WO2020258276A1 - 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

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Publication number
WO2020258276A1
WO2020258276A1 PCT/CN2019/093808 CN2019093808W WO2020258276A1 WO 2020258276 A1 WO2020258276 A1 WO 2020258276A1 CN 2019093808 W CN2019093808 W CN 2019093808W WO 2020258276 A1 WO2020258276 A1 WO 2020258276A1
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Prior art keywords
vehicle
yield
intelligent driving
driving vehicle
road
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PCT/CN2019/093808
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English (en)
French (fr)
Inventor
马万里
赵世杰
周小成
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驭势科技（北京）有限公司
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Priority to CN201980001033.9A priority Critical patent/CN110494341A/zh
Priority to PCT/CN2019/093808 priority patent/WO2020258276A1/zh
Publication of WO2020258276A1 publication Critical patent/WO2020258276A1/zh

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • 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
    • 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]
    • 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]
    • H04W4/46—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for vehicle-to-vehicle communication [V2V]
    • 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]
    • H04W4/48—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for in-vehicle communication
    • 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 two directions. If the planning is unreasonable, there will be congestion or congestion. Therefore, special planning is required for this special road condition.
  • the existing autonomous driving technology does not provide any solutions 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, including:
  • 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:
  • the yield determination unit is used to determine whether there is a passing vehicle that requires an intelligent driving vehicle to yield on the special road;
  • a yield path generation unit is used to determine a yieldable area based on the state information of the intelligent driving vehicle based on the existence of a passing vehicle that needs to yield; planning based on the state information of the smart driving vehicle and the yieldable area Generating a yield path of the intelligent driving vehicle;
  • the yield path driving unit is used to control the intelligent driving vehicle to follow the yield path.
  • 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 yield path is planned and generated, and then the intelligent driving vehicle is The yield path is driven to achieve yield, and then active yield actions can be taken in advance, which improves traffic efficiency and ensures traffic safety.
  • the decision is made based on the monitoring information of the road monitoring unit, that is, through 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 flowchart of yet another method for giving way for a smart driving vehicle provided by an embodiment of the present disclosure
  • FIG. 7 is a signaling diagram of a method for giving way for an intelligent driving vehicle provided by an embodiment of the present disclosure
  • FIG. 8A is a scene diagram of another intelligent driving vehicle driving provided by an embodiment of the present disclosure.
  • FIG. 8B to 8E are schematic diagrams of giving way according to the intelligent driving vehicle in the scene shown in FIG. 8A;
  • FIG. 9 is a schematic diagram of a yield point used in another method for yielding a smart driving vehicle provided by an embodiment of the present disclosure.
  • embodiments of the present disclosure provide a solution for intelligently driving vehicles to give way to driving on special roads.
  • the give way area on the special road is determined, and the give way path of the intelligent driving vehicle is generated according to the allowable area plan, and the intelligent driving vehicle is controlled to follow Driving on the yield path can then take active yield actions 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.
  • a smart driving vehicle and some emergency vehicles for example, an ambulance, a fire engine, etc.
  • the smart driving vehicle needs to give way to the emergency vehicle.
  • the intelligent driving vehicle when the intelligent driving vehicle is driving on a special road, it will also encounter a situation where it needs to give way.
  • the special road is a two-way single-lane road, it is necessary to give way to 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 give way to oncoming vehicles (such as rescue vehicles). ) make a concession.
  • 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 an independent background 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 sensor, such as a camera, a lidar, etc., or a road device, such as a V2X device, a roadside traffic light device, and the like.
  • 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 collecting the road monitoring information, the road monitoring unit 002 may send the road monitoring information to the cloud server, or may also send the road monitoring information 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 requested information includes, but is not limited to, the current vehicle identifier, the current vehicle pose, and corresponding road monitoring unit information corresponding to the vehicle.
  • 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, giving way to some vehicles on special roads, thereby improving the efficiency of vehicles on special roads while ensuring 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 also used to determine whether there is a passing vehicle that requires an intelligent driving vehicle to give way on the special road; based on the existence of a passing vehicle that needs to give way, determine the allowable area according to the state information of the intelligent driving vehicle; Based on the state information of the smart driving vehicle and the yieldable area, planning and generating a yield path of the smart driving vehicle; controlling the smart driving vehicle to drive along the yield path.
  • 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.
  • 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.
  • V2X Vehicle to X, vehicle wireless communication
  • 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 configured to perform path planning and decision-making based on the sensing positioning information generated by the sensing 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 yield 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 yield module 204 is used to determine whether there is a passing vehicle that requires a smart driving vehicle to yield on the special road; based on the existence of a passing vehicle that needs to yield, it is determined according to the state information of the smart driving vehicle. Giving way area; based on the state information of the smart driving vehicle and the giving way area, planning and generating the giving way path of the smart driving vehicle; controlling the smart driving vehicle to drive according to the giving way path.
  • the yield module 204 is also used to obtain monitoring information of the corresponding road monitoring unit, and determine whether there is a passing vehicle that needs to yield based on the acquired monitoring information.
  • 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 yield determining unit 301, a yield path generating unit 302, a yield path driving unit 303, and other units that can be used to perform yield operations.
