题，本申请的至少一个实施例提供了一种智能驾驶车辆让行方法、装置及车载设备。
第一方面，本公开实施例提出一种智能驾驶车辆让行方法，其中，所述智能驾驶车辆行驶在特殊道路，所述特殊道路配置有多个道路监测单元，方法包括：
基于所述智能驾驶车辆的状态信息确定相应道路监测单元；
接收所述相应道路监测单元的监测信息；
基于所述监测信息确定是否存在需要避让的通行车辆；
若存在需要避让的通行车辆，控制智能驾驶车辆进行避让。
第二方面，本公开实施例还提出一种车载设备，包括:
处理器和存储器；
所述处理器通过调用所述存储器存储的程序或指令，用于执行如第一方面所述方法的步骤。
第三方面，本公开实施例还提出一种非暂态计算机可读存储介质，所述非暂态计算机可读存储介质存储程序或指令，所述程序或指令使计算机执行如第一方面所述方法的步骤。
第四方面，本公开实施例还提出一种智能驾驶车辆让行装置，所述智能驾驶车辆让行装置所属的智能驾驶车辆行驶在特殊道路，所述特殊道路配置有多个道路监测单元，所述智能驾驶车辆让行装置包括：
确定单元，用于基于所述智能驾驶车辆的状态信息确定相应道路监测单元；
接收单元，用于接收所述相应道路监测单元的监测信息；
避让单元，用于基于所述监测信息确定是否存在需要避让的通行车辆；在存在需要避让的通行车辆时，控制智能驾驶车辆进行避让。
第五方面，本公开实施例还提出一种车辆让行***，包括：服务器，如第四方面任意一个实施例中所述的智能驾驶车辆让行装置和配置在道路上的多个道路监测单元；
所述道路监测单元与所述服务器交互，所述服务器与所述智能驾驶车辆让行装置交互。
可见，在本公开实施例的至少一个实施例中，通过智能驾驶车辆的状态信息获取相应的道路监测单元，并依据道路监测单元的监测信息，确定是否存在需要避让的通行车辆，在存在时，可控制智能驾驶车辆避让，进而能够提前采取主动让行动作，提高通行效率，保证交通安全。
在本公开实施例中根据道路监测单元的监测信息做决策，即通过车与路之间的有效协同，非仅靠智能驾驶车辆自身的传感器进行感知，从而提高通行效率，保证交通安全。
附图说明
为了更清楚地说明本公开实施例的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本申请的一些实施例，对于本领域普通技术人员来讲，还可以根据这些附图获得其他的附图。
图1是本公开实施例提供的一种智能驾驶车辆行驶的场景图；
图2是本公开实施例提供的一种智能驾驶车辆的整体架构图；
图3A是本公开实施例提供的一种智能驾驶***的框图；
图3B是本公开实施例提供的一种让行模块的框图；
图4是本公开实施例提供的一种车载设备的框图；
图5是本公开实施例提供的一种智能驾驶车辆让行方法的流程图；
图6是本公开实施例提供的一种智能驾驶车辆让行方法的信令图；
图7A是本公开实施例提供的又一种智能驾驶车辆行驶的场景图；
图7B至图7E分别是根据图7A所示的场景中的智能驾驶车辆让行的示意图。
具体实施方式
下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本申请一部分实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员所获得的所有其他实施例，都属于本申请保护的范围。
为了能够更清楚地理解本申请的上述目的、特征和优点，下面结合附图和实施例对本申请作进一步的详细说明。可以理解的是，所描述的实施例是本申请的一部分实施例，而不是全部的实施例。此处所描述的具体实施例仅仅用于解释本申请，而非对本申请的限定。基于所描述的本申请的实施例，本领域普通技术人员所获得的所有其他实施例，都属于本申请保护的范围。
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。
针对现有技术中部分道路规划不合理，导致通行车辆经常出现拥堵或堵塞的 情况，使得交通通行效率低的问题，本公开实施例提供一种智能驾驶车辆让行方案，实现在特殊道路上行驶时，依据道路监测单元的监测信息，确定是否存在需要避让的通行车辆，在存在时，可控制智能驾驶车辆避让，进而能够提前采取主动让行动作，提高通行效率，保证交通安全。
本公开实施例提供一种智能驾驶车辆让行方案，可应用于智能驾驶车辆和多种场景。例如当智能驾驶车辆和一些紧急车辆(例如，救护车，消防车等)迎面行驶时，智能驾驶车辆需要对该紧急车辆进行避让。在一些实施例中，当智能驾驶车辆行驶在特殊道路上时，也会遇到需要进行避让或让行的情况。例如，当所述特殊道路为双向单车道时，需要对同向行驶且位于智能驾驶车辆行驶方向后方的通行车辆(如消防车)进行避让，或者需要对迎面行驶的通行车辆(如救援车)进行避让。另外，在上述场景中，可配置有多个道路监测单元。在一些实施例中，道路监测单元可为配置在道路两侧的设备，用于收集监控范围内的监测信息。在一些实施例中，道路监测单元也可嵌入到其他设备中，如嵌入在红绿灯设备、摄像头或其他道路指示牌上。
图1是本公开的一些实施例中一种智能驾驶车辆行驶的场景，如图1所示，所述场景中包括云端服务器001、道路监测单元(RSU：Road Side Unit)002，智能驾驶车辆003及通行车辆004。在一些实施例中，云端服务器001可用于获取道路监测单元002和智能驾驶车辆003的信息，以及可以发送信息至智能驾驶车辆003。在一些实施例中，云端服务器001可以根据智能驾驶车辆003的信息将道路监测单元002中的与智能驾驶车辆003相对应的监测信息发送给智能驾驶车辆003。在一些实施例中，所述的云端服务器001可以是一个服务器，也可以是一个服务器群组。服务器群组可以是集中式的，也可以是分布式的。服务器可以是本地的或远程的。
道路监测单元002可以用于收集道路监测信息。在一些实施例中，道路监测单元002可以是环境感知传感器，例如，摄像头、激光雷达等，也可以是道路设备，例如V2X设备，路边红绿灯装置等。在一些实施例中，道路监测单元002可以监控隶属于相应道路监测单元002的道路情况，例如，通过车辆的类型、速度、优先级别等。道路监测单元002在收集到道路监测信息后，可将所述道路监测信 息发送给云端服务器，也可以发送给通过道路的智能驾驶车辆。
智能驾驶车辆003用于根据周围环境生成控制信息并控制车辆行驶。在一些实施例中，所述智能驾驶车辆003可以发送请求信息至云端服务器，用于获取云端服务器的相关信息。在一些实施例中，请求信息包括但不限于当前车辆位姿、与车辆对应的相应道路监测单元信息等。在一些实施例中，所述智能驾驶车辆003可以接收来自云端服务器001的反馈信息，其中所述反馈信息包括但不限于相应道路监测单元的道路监测信息。在一些实施例中，智能驾驶车辆003可以根据相应道路监测单元的道路监测信息，实现智能驾驶车辆003的规划控制信息，例如，在特殊道路上对部分通行车辆的避让，由此，提高了特殊道路上车辆的通行效率，同时保证了交通安全。
通行车辆004是在道路上通行的各种车辆。在一些实施例中，所述的通行车辆004可以是智能驾驶车辆，也可以是人工驾驶车辆，还可以是不同级别的自动驾驶车辆。通行车辆004也可以是包括但不限于小车、中型车、大型车、货物车、救护车、消防车等车辆。在一些实施例中，不同的通行车辆拥有不同的优先级，例如，救护车或者消防车的优先级比普通车辆的优先级就要高。
图2是本公开的一些实施例中一种智能驾驶车辆的整体架构图，如图2所示，智能驾驶车辆包括：传感器组、智能驾驶***100、车辆底层执行***以及其他可用于驱动智能驾驶车辆和控制智能驾驶车辆运行的部件。