  • the yield determination unit 301 is used to determine whether there is a passing vehicle that needs to yield on the road on which the intelligent driving vehicle is currently traveling. In some embodiments, the intelligent driving vehicle determines whether there is a passing vehicle that needs to yield based on the monitoring information of the road monitoring unit. The intelligent driving vehicle determines the corresponding road monitoring unit based on the state information of the intelligent driving vehicle, and sends the corresponding road monitoring unit information to the cloud server, thereby receiving the monitoring information of the corresponding road monitoring unit. Among them, the state information of the intelligent driving vehicle includes, but is not limited to: position information, speed, and heading. In some embodiments, the yield determination unit 301 determines the corresponding road monitoring unit based on one or more of the position information, speed, and heading of the intelligent driving vehicle.
  • the yield determination unit 301 obtains corresponding road monitoring unit information based on one or more of the position information, heading, and high-precision map of the intelligent driving vehicle.
  • the high-precision map includes one or more of the location and the monitoring range of the road monitoring unit in the road network.
  • the yield determination unit 301 may send an acquisition request to the server to acquire the monitoring information of the corresponding road monitoring unit, wherein the acquisition request includes at least the corresponding road monitoring unit information.
  • the smart driving vehicle yielding device can interact with the cloud server to obtain the monitoring information of the road monitoring unit.
  • the smart driving vehicle yielding device of the embodiment of the present disclosure can directly interact with the road monitoring unit to obtain monitoring information of the road monitoring unit.
  • the monitoring information of the road monitoring unit includes but is not limited to at least one of passing vehicle information, passing vehicle priority, passing vehicle location, passing vehicle heading, passing vehicle type, and passing vehicle speed.
  • the yield determination unit 301 may determine whether there is a passing vehicle that needs to yield based on at least one of the passing vehicle priority, the passing vehicle position, and the passing vehicle heading.
  • the priority of the passing vehicle is higher than the priority of the intelligent driving vehicle, and the intelligent driving vehicle needs to yield.
  • the intelligent driving vehicle needs to give way.
  • the speed of the passing vehicle is greater than the speed of the intelligent driving vehicle, and the priority of the passing vehicle is higher than the priority of the intelligent driving vehicle, The intelligent driving vehicle needs to give way.
  • the yield path generating unit 302 is configured to plan and generate a yield path when there are vehicles that need to yield.
  • the yielding path generation unit 302 obtains the yieldable area of the special road based on the state information of the intelligent driving vehicle.
  • the state information of the intelligent driving vehicle includes, but is not limited to: position information, speed, and heading.
  • the yield path generating unit 301 determines a yield area based on the position, heading, and/or high-precision map of the vehicle, and determines a yield point based on the yield area.
  • the giving way area and the giving way point can be marked on the high-precision map, that is, whether there are any give-away areas on the road recorded in the high-precision map used by the intelligent driving system, and any The yield point information in the row area.
  • the yield point in the yield area is determined according to the yield area and the road network topology.
  • the road network topology may include road network information corresponding to the yieldable area, where the road network topology and road network information are included in the high-precision map.
  • the yield path generation unit 302 determines a yield point based on the yield area, and uses the yield point as a yield destination during path planning in the intelligent driving vehicle, thereby planning and generating Yield path. In some embodiments, if there are multiple yield points, the yield path generating unit 302 can select one of the yield points as the target yield point to reach the yield point and plan the yield path, for example, the target yield point/ The reachable yield point may be the yield point closest to the intelligent driving vehicle as the target yield point, or the target yield point may be any yield point in the yield area.
  • the yield path generating unit 302 is also used to adjust the priority of the smart driving vehicle when there is no yield point, so that the smart driving vehicle becomes another passing vehicle on a special road and needs to yield.
  • Vehicles The adjustment of the priority of the intelligent driving vehicle may adjust the priority of the intelligent driving vehicle to a higher priority on the current traffic road to ensure passage, or it may be adjusted to the highest priority.
  • the intelligent driving system may send the adjusted priority to the cloud server or road monitoring unit, and the cloud server or road monitoring unit may notify other vehicles, or it may use vehicle-to-vehicle communication to notify other vehicles on the current road. Passing vehicles, so that other passing vehicles give way to the current intelligent driving vehicle.
  • the yield path driving unit 303 is used to generate a control instruction to control the intelligent driving vehicle to follow the yield path.
  • the yield path driving unit 303 generates a control instruction based on the yield path, converts the control instruction into a control execution instruction, and issues it to the vehicle bottom-level execution system.
  • the vehicle bottom-level execution system controls the intelligent driving vehicle to follow the yield path based on the control execution instruction.
  • the smart-driving vehicle yielding device 300 further includes a path planning unit not shown in the figure, which is used to generate the original destination and the current position of the smart-driving vehicle after the yield is completed based on the yielding path. Plan the path.
  • the original destination may be understood as the destination where the intelligent driving vehicle travels before giving way.
  • the current position of the intelligent driving vehicle may be the reachable yield point in the foregoing yieldable area. The reachable yield point is used as location information when planning a route.
  • the sensor data of the intelligent driving vehicle is not only relied on, thereby improving the traffic efficiency and ensuring traffic safety.
  • each unit in the way of intelligent driving vehicle yielding device 300 is only a logical function division.
  • there may be other ways of dividing for example, yield determining unit 301, yield path generating unit 302, and
  • the yield path driving unit 303 may be implemented as a unit; the yield determination unit 301, the yield path generation unit 302, and the yield path driving unit 303 may also be divided into multiple sub-units.
  • 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 determines the intelligence based on the monitoring information of the road monitoring unit. Whether there is a passing vehicle that needs to yield on the road on which the vehicle is driving.
  • the road monitoring unit is determined based on the state information of the intelligent driving vehicle.
  • the state information of the intelligent driving vehicle includes, but is not limited to: position, speed, and heading.
  • the intelligent driving system determines the corresponding road monitoring unit in real time or periodically based on the status information of the intelligent driving vehicle, and then determines whether there is a passing vehicle that needs to yield on the current road based on the monitoring information of the corresponding road monitoring unit.
  • the monitoring information of each road monitoring unit 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. Furthermore, in a specific implementation process, it is determined whether there is a passing vehicle that needs to yield according to at least one of the priority of the passing vehicle, the location information of the passing vehicle, and the heading of the passing vehicle.
  • the monitoring information of the corresponding road monitoring unit there is at least one passing vehicle in the monitoring information of the corresponding road monitoring unit that has a priority higher than that of a smart driving vehicle, it is determined that there is a passing vehicle that needs to yield on the current road.
  • the intelligent driving vehicle in the monitoring information of the corresponding road monitoring unit, there is at least one passing vehicle whose priority is higher than the priority of the intelligent driving vehicle, and the heading is the same, and there is a high priority passing vehicle in the driving direction of the intelligent driving vehicle Behind the road, it is determined that there are passing vehicles that need to give way on the current road, that is, the intelligent driving vehicle needs to give way to the high-priority passing vehicles.
  • step 520 when the intelligent driving system determines that it is necessary to give way, it obtains the yieldable area on the current driving road.
  • the row area may be marked on the high-precision map.
  • the allowable area is understood to be an area on the road where temporary parking is possible.
  • the smart driving system determines the yieldable area according to the state information of the smart driving vehicle.
  • the state information of the intelligent driving vehicle includes, but is not limited to: position, heading, and speed.
  • the yieldable area on the driving road is determined.
  • the allowable area on the driving road is determined according to the position, heading and high-precision map of the intelligent driving vehicle.
  • the smart driving system plans a yield path based on the yieldable area and the state information of the smart driving vehicle.
  • the intelligent driving system determines whether there is a yield point in the yield area, and if there is a yield point, the yield point is used as the destination of the planned yield path, and the path planning is performed to obtain the yield path .
  • the intelligent driving system determines that there is a yield point in the yieldable area, and there are multiple yield points, the smart driving system may select a yield point as the reachable yield point according to the yield point screening rule At this time, the selected reachable yield point is the destination used for planning the yield path.
  • the reachable yield point may be the yield point closest to the position information of the intelligent driving vehicle.
  • a yield point is selected as the reachable yield point according to the yield point screening rule.
  • the intelligent driving system plans the yield path based on the status information of the intelligent driving vehicle and the reachable yield point.
  • whether there is a yield point in the yield area is determined by the yield area and the road network topology in the high-resolution map.
  • the topological structure of the road network includes road network information of the concession area.
  • the smart driving system adjusts the priority of the smart driving vehicle by adjusting the priority of the vehicle, so that the smart driving vehicle after the adjusted priority becomes the current road Vehicles that board other passing vehicles to give way.
  • the intelligent driving system controls the intelligent driving vehicle to drive along the yield path.
  • the intelligent driving system can generate and issue control instructions for the underlying vehicle execution system based on the yield path, and the vehicle underlying execution system controls the intelligent driving vehicle according to the control execution follow the yield path.
  • the intelligent driving system controls the intelligent driving vehicle to drive according to the yielding path based on the perception information of the sensor group and the underlying execution system of the vehicle.
  • the give way area of the intelligent driving vehicle is obtained, and then the give way path is generated based on the give way area planning, thereby controlling the intelligent driving Vehicles follow the concession path to achieve concession, thereby improving the efficiency of traffic and ensuring traffic safety.
  • step 510 may include sub-step 5101 to sub-step 5103; specifically, in step 5101, the corresponding road monitoring unit is determined based on the state information of the intelligent driving vehicle.
  • the state information of the intelligent driving vehicle includes, but is not limited to, position information, speed, driving state, and heading of the intelligent driving vehicle.
  • the location information of the intelligent driving vehicle may 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 and high-precision map of the intelligent driving vehicle.
  • 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.
  • 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.
  • the high-precision map of this embodiment may be the high-precision map used in the intelligent driving field described in the foregoing content.
  • 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 the monitoring information of the corresponding road monitoring unit.
  • the intelligent driving system may send an acquisition request to the server, where 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 priority judgment rule in advance, so that the monitoring information sent to the intelligent driving system carries the monitoring range of the road monitoring unit Vehicle priority of vehicles passing inside.
  • the intelligent driving system determines whether there is a passing vehicle that needs to yield on the current road based on the monitoring information. In some embodiments, it is determined whether there is a passing vehicle that needs to yield 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 yield 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 yield based on the priority of the passing vehicle and the heading of the passing vehicle in the monitoring information.
  • the intelligent driving vehicle when the priority of the passing vehicle is higher than the priority of the intelligent driving vehicle, the intelligent driving vehicle needs to yield.
  • 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 give way.