传感器组，用于采集智能驾驶车辆外界环境的数据和探测智能驾驶车辆的位置数据。传感器组例如包括但不限于摄像头、激光雷达、毫米波雷达、GPS(Global Positioning System，全球定位***)和IMU(Inertial Measurement Unit，惯性测量单元)中的至少一个。
在一些实施例中，传感器组，还用于采集车辆的动力学数据，传感器组例如还包括但不限于车轮转速传感器、速度传感器、加速度传感器、方向盘转角传感器、前轮转角传感器中的至少一个。
智能驾驶***100，用于获取传感器组的数据，传感器组中所有传感器在智能驾驶车辆行驶过程中都以较高的频率传送数据。智能驾驶***，还用于与云端服务器无线通信，交互各种信息。智能驾驶***，还用于与道路监测单元无线通信， 交互各种信息。
智能驾驶***100，还用于基于传感器组的数据进行环境感知和车辆定位，并基于环境感知信息和车辆定位信息进行路径规划和决策，以及基于规划的路径生成车辆控制指令，从而控制车辆按照规划路径行驶。
智能驾驶***100，还用于基于所述智能驾驶车辆的状态信息确定相应道路监测单元；接收所述相应道路监测单元的监测信息；基于所述监测信息确定是否存在需要避让的通行车辆；若存在需要避让的通行车辆，控制智能驾驶车辆进行避让。
在一些实施例中，智能驾驶***100可以为软件***、硬件***或者软硬件结合的***。例如，智能驾驶***100是运行在操作***上的软件***，车载硬件***是支持操作***运行的硬件***。本公开的智能驾驶***可为智能驾驶车辆的车载设备或者车载控制装置中组件，或者为智能驾驶车辆的车载设备或者车载控制装置。
车辆底层执行***，用于接收车辆控制指令，实现对智能驾驶车辆行驶的控制。车辆底层执行***包括但不限于：转向***、制动***和驱动***。转向***、制动***和驱动***等属于车辆领域成熟的结构，在此不再赘述。
在一些实施例中，智能驾驶车辆还可包括图2中未示出的车辆CAN总线，车辆CAN总线连接车辆底层执行***。智能驾驶***100与车辆底层执行***之间的信息交互通过车辆CAN总线进行传递。
在一些实施例中，智能驾驶车辆既可以通过驾驶员又可以通过智能驾驶***100控制车辆行驶。在人工驾驶模式下，驾驶员通过操作控制车辆行驶的装置驾驶车辆，控制车辆行驶的装置例如包括但不限于制动踏板、方向盘和油门踏板等。控制车辆行驶的装置可直接操作车辆底层执行***控制车辆行驶。
在一些实施例中，智能驾驶车辆也可以为无人车，智能驾驶车辆的驾驶控制由智能驾驶***来执行。
图3A为本公开实施例提供的一种智能驾驶***200的框图。在一些实施例中，智能驾驶***200可以实现为图2中的智能驾驶***100或者智能驾驶***100的一部分，用于控制车辆行驶。
如图3A所示，智能驾驶***200包括但不限于：感知模块201、规划模块202、控制模块203、让行模块204以及其他一些可用于智能驾驶的模块。
在一些实施例中，感知模块201用于获取的传感器数据、V2X(Vehicle to X，车用无线通信)数据、高精度地图等数据。在一些实施例中，感知模块201用于基于获取的传感器数据、V2X(Vehicle to X，车用无线通信)数据、高精度地图等数据中的至少一种，进行环境感知与定位。在一些实施例中，感知模块201用于生成感知定位信息，实现对障碍物感知、摄像头图像的可行驶区域识别以及车辆的定位等。
环境感知(Environmental Perception)可以理解为对于环境的场景理解能力，例如障碍物的位置，道路标志/标记的检测，行人/车辆的检测等数据的语义分类。
在一些实施例中，环境感知可采用融合摄像头、激光雷达、毫米波雷达等多种传感器的数据进行环境感知。
定位(Localization)属于感知的一部分，是确定智能驾驶车辆相对于环境的位置的能力。
定位可采用：GPS定位，GPS的定位精度在数十米到厘米级别，定位精度高；定位还可采用融合GPS和惯性导航***(Inertial Navigation System)的定位方法。定位还可采用SLAM(Simultaneous Localization And Mapping，同步定位与地图构建)，SLAM的目标即构建地图的同时使用该地图进行定位，SLAM通过利用已经观测到的环境特征确定当前车辆的位置以及当前观测特征的位置。
V2X是智能交通运输***的关键技术，使得车与车、车与基站、基站与基站之间能够通信，从而获得实时路况、道路信息、行人信息等一系列交通信息，提高智能驾驶安全性、减少拥堵、提高交通效率、提供车载娱乐信息等。
高精度地图是智能驾驶领域中使用的地理地图，与传统地图相比，不同之处在于：1)高精度地图包括大量的驾驶辅助信息，例如依托道路网的精确三维表征：包括交叉路口局和路标位置等；2)高精地图还包括大量的语义信息，例如报告交通灯上不同颜色的含义，又例如指示道路的速度限制，以及左转车道开始的位置；3)高精度地图能达到厘米级的精度，确保智能驾驶车辆的安全行驶。
规划模块202用于基于感知定位模块生成的感知定位信息，进行路径规划和 决策。
在一些实施例中，规划模块202用于基于感知定位模块生成的感知定位信息，并结合V2X数据、高精度地图等数据、道路监测单元的信息、云端服务器的信息中的至少一种，进行路径规划和决策。
在一些实施例中，规划模块202用于规划路径、决策：行为(例如包括但不限于跟车、超车、停车、绕行等)、车辆航向、车辆速度、车辆的期望加速度、期望的方向盘转角等，生成规划决策信息。
控制模块203用于基于规划模块生成的规划决策信息，进行路径跟踪、轨迹跟踪或路径避让。
在一些实施例中，控制模块203用于生成车辆底层执行***的控制指令，并下发控制指令，以使车辆底层执行***控制车辆按照期望路径行驶，例如通过控制方向盘、刹车以及油门对车辆进行横向和纵向控制。
在一些实施例中，控制模块203还用于基于路径跟踪算法计算前轮转角。
在一些实施例中，路径跟踪过程中的期望路径曲线与时间参数无关，跟踪控制时，可以假设智能驾驶车辆以当前速度匀速前进，以一定的代价规则使行驶路径趋近于期望路径；而轨迹跟踪时，期望路径曲线与时间和空间均相关，并要求智能驾驶车辆在规定的时间内到达某一预设好的参考路径点。
路径跟踪不同于轨迹跟踪，不受制于时间约束，只需要在一定误差范围内跟踪期望路径。
让行模块204用于基于所述智能驾驶车辆的状态信息确定相应道路监测单元；接收所述相应道路监测单元的监测信息；基于所述监测信息确定是否存在需要避让的通行车辆；若存在需要避让的通行车辆，用于规划生成所述智能驾驶车辆的避让路径，控制智能驾驶车辆按照避让路径行驶。例如，控制智能驾驶车辆在双向单车道上按照避让路径行驶。
在一些实施例中，让行模块204的功能可集成到感知模块201、规划模块202或控制模块203中，也可配置为与智能驾驶***相独立的模块，让行模块204可以为软件模块、硬件模块或者软硬件结合的模块。例如，让行模块204是运行在操作***上的软件模块，车载硬件***是支持操作***运行的硬件***。
图3B为本公开实施例提供的一种智能驾驶车辆让行装置300的框图。在一些实施例中，智能驾驶车辆让行装置300可以实现为图3A中的让行模块204或者让行模块204的一部分。
如图3B所示，智能驾驶车辆让行装置300包括确定单元301、接收单元302、避让单元303以及其他一些可用于执行避让操作的单元。