  • 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 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 according to the monitoring information of the road monitoring unit, Determine whether there is a passing vehicle that needs to give way. When it exists, determine the yield area, and plan the yield path according to the yield area to control the intelligent driving vehicle to yield according to the yield path, and then take the initiative to yield in advance Action to improve traffic efficiency and ensure traffic safety.
  • Fig. 7 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. 7 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 corresponding road monitoring unit information to the cloud server, and receives a 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 yield on the current road 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, a yield needs to be made, and step 705 is executed.
  • the intelligent driving system obtains the yieldable area of the current road based on the state information of the intelligent driving vehicle.
  • the intelligent driving system obtains the concessionable area of the special road based on the state information and the high-precision map sensed by the sensing module in the intelligent driving vehicle.
  • the yieldable area is an area displayed on a high-precision map that allows temporary parking.
  • the concession area as described in Fig. 9 shows a polygonal concession area. On an actual road, the shape of the concession area may not be limited to a polygonal shape, but any other shape is acceptable.
  • the yieldable area acquired by the smart driving system is usually the area closest to the smart driving vehicle of the current smart driving system, and the yieldable area is located in front of the driving direction of the smart driving vehicle.
  • step 706 the intelligent driving system determines whether there is a yield point based on the yield area. In some embodiments, it is determined whether there is a yield point based on the yield area and the road network topology. In some embodiments, both the yieldable area and the road network topology are information included in the high-precision map.
  • the road network topology includes road network information corresponding to the yieldable area. Based on the description of the aforementioned high-precision map, the road network information belongs to the information included in the high-precision map.
  • step 710 is executed to adjust the priority of the intelligent driving vehicle. If there is a yield point, step 707 is executed.
  • step 707 if there is a yield point, the intelligent driving system plans to generate the yield path based on the state information and the yield point. In some embodiments, if there is a yield point, and there is one yield point (as shown in FIG. 9), the yield point is regarded as the reachable yield point. If there are yield points and there are multiple yield points, the yield point closest to the intelligent driving vehicle is selected as the reachable yield point; or if there are multiple yield points, it is based on the passing vehicle and For the position, speed and heading of the intelligent driving vehicle, select a yield point as the reachable yield point. In some embodiments, the intelligent driving system plans to generate the yield path based on the state information and the reachable yield point.
  • the reachable yield point is used as the destination when planning the yield path.
  • the reachable yield point is terminal point information used to locate when generating the yield path in the yield area.
  • the end point in the yield path is the location information of the reachable yield point.
  • the yield point may be a preset position in the yield area, which belongs to information in a high-precision map or is manually set in advance.
  • the intelligent driving system controls the intelligent driving vehicle to drive along the yield 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 yield path planned by the intelligent driving system is the path with the shortest distance from the current position. In some embodiments, if the smart driving vehicle is driving on a two-way single-lane lane, the yield path is the path that is consistent with the current driving direction of the smart driving vehicle and has the shortest distance from the current position of the smart driving vehicle.
  • the intelligent driving system In step 709, the intelligent driving system generates a planned route based on the destination and the current position of the intelligent driving vehicle after completing the yield based on the yield path.
  • the yield path is the previously planned yield path, and the destination can be understood as the destination before the planned yield path.
  • the smart driving system adjusts the priority of the smart driving vehicle so that the smart driving vehicle becomes a vehicle that other passing vehicles on the current road give way.
  • the smart driving system informs other vehicles on the current road of the current priority of the smart driving vehicle through a vehicle-to-vehicle communication method.
  • the vehicle-server-vehicle communication method may be used to notify other vehicles on the current special road, such as the reverse vehicle, to give way.
  • the smart driving system adjusts the priority of the smart driving vehicle, it sends the adjusted priority of the current smart driving vehicle to the server, so that the server will send the adjusted priority to other vehicles on the current road so that other vehicles can pass.
  • the vehicle gives way.
  • the intelligent driving system sends the adjusted priority of the current intelligent driving vehicle to the corresponding road monitoring unit, so that the corresponding road monitoring unit transfers the adjusted priority to the current It is sent by other vehicles on the road, or the corresponding road monitoring unit reports to the server, so that the server sends the adjusted priority to other vehicles on the current road, so that other vehicles give way.
  • the yield point is determined through the yield area in advance, and then the yield path is planned and generated according to the state information of the smart car and the yield point, so as to better realize the yield path to the smart car Plan to ensure traffic safety and improve traffic efficiency.
  • the two sides in FIGS. 8A to 8E are two-way two-lane lanes, and the middle section is a two-way single-lane lane. Different yield points are set on the two-way two-lane and the two-way single-lane.
  • the passing vehicle is a fire truck with a higher priority than a smart driving vehicle.
  • the smart driving vehicle is in A and B Runs back and forth between points, 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. 8A, 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. 8A to Fig. 8E are temporary stop points respectively.
  • Section II is a two-way single-lane, when RSU detects that a high-priority vehicle (take a fire truck as an example) is in this area, the intelligent driving vehicle needs to give way.
  • the specific concession scenarios can be divided into the following four:
  • the yield requirement for 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 yield path to realize the yield operation to the 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 yield decision and generate the corresponding yield 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 internal sensors of the vehicle, with the help of the monitoring information provided by the road monitoring unit, a yield action is taken in advance for the high-priority vehicles. Traffic efficiency ensures 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.