确定单元301，用于基于所述智能驾驶车辆的状态信息确定相应道路监测单元；在一些实施例中，智能驾驶车辆的状态信息包括但不限于：位置信息及航向、速度、行驶目的地和行驶状态中的至少一种。在一些实施例中，确定单元301，还用于基于所述智能驾驶车辆的位置信息和高精度地图，获取相应道路监测单元信息。其中，所述高精度地图还包括道路监测单元的位置信息。在一些实施例中，确定单元301，还用于基于位置信息、航向、速度和高精度地图，获取相应道路监测单元信息。在一些实施例中，相应道路监测单元信息包括但不限于，道路监测单元位置信息、道路监测单元标识、道路监测单元数量中的至少一种。在一些实施例中，道路监测单元的监测信息可包括但不限于通行车辆信息、通行车辆优先级、通行车辆位置、通行车辆航向、通行车辆类型中的至少一种。
接收单元302，用于接收所述相应道路监测单元的监测信息；在一些实施例中，接收单元302还可以用于向云端服务器发送获取请求，所述获取请求至少包括所述相应道路监测单元信息；接收云端服务器的响应，所述响应至少包括所述相应道路监测单元中每个道路监测单元的监测信息。在一些实施例中，道路监测单元实时或周期性的获取监测信息，并将获取的监测信息实时或周期性的发送云端服务器，云端服务器接收获取请求之后，依据获取请求中相应道路监测单元信息，筛选相应道路监测单元的最新监测信息或预设时间段内的监测信息，将筛选的监测信息发送接收单元302。在一些实施例中，本公开实施例的智能驾驶车辆让行装置直接与相应道路监测单元无线通信，获取相应道路监测单元监测的最新的监测信息。避让单元303，用于基于所述监测信息确定是否存在需要避让的通行车辆；在存在需要避让的通行车辆时，控制智能驾驶车辆进行避让。在一些实施例中，避让单元303基于所述通行车辆优先级、通行车辆位置和通行车辆航向中的至少一种确定是否存在需要避让的通行车辆。在一些实施例中，当所述通行车辆优先 级高于所述智能驾驶车辆优先级，所述智能驾驶车辆需要进行避让。在一些实施例中，如救护车或消防车的优先级通常是高于道路上行驶的其他通行车辆，如大型车、智能驾驶车辆，小汽车等。本公开的实施例中，高优先级的通行车辆不限定只是救护车和消防车。通行车辆的优先级可以通过通行车辆的智能驾驶***/车载设备进行设置和调整的。在一些实施例中，通行车辆优先级与智能驾驶车辆的优先级相同，或者低于智能驾驶车辆时，智能驾驶车辆则不执行避让操作。在一些实施例中，若智能驾驶车辆通过车-车通信方式接收到通行车辆中至少一辆通行车辆优先级提高并高于所述智能驾驶车辆优先级，所述智能驾驶车辆需要进行避让。在一些实施例中，避让单元303，还用于规划生成所述智能驾驶车辆的避让路径；控制智能驾驶车辆按照避让路径行驶。
在一些实施例中，智能驾驶车辆让行装置300还包括图中未示出的路径规划单元，用于基于所述避让路径完成避让后，依据目的地和智能驾驶车辆当前位置，生成规划路径。在一些实施例中，所述目的地为生成避让路径之前智能驾驶***行驶路径的目的地。在一些实施例中，所述目的地还可以是用户更新后的目的地。在一些实施例中，若智能驾驶车辆处于无人驾驶状态，则智能驾驶车辆当前位置与避让路径中的避让点/让行点位置相同。
在本公开的实施例中，通过车与路之间的有效协同，非仅靠智能驾驶车辆的传感器数据，从而提高通行效率，保证交通安全。
在一些实施例中，智能驾驶车辆让行装置300中各单元的划分仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如确定单元301、接收单元302和避让单元303可以实现为一个单元；确定单元301、接收单元302或避让单元303也可以划分为多个子单元。可以理解的是，各个单元或子单元能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。专业技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能。
图4是本公开实施例提供的一种车载设备的结构示意图。车载设备可支持智能驾驶***的运行。
如图4所示，车载设备包括：至少一个处理器401、至少一个存储器402和至 少一个通信接口403。车载设备中的各个组件通过总线***404耦合在一起。通信接口403，用于与外部设备之间的信息传输。可理解，总线***404用于实现这些组件之间的连接通信。总线***404除包括数据总线之外，还包括电源总线、控制总线和状态信号总线。但为了清楚说明起见，在图4中将各种总线都标为总线***404。
可以理解，本实施例中的存储器402可以是易失性存储器或非易失性存储器，或可包括易失性和非易失性存储器两者。
在一些实施方式中，存储器402存储了如下的元素，可执行单元或者数据结构，或者他们的子集，或者他们的扩展集：操作***和应用程序。
其中，操作***，包含各种***程序，例如框架层、核心库层、驱动层等，用于实现各种基础业务以及处理基于硬件的任务。应用程序，包含各种应用程序，例如媒体播放器(Media Player)、浏览器(Browser)等，用于实现各种应用业务。实现本公开实施例提供的智能驾驶车辆让行方法的程序可以包含在应用程序中。
在本公开实施例中，处理器401通过调用存储器402存储的程序或指令，具体的，可以是应用程序中存储的程序或指令，处理器401用于智能驾驶车辆让行方法各实施例的步骤。
本公开实施例提供的智能驾驶车辆让行方法可以应用于处理器401中，或者由处理器401实现。处理器401可以是一种集成电路芯片，具有信号的处理能力。在实现过程中，上述方法的各步骤可以通过处理器401中的硬件的集成逻辑电路或者软件形式的指令完成。上述的处理器401可以是通用处理器、数字信号处理器(Digital Signal Processor，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现成可编程门阵列(Field Programmable Gate Array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
本公开实施例提供的智能驾驶车辆让行方法的步骤可以直接体现为硬件译码处理器执行完成，或者用译码处理器中的硬件及软件单元组合执行完成。软件单元可以位于随机存储器，闪存、只读存储器，可编程只读存储器或者电可擦写可编程存储器、寄存器等本领域成熟的存储介质中。该存储介质位于存储器402，处 理器401读取存储器402中的信息，结合其硬件完成方法的步骤。
图5是本申请的一些实施例所示的一种智能驾驶车辆让行方法的示例流程图。该方法的执行主体为车载设备，在一些实施例中，该方法的执行主体为车载设备所支持的智能驾驶***。
在一些实施例中，所述的智能驾驶车辆让行方法可应用于处于特殊道路的智能驾驶车辆在行驶过程中的车辆的让行方案。在一些实施例中，特殊道理包括但不限于双向单车道等。在一些实施例中，多个道路监测单元获取所述特殊道路的监测信息。在一些实施例中，多个道路监测单元间隔配置特殊道路上。在一些实施例中，每一个道路监测单元可单独设置，或嵌入其他设备中如红绿灯设备中或道路指示牌中，本公开实施例不对道路监测单元的具体设备结构进行限定，任何具有上述道路监测单元功能的设备都属于在本申请的范围。
如图5所示，在步骤501中，所述智能驾驶车辆(例如所述智能驾驶车辆的车载设备或车辆控制装置，或者车载设备所支持的智能驾驶***)基于所述智能驾驶车辆的状态信息确定相应道路监测单元。在一些实施例中，所述智能驾驶车辆的状态信息包括但不限于智能驾驶车辆的位置信息、航向、速度和行驶状态。在一些实施例中，智能驾驶车辆的位置信息可通过所述智能驾驶***的感知模块获得。在一些实施例中，智能驾驶车辆的航向、速度和行驶状态均可通过智能驾驶***的感知模块获得。
在一些实施例中，在步骤501中，智能驾驶车辆基于智能驾驶车辆的位置信息和高精度地图，获取相应道路监测单元信息。在一些实施例中，智能驾驶车辆基于智能驾驶车辆的位置信息、速度、航向和高精度地图，获取相应道路监测单元信息。在一些实施例中，所述高精度地图包括但不限于道路监测单元的位置信息、路网信息、路网标识信息等。在一些实施例中，本公开实施例的高精度地图可为前述内容中所述的智能驾驶领域中使用的高精度地图。通过使用高精度地图，以准确实时获取相应的道路监测单元信息。在一些实施例中，智能驾驶车辆确定相应的道路监测单元可理解为获取需要的道路监测单元的标识、位置信息等。
在步骤502中，所述智能驾驶***接收所述相应道路监测单元的监测信息。在一些实施例中，所述智能驾驶***可以向服务器发送获取请求，其中，所述获 取请求至少包括所述相应道路监测单元信息。在一些实施例中，所述智能驾驶***可以接收服务器的响应，其中所述响应至少包括所述相应道路监测单元中每个道路监测单元的监测信息。在一些实施例中，服务器周期性或实时接收道路监测单元上报的监测信息，服务器接收到获取请求后，依据获取请求中相应道路监测单元信息筛选对应的道路监测单元的最新监测信息。在一些实施例中，服务器可以为云端服务器。在一些实施例中，获取请求中至少包括相应道路监测单元的位置信息或相应道路监测单元的标识。响应中包括但不限于：相应道路监测单元的监测信息、相应道路监测单元的位置信息、相应道路监测单元的标识、相应道路监测单元的状态信息、道路监测单元的监测信息中车辆的优先级等。在一些实施例中，道路监测单元的状态信息可包括但不限于道路监测单元正常状态和道路监测单元关闭状态。
在一些实施例中，所述监测信息包括但不限于通行车辆信息、通行车辆优先级、通行车辆位置、通行车辆航向、通行车辆类型中的至少一种。
在一些实施例中，所述服务器可以实时接收道路监测单元上报的信息，并实时或周期性发送所述道路监测单元的监测信息至所述道路范围之内的智能驾驶车辆。在一些实施例中，智能驾驶车辆可通过服务器无遗漏的获取道路监测单元的监测信息，以保证智能驾驶车辆的获取的监测信息的准确性和实时性。
在一些实施例中，智能驾驶车辆车获得的监测信息可以是经过服务器如云端服务器识别并处理后的监测信息，也可以是服务器如云端服务器直接转发的道路监测单元的监测信息，本公开实施例不对其限定，根据实际需要选择。在一些实施例中，监测信息中通行车辆优先级可为道路监测单元在监测通行车辆后根据预先的优先级判断规则对通行车辆的优先级进行标注，获得道路监测单元监测范围内通行车辆的车辆优先级。在一些实施例中，监测信息中的通行车辆优先级可理解为服务器根据预先的优先级判断规则对通行车辆的优先级进行标注，使得向车载设备发送的监测信息中携带道路监测单元监测范围内通行车辆的车辆优先级。
在步骤503中，智能驾驶***基于所述监测信息确定是否存在需要避让的通行车辆。在一些实施例中，基于所述通行车辆优先级、通行车辆位置和通行车辆航向中的至少一种确定是否存在需要避让的通行车辆。在一些实施例中，智能驾 驶***基于监测信息中通行车辆优先级，确定是否存在需要避让的通行车辆。在一些实施例中，智能驾驶***基于监测信息中通行车辆优先级、通行车辆航向，确定是否存在需要避让的通行车辆。
在一些实施例中，智能驾驶车辆的航向与所述通行车辆航向相反，且所述通行车辆优先级高于所述智能驾驶车辆优先级，所述智能驾驶车辆需要进行避让。
在一些实施例中，智能驾驶车辆的航向与所述通行车辆航向相同，且所述通行车辆优先级高于所述智能驾驶车辆优先级，通行车辆位于智能驾驶车辆后方时，所述智能驾驶车辆需要进行避让。
在步骤504中，智能驾驶***在存在需要避让的通行车辆时，控制智能驾驶车辆进行避让。在一些实施例中，所述通行车辆优先级高于所述智能驾驶车辆优先级，所述智能驾驶车辆需要进行避让。在一些实施例中，智能驾驶***规划生成智能驾驶车辆的避让路径，控制智能驾驶车辆按照避让路径行驶。
在智能驾驶车辆行驶过程中，智能驾驶***可周期性或实时地确定相应道路监测单元，并接收相应道路监测单元的监测信息，进而依据道路监测单元的监测信息，确定是否存在需要避让的通行车辆，若存在时，可控制智能驾驶车辆避让，进而提前采取主动让行动作，提高通行效率，保证交通安全。
图6是根据本申请的一些实施例所示的又一种智能驾驶车辆让行方法的信令图。图6所示方法的执行主体为车载设备，在一些实施例中，该方法的执行主体可为车载设备所支持的智能驾驶***，在一些实施例中，该方法的执行主体可为车载设备所支持的智能驾驶车辆让行装置。
在步骤601中，智能驾驶***基于智能驾驶车辆的位置信息和高精度地图，获取相应道路监测单元信息。在一些实施例中，相应道路监测单元信息包括：用于收集智能驾驶车辆行驶道路上道路信息的设备，例如，周期性设置在道路两侧的摄像头，或者周期性设置的激光雷达等。在一些实施例中，道路监测单元收集的道路信息包括但不限于：通行车辆类型、通行车辆状态、通行车辆航向、通行车辆其他信息如颜色、车牌号等。在一些实施例中，智能驾驶***直接与获取的相应的道路监测单元无线通信，获取相应道路监测单元的监测信息。
本公开的实施例中提供一种借助于云端服务器获取相应道路监测单元的监测 信息的方式。在步骤602和步骤603中，智能驾驶***向云端服务器发送包括相应道路监测单元信息的获取请求，并接收云端服务器返回的道路监测单元监测信息的响应。在一些实施例中，获取请求中的相应道路监测单元信息包括但不限于：道路监测单元的标识、道路监测单元的名称、道路监测单元的位置信息等。在一些实施例中，响应中包括云端服务器转发的相应道路监测单元的监测信息，或者云端服务器对相应道路监测单元的监测信息进行处理后的监测信息。在一些实施例中，监测信息包括但不限于通行车辆信息、通行车辆优先级、通行车辆位置、通行车辆航向、通行车辆类型中的至少一种。在一些实施例中，相应道路监测单元可包括智能驾驶车辆行驶方向前方预设范围内的所有道路监测单元，和智能驾驶车辆行驶方向后方预设范围内的所有道路监测单元。在本公开的实施例中，每一道路监测单元周期性或实时地向云端服务器上传监控范围内的监测信息。
在步骤604中，智能驾驶***基于监测信息，确定是否存在需要避让的通行车辆。在一些实施例中，通行车辆可以是消防车、救护车等优先级高于智能驾驶车辆优先级的车辆。在一些实施例中，若存在通行车辆基于特殊情况(如失控、搭乘生命危险人员等)提高车辆优先级，并通知相应道路监测单元，此时的通行车辆可以是其他车辆，如大型运货车，或者无人车等。如果监测信息中存在通行车辆优先级高于当前智能驾驶***的智能驾驶车辆的优先级，则需要进行避让，执行步骤605。如果所有相应道路监测单元的监测信息中通行车辆的优先级都不高于当前智能驾驶***的智能驾驶车辆的优先级，则无需进行避让，重复上述步骤601中的获取相应道路监测单元的过程。
在步骤605中，智能驾驶***规划生成智能驾驶车辆的避让路径。在一些实施例中，智能驾驶***基于智能驾驶车辆当前位置信息和可让行区域的信息，规划生成避让路径。在一些实施例中，可让行区域为在高精度地图中标记的当前行驶道路上能够临时停车的区域。
在步骤606中，智能驾驶***控制智能驾驶车辆按照避让路径行驶。在一些实施例中，智能驾驶***基于当前行驶道路上的约束条件(如最高限速)控制智能驾驶车辆快速行驶。在一些实施例中，智能驾驶***规划的避让路径为与当前位置距离最短的路径。在一些实施例中，若智能驾驶车辆行驶在双向单车道上， 则避让路径是与当前智能驾驶车辆行驶方向一致的，且与智能驾驶车辆当前位置距离最短的路径。