  • the yield path is planned in advance to realize the yield to the passing vehicle, and the effective coordination between the vehicle and the road improves the passing efficiency and ensures Traffic safety and industrial applicability.

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Abstract

一种智能驾驶车辆让行方法、装置及车载设备，其中，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：基于道路监测单元（002）的监测信息判断智能驾驶车辆（003）是否存在需要让行的通行车辆（004）；若存在需要让行的通行车辆（004），根据智能驾驶车辆（003）的状态信息确定可让行区域；基于智能驾驶车辆（003）的状态信息和可让行区域，规划生成智能驾驶车辆（003）的让行路径；控制智能驾驶车辆（003）按照让行路径行驶。实现了对通行车辆的让行，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，现有技术的自动驾驶技术中并没有针对这种特殊路况提供任何方案。
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，包括：
判断所述特殊道路上是否存在需要智能驾驶车辆让行的通行车辆；
基于存在需要让行的通行车辆，根据所述智能驾驶车辆的状态信息确定可让行区域；
基于所述智能驾驶车辆的状态信息和所述可让行区域，规划生成所述智能驾 驶车辆的让行路径；
控制智能驾驶车辆按照让行路径行驶。
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：
让行确定单元，用于判断所述特殊道路上是否存在需要智能驾驶车辆让行的通行车辆；
让行路径生成单元，用于基于存在需要让行的通行车辆，根据所述智能驾驶车辆的状态信息确定可让行区域；基于所述智能驾驶车辆的状态信息和所述可让行区域，规划生成所述智能驾驶车辆的让行路径；
让行路径行驶单元，用于控制智能驾驶车辆按照让行路径行驶。
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，判断特殊道路上是否存在需要让行的通行车辆，在确定存在需要让行的通行车辆时，规划生成让行路径，进而控制智能驾驶车辆按照让行路径行驶以实现让行，进而能够提前采取主动让行动作，提高了通行效率，保证了交通安全。
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又一种智能驾驶车辆让行方法的流程图；
图7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8A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8B至图8E分别是根据图8A所示的场景中的智能驾驶车辆让行的示意图；
图9是本公开实施例提供的又一种智能驾驶车辆让行方法中使用的让行点的示意图。
具体实施方式
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，基于道路监测单元的监测信息确定需要让行通行车辆时，确定特殊道路上的可让行区域，并依据可让行区域规划生成所述智能驾驶车辆的让行路径，控制智能驾驶车辆按照让行路径行驶，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让行。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)进行让行，或者需要对迎面行驶的通行车辆(如救援车)进行让行。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可以是一个独立的后台服务器，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标识、当前车辆位姿、与车辆相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让行，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，还用于判断所述特殊道路上是否存在需要智能驾驶车辆让行的通行车辆；基于存在需要让行的通行车辆，根据所述智能驾驶车辆的状态信息确定可让行区域；基于所述智能驾驶车辆的状态信息和所述可让行区域，规划生成所述智能驾驶车辆的让行路径；控制智能驾驶车辆按照让行路径行驶。
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、轨迹跟踪或路径让行。
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，让行模块204用于判断所述特殊道路上是否存在需要智能驾驶车辆让行的通行车辆；基于存在需要让行的通行车辆，根据所述智能驾驶车辆的状态信息确定可让行区域；基于所述智能驾驶车辆的状态信息和所述可让行区域，规划生成所述智能驾驶车辆的让行路径；控制智能驾驶车辆按照让行路径行驶。在一些实施例中，让行模块204还用于获取相应道路监测单元的监测信息，并依据获取的监测信息确定是否存在需要让行的通行车辆。
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包括让行确定单元301，让行路径生成单元302和让行路径行驶单元303以及其他一些可用于执行让行操作的单元。
让行确定单元301用于确定所述智能驾驶车辆当前行驶的道路上是否存在需要让行的通行车辆。在一些实施例中，所述智能驾驶车辆基于道路监测单元的监测信息确定是否存在需要让行的通行车辆。所述智能驾驶车辆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，并将所述相应道路监测单元信息发送给云端服务器，从而接收所述相应道路监测单元的监测信息。其中，智能驾驶车辆的状态信息包括但不限于：位置信息、速度及航向。在一些实施例中，所述让行确定单元301基于所述智能驾驶车辆的位置信息、速度和航向等一种或者多种确定所述相应道路监测单元。
让行确定单元301基于所述智能驾驶车辆的位置信息、航向和高精度地图中的一种或多种，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在路网中的位置、监测范围等一种或多种。在一些实施例中，让行确定单元301可以向服务器发送获取请求从而获取相应道路监测单元的监测信息，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，智能驾驶车辆让行装置可与云端服务器交互，获取道路监测单元的监测信息。当然，在一些实施例中，本公开实施例的智能驾驶车辆让行装置可与道路监测单元直接交互，获取道路监测单元的监测信息。在一些实施例中，道路监测单元的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、通行车辆速度中的至少一种。
让行确定单元301可以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让行的通行车辆。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让行。在一些实施例中，当所述通行车辆与所述智能驾驶车辆航向相反，且通行车辆优先级高于所述智能驾驶车辆优先级时，所述智能驾驶车辆需要进行让行。在一些实施例中，当所述通行车辆与所述智能驾驶车辆航向相同，所述通行车辆车速大于所述智能驾驶车辆车速时，且通行车辆优先级高于所述智能驾驶车辆优先级时， 所述智能驾驶车辆需要进行让行。
让行路径生成单元302用于在存在需要让行通行车辆的情况下规划生成让行路径。在一些实施例中，让行路径生成单元302基于所述智能驾驶车辆的状态信息，获取所述特殊道路的可让行区域。其中，所述智能驾驶车辆的状态信息包括但不限于：位置信息、速度及航向。在一些实施例中，让行路径生成单元301基于所述车辆的位置、航向和/或高精度地图确定可让行区域，并基于可让行区域确定让行点。其中，所述可让行区域和所述让行点可标记于所述高精度地图中，即智能驾驶***使用的高精度地图中记录有没有道路上的可让行区域，以及任一可让行区域内的让行点信息。在一些实施例中，依据可让行区域和路网拓扑结构，确定可让行区域中的让行点。此时，所述路网拓扑结构可包括所述可让行区域对应的路网信息，其中，路网拓扑结构和路网信息等包含于所述高精度地图中。
在一些实施例中，让行路径生成单元302基于所述可让行区域确定让行点，并以所述让行点作为所述智能驾驶车辆中路径规划时的让行目的地，从而规划生成让行路径。在一些实施例中，如果存在多个让行点，让行路径生成单元302可以选择其中一个让行点作为目的让行点即可达让行点规划让行路径，例如，目的让行点/可达让行点可以是与所述智能驾驶车辆距离最近的让行点作为目的让行点，或者，目的让行点可以是可让行区域内任意一个让行点。
在一些实施例中，让行路径生成单元302，还用于在不存在让行点时，可以调整智能驾驶车辆的优先级，使所述智能驾驶车辆成为特殊道路中其他通行车辆需要进行让行的车辆。所述调整智能驾驶车辆的优先级可以将智能驾驶车辆的优先级调整至当前通行道路上较高的优先级以保证通过，也可以调整至最高优先级。在一些实施例中，智能驾驶***可以将调整后的优先级发送给云端服务器或道路监测单元，并由云端服务器或道路监测单元通知其他车辆，也可以采用车-车通信方式通知当前道路上其他通行车辆，以使其他通行车辆对当前的智能驾驶车辆进行让行。
让行路径行驶单元303用于生成控制指令以控制智能驾驶车辆按照让行路径行驶。在一些实施例中，让行路径行驶单元303基于所述让行路径生成控制指令，并将控制指令转化成控制执行指令，下发给车辆底层执行***。车辆底层执行系 统基于所述控制执行指令控制智能驾驶车辆按照让行路径行驶。
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，用于基于所述让行路径完成让行后，依据原始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，原始目的地可理解为在让行路径之前，智能驾驶车辆行驶的目的地。在一些实施例中，智能驾驶车辆当前位置可为前述的可让行区域中的可达让行点。可达让行点作为规划路径时使用的位置信息。
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，例如让行确定单元301、让行路径生成单元302和让行路径行驶单元303可以实现为一个单元；让行确定单元301、让行路径生成单元302和让行路径行驶单元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510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***)基于道路监测单元的监测信息确定智能驾驶车辆行驶的道路上是否存在需要让行的通行车辆。在一些实施例中，所述道路监测单元为基于所述智能驾驶车辆的状态信息确定的。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、速度及航向。在一些实施例中，智能驾驶***基于智能驾驶车辆的状态信息实时或周期性的确定相应道路监测单元，进而依据相应道路监测单元的监测信息，确定当前道路上是否存在需要让行的通行车辆。在一些实施例中，每一个道路监测单元的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。进而，在具体实现过程中，依据通行车辆优先级、通行车辆位置信息和通行车辆航向中的至少一种确定是否存在需要让行的通行车辆。例如，相应道路监测单元的监测信息中存在至少一辆通行车辆的优先级高于智能驾驶车辆的优先级，则确定当前道路上存在需要让行的通行车辆。在一些实施例中，相应道路监测单元的监测信息中存在至少一辆通行车辆的优先级高于智能驾驶车辆的优先级，且航向相同，所有通行车辆的位置信息位于智能驾驶车辆行驶方向的前方，则确定当前道路上不存在需要让行的通行车辆，智能驾驶车辆无需让行高优先级的通行车辆。在一些实施例中，相应道路监测单元的监测信息中存在至少一辆通行车辆的优先级高于智能驾驶车辆的优先级，且航向相同，且存在高优先级的通行车辆位于智能驾驶车辆行驶方向的后方，则确定当前道路上存在需要让行的通行车辆，即智能驾驶车辆需要对高优先级的通行车辆进行让行操作。
在步骤520中，智能驾驶***确定需要让行时，获取当前行驶道路上的可让行区域。在一些实施例中，可让行区域标记于高精度地图中。在一些实施例中，可让行区域理解为行驶道路上可以临时停车的区域。在一些实施例中，智能驾驶***依据智能驾驶车辆的状态信息确定可让行区域。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、航向及速度等。在一些实施例中，基于智 能驾驶车辆的位置、速度和高精度地图，确定行驶道路上的可让行区域。在一些实施例中，依据智能驾驶车辆的位置、航向和高精度地图，确定行驶道路上的可让行区域。
在步骤530中，智能驾驶***依据可让行区域和智能驾驶车辆的状态信息，规划让行路径。在一些实施例中，智能驾驶***判断可让行区域内是否存在让行点，若存在让行点，则将让行点作为规划的让行路径的目的地，进行路径规划，得到让行路径。在一些实施例中，智能驾驶***判断确定可让行区域内存在让行点，且让行点为多个，则智能驾驶***可按照让行点筛选规则选择一个让行点作为可达让行点，此时选择的可达让行点即为用于规划让行路径使用的目的地。在一些实施例中，可达让行点可以是与智能驾驶车辆的位置信息距离最近的让行点。在一些实施例中，基于通行车辆的位置、智能驾驶车辆的位置、速度及航向，依据让行点筛选规则选择一个让行点作为可达让行点。此时，智能驾驶***基于智能驾驶车辆的状态信息和可达让行点，规划让行路径。在一些实施例中，可让行区域内是否存在让行点，是通过可让行区域和高精度地图中的路网拓扑结构确定的。其中路网拓扑结构包括可让行区域的路网信息。在一些实施例中，若可让行区域内不存在让行点，则智能驾驶***采用调整车辆优先级的方式调整智能驾驶车辆的优先级，以使调整优先级后的智能驾驶车辆成为当前道路上其他通行车辆进行让行的车辆。