在步骤607中，智能驾驶***基于所述避让路径完成避让后，依据目的地和智能驾驶车辆当前位置，生成规划路径。在一些实施例中，避让路径为前述规划的避让路径，目的地可理解为规划避让路径之前的目的地。
在本公开的实施例中，通过智能驾驶车辆的状态信息获取相应的道路监测单元，并依据道路监测单元的监测信息，确定是否存在需要避让的通行车辆，在存在时，规划生成避让路径，进而可控制智能驾驶车辆按照避让路径进行避让，实现了提前采取主动让行动作，提高了通行效率，保证了交通安全。
进一步地，为较好的理解，以下结合图7A至图7E的例子进行说明。
本公开实施例中，图7A至图7E中两边是双向双车道，中间一段是双向单车道，在双向双车道和双向单车道上设置了不同的避让点。
下面以在特殊道路上需要提前避让通行车辆的场景为例，本公开实施例中的通行车辆为优先级高于智能驾驶车辆的消防车，如图7A所示，智能驾驶车辆在A、B点之间来回运行，其中，
Section I为双向双车道，Section II为单向双车道，Section III为双向双车道；
三个RSU-A、RSU-B、RSU-C分别安装在如图7A所示位置，RSU-A、RSU-B、RSU-C分别监控各自所在位置一定范围内的车辆通行情况。这里RSU可以是摄像头、激光雷达或其他能够感知到监控范围内通行车辆的位置、速度、航向、车辆类型的设备；
图7A至图7E中S1～S5分别为临时停靠点。
由于Section II为双向单车道，当RSU监测到有高优先级的消防车在该区域时，智能驾驶车辆需要主动避让，具体让行场景可分为以下四种：
1)智能驾驶车辆在Section I区域，向B点行驶时，若RSU-C监测到消防车进入Section I，则智能驾驶车辆要到停靠点S1避让，如图7B所示；
2)智能驾驶车辆在Section I区域，向B点行驶时，若Section II区域内有消防车向A点行驶，则智能驾驶车辆要到停靠点S1避让，如图7C所示。
3)智能驾驶车辆在Section II区域，向B点行驶时，若Section II区域内有消防车向A点行驶或向B点行驶，则智能驾驶车辆要到最近的停靠点S2～S5进行避让，若已驶离了最近避让点(即马上离开Section II区域)，则不必再让行，如图7D所示。
4)智能驾驶车辆在Section II区域，向A点行驶时，若Section II区域内有消防车向A点行驶或向B点行驶，则智能驾驶车辆同样要到最近的停靠点S2～S5进行避让，若已驶离了最近避让点(即马上离开Section II区域)，则不必再让行，如图7E所示。
上述四种情况的让行需求是智能驾驶车辆的智能驾驶***根据道路监测单元的监测信息进行决策，并规划避让路径，实现对高优先级车辆的避让操作，保证了交通安全。
本公开实施例中智能驾驶***根据道路监测单元的监测信息做决策，即通过车与路之间的有效协同，非仅靠智能驾驶车辆自身的传感器进行感知，从而提高通行效率，保证交通安全。
本公开的实施例提供一种车辆让行***，包括：服务器、上述任意公开实施例提及的车载控制装置/智能驾驶***/车载设备/智能驾驶车辆让行装置和配置在道路上的多个道路监测单元；
所述道路监测单元与所述服务器交互，所述服务器与所述车载控制装置/智能驾驶***/车载设备/智能驾驶车辆让行装置交互，参见上述各方法实施例中的描述。在一些实施例中，智能驾驶***为车载设备所支持的***，智能驾驶车辆让行装置为智能驾驶***中的组件/模块，智能驾驶***为车载控制装置中的组件/模块。
在一些实施例中，道路监测单元，用于监测在所在道路的监控范围内的通行车辆，监测结果包括但不限于通行车辆的位置、速度、航向、通行车辆类型等信息，并将监测结果、道路监测单元的位置信息、道路监测单元的状态信息(是否正常工作)以固定时间周期上传到服务器/云端服务器。在一些实施例中，道路监测单元可以是摄像头、激光雷达、红绿灯等其他能够获取道路上监控范围内监测信息的设备。
服务器，用于接收各个位置的道路监测单元上报的监测信息，并根据智能驾驶***上报的其所需的相应道路监测单元，筛选相应道路监测单元的监测信息发到车载设备/智能驾驶***；
智能驾驶***，用于基于当前位置及高精度地图中所记录的各道路监测单元的位置信息，获取其所需的相应道路监测单元的信息，并上报到服务器。
智能驾驶***根据获取的监测信息，来更新相应区域内是否有高优先级车辆，从而做出对应的避让决策，并生成相应的避让路径。在一些实施例中，通行车辆的优先级可以是通行车辆智能驾驶***调整或设定的，或者通行车辆的优先级是道路监测单元依据通行车辆类型确定的。在一些实施例中，智能驾驶***可为运行在车载设备/车载控制装置中的程序或者为车载设备/车载控制装置中的组件。服务器为后台服务器或控制平台或云端服务器。
本公开实施例应用于特殊道路，由于本车的传感器感知距离有限，需要根据道路监测单元提供的监测信息，对高优先级的车辆提前采取让行动作，通过车与路之间有效协同，可提高通行效率，保证交通安全。
本公开实施例的车辆让行***在应用中，能够提高智能驾驶车辆所在道路上的通行效率，同时保证车辆行驶安全。
需要说明的是，对于前述的各方法实施例，为了简单描述，故将其都表述为一系列的动作组合，但是本领域技术人员应该知悉，本公开实施例并不受所描述的动作顺序的限制，因为依据本公开实施例，某些步骤可以采用其他顺序或者同时进行。其次，本领域技术人员也应该知悉，说明书中所描述的实施例均属于可选实施例，所涉及的动作和模块并不一定是本申请所必须的。
本公开实施例还提出一种非暂态计算机可读存储介质，所述非暂态计算机可读存储介质存储程序或指令，所述程序或指令使计算机执行如智能驾驶车辆让行方法各实施例的步骤，为避免重复描述，在此不再赘述。
通过以上的实施方式的描述，本领域的技术人员可以清楚地了解到根据上述实施例的方法可借助软件加必需的通用硬件平台的方式来实现，当然也可以通过硬件，但很多情况下前者是更佳的实施方式。基于这样的理解，本公开实施例的技术方案本质上或者说对现有技术做出贡献的部分可以以软件产品的形式体现出 来，该计算机软件产品存储在一个存储介质(如ROM/RAM、磁碟、光盘)中，包括若干指令用以使得一台终端设备(可以是手机，计算机，服务器，或者网络设备等)执行本申请各个实施例所述的方法。
需要说明的是，在本文中，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、物品或者装置不仅包括那些要素，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、物品或者装置所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括该要素的过程、方法、物品或者装置中还存在另外的相同要素。
本领域的技术人员能够理解，尽管在此所述的一些实施例包括其它实施例中所包括的某些特征而不是其它特征，但是不同实施例的特征的组合意味着处于本发明的范围之内并且形成不同的实施例。
本领域的技术人员能够理解，对各个实施例的描述都各有侧重，某个实施例中没有详述的部分，可以参见其他实施例的相关描述。
虽然结合附图描述了本发明的实施方式，但是本领域技术人员可以在不脱离本发明的精神和范围的情况下做出各种修改和变型，这样的修改和变型均落入由所附权利要求所限定的范围之内。
工业实用性
本公开实施例中，依据道路监测单元提供的监测信息，对需要避让车辆提前采取让行动作，通过车与路之间有效协同，提高通行效率，保证交通安全，具有工业实用性。