在步骤540中，智能驾驶***控制智能驾驶车辆按照让行路径行驶。在一些实施例中，智能驾驶***的智能驾驶车辆为无人车时，智能驾驶***基于让行路径可生成下发车辆底层执行***的控制指令，进而车辆底层执行***依据控制执行控制智能驾驶车辆按照让行路径行驶。在一些实施例中，智能驾驶***基于传感器组的感知信息和车辆底层执行***控制智能驾驶车辆按照让行路径行驶。
本公开实施例中，通过道路监测单元的监测信息，确定存在需要让行的通行车辆时，获取智能驾驶车辆的可让行区域，进而基于可让行区域规划生成让行路径，进而控制智能驾驶车辆按照让行路径行驶以实现让行，进而提高了通行效率，保证了交通安全。
在一些实施例中，如图6所示，步骤510可包括子步骤5101至子步骤5103； 具体地，在步骤5101中，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、速度、行驶状态和航向。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5102中，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，使得向智能驾驶***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5103中，智能驾驶***基于所述监测信息确定当前道路上是否存在需要让行的通行车辆。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让行的通行车辆。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，确定是否存在需要让行的通行车辆。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，确定是否存在需要让行的通行车辆。
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时，所述智能驾驶车辆需要进行让行。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让行。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让行。
在本公开的实施例中，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，确定是否存在需要让行的通行车辆，在存在时，确定可让行区域，并依据可让行区域规划让行路径，以控制智能驾驶车辆按照让行 路径进行让行，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7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7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701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702和步骤703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704中，智能驾驶***基于监测信息，确定当前道路上是否存在需要让行的通行车辆。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，则需要进行让行，执行步骤705。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，则无需进行让行，重复上述步骤701中的获取相应道路监测单元的过程。
在步骤705中，智能驾驶***基于智能驾驶车辆的状态信息，获取当前道路的可让行区域。在一些实施例中，智能驾驶***基于智能驾驶车辆中感知模块感知的状态信息和高精度地图，获取特殊道路的可让行区域。在一些实施例中，可让行区域为高精度地图中显示的能够临时停车的区域。如图9所述的可让行区域，图9释出了一种多边形形状的可让行区域，在实际道路上，可让行区域的形状可不限定为多边形形状，任何其他的形状均可。在一些实施例中，智能驾驶***获取的可让行区域通常是距离当前智能驾驶***的智能驾驶车辆最近的区域，且可让行区域位于智能驾驶车辆行驶方向的前方。
在步骤706中，智能驾驶***基于可让行区域确定是否存在让行点。在一些实施例中，基于所述可让行区域和路网拓扑结构，确定是否存在让行点。在一些实施例中，可让行区域和路网拓扑结构均是高精度地图中包括的信息。所述路网拓扑结构包括所述可让行区域对应的路网信息，基于前述高精度地图的描述，路网信息属于高精度地图中包括的信息。在一些实施例中，若可让行区域不存在让行点，则执行步骤710，调整智能驾驶车辆的优先级。若存在让行点，执行步骤707。
在步骤707中，若存在让行点，则智能驾驶***基于所述状态信息和让行点，规划生成所述让行路径。在一些实施例中，若存在让行点，且让行点是一个(如图9所示)，则将该让行点作为可达让行点。如果存在让行点，且让行点是多个，则选择与所述智能驾驶车辆距离最近的让行点作为可达让行点；或者，若让行点为多个，则基于通行车辆和智能驾驶车辆的位置、速度及航向，选择一个让行点作为可达让行点。在一些实施例中，智能驾驶***基于所述状态信息和所述可达让行点，规划生成所述让行路径。此时，可达让行点作为规划让行路径时的目的地。在一些实施例中，可达让行点是可让行区域中用于生成让行路径时定位的终 点信息。在一些实施例中，让行路径中的终点即为可达让行点的位置信息。在一些实施例中，让行点可以是可让行区域内的预先设置的位置，属于高精度地图中的信息或者是预先人工设置的。
在步骤708中，智能驾驶***控制智能驾驶车辆按照让行路径行驶。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***规划的让行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，则让行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709中，智能驾驶***基于所述让行路径完成让行后，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，让行路径为前述规划的让行路径，目的地可理解为规划让行路径之前的目的地。