Claims (14)

  1. 一种智能驾驶车辆让行方法，其中，所述智能驾驶车辆行驶在特殊道路，所述特殊道路配置有多个道路监测单元，其特征在于，包括：
    基于所述智能驾驶车辆的状态信息确定相应道路监测单元；
    接收所述相应道路监测单元的监测信息；
    基于所述监测信息确定是否存在需要避让的通行车辆；
    若存在需要避让的通行车辆，控制智能驾驶车辆进行避让。
  2. 根据权利要求1所述的方法，其特征在于，所述特殊道路包括双向单车道。
  3. 根据权利要求1所述的方法，其特征在于，所述智能驾驶车辆的状态信息包括：位置信息及航向。
  4. 根据权利要求3所述的方法，其特征在于，所述基于所述智能驾驶车辆的状态信息确定相应道路监测单元包括：
    基于所述智能驾驶车辆的位置信息和高精度地图，获取相应道路监测单元信息；
    其中，所述高精度地图包括道路监测单元的位置信息。
  5. 根据权利要求1所述的方法，其特征在于，所述监测信息包括通行车辆信息、通行车辆优先级、通行车辆位置、通行车辆航向、通行车辆类型中的至少一种。
  6. 根据权利要求1所述的方法，其特征在于，所述接收所述相应道路监测单元的监测信息，包括：
    向服务器发送获取请求，所述获取请求至少包括所述相应道路监测单元信息；
    接收服务器的响应，所述响应至少包括所述相应道路监测单元中每个道路监测单元的监测信息。
  7. 根据权利要求1-6任一所述的方法，其特征在于，基于所述监测信息确定是否存在需要避让的通行车辆，包括：
    基于所述通行车辆优先级、通行车辆位置和通行车辆航向中的至少一种确定是否存在需要避让的通行车辆。
  8. 根据权利要求7所述的方法，其特征在于，进一步包括：
    所述通行车辆优先级高于所述智能驾驶车辆优先级，所述智能驾驶车辆需要进行避让。
  9. 根据权利要求1所述的方法，其特征在于，所述存在需要避让的通行车辆，控制智能驾驶车辆进行避让，包括：
    规划生成所述智能驾驶车辆的避让路径；
    控制智能驾驶车辆按照避让路径行驶。
  10. 根据权利要求1所述的方法，其特征在于，进一步包括：
    基于所述避让路径完成避让后，依据目的地和智能驾驶车辆当前位置，生成规划路径。
  11. 一种车载设备，其特征在于，包括：处理器和存储器；
    所述处理器通过调用所述存储器存储的程序或指令，用于执行如权利要求1至10中任一项所述方法的步骤。
  12. 一种非暂态计算机可读存储介质，其特征在于，所述非暂态计算机可读存储介质存储程序或指令，所述程序或指令使计算机执行如权利要求1至10任一项所述方法的步骤。
  13. 一种智能驾驶车辆让行装置，其特征在于，所述智能驾驶车辆让行装置所属的智能驾驶车辆行驶在特殊道路，所述特殊道路配置有多个道路监测单元，所述智能驾驶车辆让行装置包括：
    确定单元，用于基于所述智能驾驶车辆的状态信息确定相应道路监测单元；
    接收单元，用于接收所述相应道路监测单元的监测信息；
    避让单元，用于基于所述监测信息确定是否存在需要避让的通行车辆；在存在需要避让的通行车辆时，控制智能驾驶车辆进行避让。
  14. 一种车辆让行***，其特征在于，包括：
    服务器、配置在道路上的多个道路监测单元和权利要求13中的智能驾驶车辆让行装置；
    所述道路监测单元与所述服务器交互，所述服务器与智能驾驶车辆让行装置交互。
PCT/CN2019/093809 2019-06-28 2019-06-28 一种智能驾驶车辆让行方法、装置及车载设备 WO2020258277A1 (zh)

Priority Applications (2)

Application Number Priority Date Filing Date Title
CN201980001028.8A CN110603181B (zh) 2019-06-28 2019-06-28 一种智能驾驶车辆让行方法、装置及车载设备
PCT/CN2019/093809 WO2020258277A1 (zh) 2019-06-28 2019-06-28 一种智能驾驶车辆让行方法、装置及车载设备

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CN2019/093809 WO2020258277A1 (zh) 2019-06-28 2019-06-28 一种智能驾驶车辆让行方法、装置及车载设备

Publications (1)

Publication Number Publication Date
WO2020258277A1 true WO2020258277A1 (zh) 2020-12-30

Family

ID=68852439

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2019/093809 WO2020258277A1 (zh) 2019-06-28 2019-06-28 一种智能驾驶车辆让行方法、装置及车载设备

Country Status (2)

Country Link
CN (1) CN110603181B (zh)
WO (1) WO2020258277A1 (zh)

Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113799801A (zh) * 2021-10-19 2021-12-17 中国第一汽车股份有限公司 车辆避让控制方法、装置、电子设备及存储介质
CN113823092A (zh) * 2021-09-28 2021-12-21 深圳优地科技有限公司 机器人运行控制方法、设备及计算机可读存储介质
CN113819920A (zh) * 2021-10-10 2021-12-21 安徽江淮汽车集团股份有限公司 针对拥堵路段的自动驾驶非自主导航方法
CN114170803A (zh) * 2021-12-15 2022-03-11 阿波罗智联(北京)科技有限公司 路侧感知***和交通控制方法
CN114299717A (zh) * 2021-12-28 2022-04-08 大众问问(北京)信息科技有限公司 车辆紧急避让方法、装置、计算机设备和存储介质
CN114894209A (zh) * 2022-04-29 2022-08-12 武汉理工大学 一种基于车辆管理***的无人矿卡路径规划冲突避让方法
CN115359675A (zh) * 2022-07-15 2022-11-18 东风汽车集团股份有限公司 道路车辆行驶管理方法及相关设备
CN117275232A (zh) * 2023-09-28 2023-12-22 广东省电信规划设计院有限公司 基于车路协同的动态感知方法及装置

Families Citing this family (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111002981A (zh) * 2019-12-26 2020-04-14 苏州智加科技有限公司 提示方法、装置、自动驾驶车辆及存储介质
KR20210123904A (ko) * 2020-04-06 2021-10-14 현대자동차주식회사 차량 정보 제공 서버 및 차량 정보 제공 방법
CN111526483A (zh) * 2020-04-29 2020-08-11 江铃汽车股份有限公司 一种避让控制方法、装置、存储介质、车辆及***
CN111785039B (zh) * 2020-07-02 2022-12-06 北京易控智驾科技有限公司 双向单车道智能驾驶车辆的管制方法、装置、设备及介质
CN112258877A (zh) * 2020-10-21 2021-01-22 江苏徐工工程机械研究院有限公司 用于矿山运输车辆的交通控制方法、平台及其***
CN112238871A (zh) * 2020-11-06 2021-01-19 北京航迹科技有限公司 执行自动驾驶功能的车辆的控制方法、装置、设备、介质
CN112614366A (zh) * 2020-12-11 2021-04-06 国汽(北京)智能网联汽车研究院有限公司 汽车协同决策方法、装置、电子设备及计算机存储介质
CN112874534A (zh) * 2021-01-29 2021-06-01 朝阳聚声泰（信丰）科技有限公司 一种提高驾驶安全的方法
CN112863207B (zh) * 2021-04-06 2022-05-06 宁波均联智行科技股份有限公司 一种车辆行驶控制方法及装置
CN113487888A (zh) * 2021-07-16 2021-10-08 北京交通大学 一种城区内部道路机动车泊车与行车控制方法
CN113870556B (zh) * 2021-09-22 2023-01-20 驭势科技（北京）有限公司 协同避障方法、装置、***、设备、介质和产品
CN113734202B (zh) * 2021-09-22 2023-12-01 驭势科技（北京）有限公司 多车协同方法、装置、***、设备、介质和产品
DE102021127070A1 (de) 2021-10-19 2023-04-20 Cariad Se Verfahren zum Bestimmen einer Soll-Trajektorie eines zumindest teilweise automatisiert betriebenen Kraftfahrzeugs auf einer einspurigen Fahrbahn, Computerprogrammprodukt sowie Assistenzsystem
CN114194258B (zh) * 2021-11-08 2023-09-19 东风悦享科技有限公司 一种轨道车辆的避让调度方法、***及装置
CN114030468A (zh) * 2021-12-07 2022-02-11 武汉路特斯汽车有限公司 特殊路况的车辆自动避让控制方法、控制***及车辆
CN115601972B (zh) * 2022-11-28 2023-03-21 青岛慧拓智能机器有限公司 无人矿山行驶区域的障碍物处理***
CN116311890A (zh) * 2022-12-28 2023-06-23 广州汽车集团股份有限公司 一种基于v2x的紧急车辆避让方法、装置及***

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2000172988A (ja) * 1998-12-04 2000-06-23 Nec Corp 緊急車両走行通報システム
CN104882019A (zh) * 2015-06-12 2015-09-02 北汽福田汽车股份有限公司 车辆的避让方法和避让***
CN107134160A (zh) * 2016-02-29 2017-09-05 法拉第未来公司 紧急信号检测和响应
CN206772594U (zh) * 2016-12-14 2017-12-19 特路（北京）科技有限公司 自动驾驶车辆避让动态障碍物能力的测试场
CN109030021A (zh) * 2018-07-11 2018-12-18 长安大学 一种无人车紧急车辆避让能力测试方法及测试场
CN109552328A (zh) * 2018-12-26 2019-04-02 广州小鹏汽车科技有限公司 一种自动避让特种车辆的控制方法及车载***

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2000172988A (ja) * 1998-12-04 2000-06-23 Nec Corp 緊急車両走行通報システム
CN104882019A (zh) * 2015-06-12 2015-09-02 北汽福田汽车股份有限公司 车辆的避让方法和避让***
CN107134160A (zh) * 2016-02-29 2017-09-05 法拉第未来公司 紧急信号检测和响应
CN206772594U (zh) * 2016-12-14 2017-12-19 特路（北京）科技有限公司 自动驾驶车辆避让动态障碍物能力的测试场
CN109030021A (zh) * 2018-07-11 2018-12-18 长安大学 一种无人车紧急车辆避让能力测试方法及测试场
CN109552328A (zh) * 2018-12-26 2019-04-02 广州小鹏汽车科技有限公司 一种自动避让特种车辆的控制方法及车载***

Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113823092A (zh) * 2021-09-28 2021-12-21 深圳优地科技有限公司 机器人运行控制方法、设备及计算机可读存储介质
CN113823092B (zh) * 2021-09-28 2022-11-18 深圳优地科技有限公司 机器人运行控制方法、设备及计算机可读存储介质
CN113819920A (zh) * 2021-10-10 2021-12-21 安徽江淮汽车集团股份有限公司 针对拥堵路段的自动驾驶非自主导航方法
CN113819920B (zh) * 2021-10-10 2024-05-17 安徽江淮汽车集团股份有限公司 针对易于拥堵路段的自动驾驶非自主导航方法
CN113799801A (zh) * 2021-10-19 2021-12-17 中国第一汽车股份有限公司 车辆避让控制方法、装置、电子设备及存储介质
CN114170803A (zh) * 2021-12-15 2022-03-11 阿波罗智联(北京)科技有限公司 路侧感知***和交通控制方法
CN114299717A (zh) * 2021-12-28 2022-04-08 大众问问(北京)信息科技有限公司 车辆紧急避让方法、装置、计算机设备和存储介质
CN114894209A (zh) * 2022-04-29 2022-08-12 武汉理工大学 一种基于车辆管理***的无人矿卡路径规划冲突避让方法
CN114894209B (zh) * 2022-04-29 2024-05-03 武汉理工大学 一种基于车辆管理***的无人矿卡路径规划冲突避让方法
CN115359675A (zh) * 2022-07-15 2022-11-18 东风汽车集团股份有限公司 道路车辆行驶管理方法及相关设备
CN117275232A (zh) * 2023-09-28 2023-12-22 广东省电信规划设计院有限公司 基于车路协同的动态感知方法及装置
CN117275232B (zh) * 2023-09-28 2024-05-31 广东省电信规划设计院有限公司 基于车路协同的动态感知方法及装置

Also Published As

Publication number Publication date
CN110603181A (zh) 2019-12-20
CN110603181B (zh) 2022-04-29

Similar Documents

Publication Publication Date Title
WO2020258277A1 (zh) 一种智能驾驶车辆让行方法、装置及车载设备
JP7152949B2 (ja) ビークルプラトーニングのための責務および取り決めの同意
CN113299096B (zh) 合作式交叉路口通行控制方法、装置及设备
EP3690851B1 (en) Planning method for express lane and unit
US20230056023A1 (en) Vehicle-road driving intelligence allocation
WO2021102957A1 (zh) 一种车道保持方法、车载设备和存储介质
CN110861650B (zh) 车辆的路径规划方法、装置，车载设备和存储介质
WO2020258276A1 (zh) 一种智能驾驶车辆让行方法、装置及车载设备
EP3916696A1 (en) Method, apparatus and device for driving control, and medium and system
US20180056998A1 (en) System and Method for Multi-Vehicle Path Planning Technical Field
CN108352112A (zh) 用于确定车辆的驾驶意图的方法和车辆通信***
US10787172B2 (en) Driving assistance device and driving assistance method
CN110606070B (zh) 一种智能驾驶车辆及其制动方法、车载设备和存储介质
JP7271259B2 (ja) 車両管理システム、車両管理装置、および車両管理方法
CN110562222B (zh) 用于弯道场景的紧急制动控制方法、车载设备和存储介质
US11804136B1 (en) Managing and tracking scouting tasks using autonomous vehicles
CN110807412B (zh) 一种车辆激光定位的方法、车载设备和存储介质
JP2018160134A (ja) 移動体、移動体制御システム及び移動体制御方法
CN110568847A (zh) 一种车辆的智能控制***、方法，车载设备和存储介质
CN110562269A (zh) 一种智能驾驶车辆故障处理的方法、车载设备和存储介质
CN110599790B (zh) 一种智能驾驶车辆进站停靠的方法、车载设备和存储介质
CN114407915A (zh) 运行设计域odd的处理方法、装置及存储介质
WO2021097844A1 (zh) 基于多传感器数据融合的护栏估计方法和车载设备
WO2020248136A1 (zh) 用于驾驶控制的方法、装置、设备、介质和***
JP2024517167A (ja) 系統的最適化を伴う車両案内

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 19935715

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 19935715

Country of ref document: EP

Kind code of ref document: A1