在步骤710中，若可让行区域不存在让行点，则智能驾驶***调整智能驾驶车辆的优先级，使所述智能驾驶车辆成为当前道路上其他通行车辆进行让行的车辆。在一些实施例中，智能驾驶***在调整智能驾驶车辆的优先级之后，通过车-车通信方式将当前智能驾驶车辆的优先级告知当前道路上的其他车辆。在一些实施例中，智能驾驶车辆的优先级调整后，可通过车-服务器-车通信方式通知当前特殊道路上的其他通行车辆如反向车辆进行让行。例如，智能驾驶***在调整智能驾驶车辆的优先级之后，向服务器发送当前智能驾驶车辆的调整后的优先级，以使服务器将调整后的优先级向当前道路上其他通行车辆发送，使得其他通行车辆进行让行。在一些实施例中，智能驾驶***在调整智能驾驶车辆的优先级之后，向相应道路监测单元发送当前智能驾驶车辆的调整后的优先级，以使相应道路监测单元将调整后的优先级向当前道路上其他通行车辆发送，或者相应道路监测单元上报服务器，以使服务器将调整后的优先级向当前道路上其他通行车辆发送，使得其他通行车辆进行让行。
本公开的实施例中，通过预先可让行区域，再确定让行点，进而根据智能车的状态信息和让行点，规划生成让行路径，以较好的实现对智能车的让行路径规划，保证交通安全，提高通行效率。
进一步地，为较好的理解，以下结合图8A至图8E的例子进行说明。
本公开实施例中，图8A至图8E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让行点。
下面以在特殊道路上需要提前让行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8A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8A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8A至图8E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车辆(以消防车为例)在该区域时，智能驾驶车辆需要主动让行，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让行，如图8B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让行，如图8C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进行让行，若已驶离了最近让行点(即马上离开Section II区域)，则不必再让行，如图8D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进行让行，若已驶离了最近让行点(即马上离开Section II区域)，则不必再让行，如图8E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，并规划让行路径，实现对高优先级车辆的让行操作，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，从而做出对应的让行决策，并生成相应的让行路径。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，由于车辆内部传感器感知距离有限，借助于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，依据道路监测单元的监测信息，确定需要让行通行车辆时，提前规划让行路径，实现对通行车辆的让行，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 (17)

  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：
    判断所述特殊道路上是否存在需要智能驾驶车辆让行的通行车辆；
    基于存在需要让行的通行车辆，根据所述智能驾驶车辆的状态信息确定可让行区域；
    基于所述智能驾驶车辆的状态信息和所述可让行区域，规划生成所述智能驾驶车辆的让行路径；
    控制智能驾驶车辆按照让行路径行驶。
  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1所述的方法，其特征在于，所述判断所述特殊道路上是否存在需要智能驾驶车辆让行的通行车辆，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，确定所述特殊道路上是否存在需要智能驾驶车辆让行的通行车辆。
  5. 根据权利要求4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5所述的方法，其特征在于，基于所述监测信息，判断所述特殊道路上是否存在需要智能驾驶车辆让行的通行车辆，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智能驾驶车辆让行的通行车辆。
  7. 根据权利要求6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时，所述智能驾驶车辆需 要让行。
  8. 根据权利要求3所述的方法，其特征在于，所述根据所述智能驾驶车辆的状态信息确定可让行区域，包括：
    基于所述智能驾驶车辆的位置信息和高精度地图确定所述可让行区域；
    其中，所述高精度地图包括可让行区域的信息。
  9. 根据权利要求1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和所述可让行区域，规划生成所述智能驾驶车辆的让行路径，包括：
    基于所述可让行区域确定是否存在让行点；
    若存在让行点，则基于所述状态信息和所述让行点，规划生成所述让行路径。
  10. 根据权利要求9所述的方法，其特征在于，所述基于所述可让行区域确定是否存在让行点，包括：
    基于所述可让行区域和路网拓扑结构，确定是否存在让行点；
    所述路网拓扑结构包括所述可让行区域对应的路网信息。
  11. 根据权利要求9或10所述的方法，其特征在于，所述若存在让行点，则基于所述状态信息和让行点，规划生成所述让行路径，包括：
    若让行点为多个，则选择与所述智能驾驶车辆的位置信息距离最近的让行点作为可达让行点；或者，若让行点为多个，则基于通行车辆和智能驾驶车辆的位置、速度及航向，选择一个让行点作为可达让行点；
    基于所述状态信息和所述可达让行点，规划生成所述让行路径。
  12. 根据权利要求9所述的方法，其特征在于，进一步包括：
    若不存在让行点，则调整智能驾驶车辆的优先级，使所述智能驾驶车辆成为所述特殊道路中其他通行车辆进行让行的车辆。
  13. 根据权利要求1所述的方法，其特征在于，进一步包括：
    基于所述让行路径完成让行后，依据目的地和智能驾驶车辆当前位置，生成规划路径。
  14. 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13中任一项所述方法的步骤。
  15. 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13任一项所述方法的步骤。
  16. 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：
    让行确定单元，用于判断所述特殊道路上是否存在需要智能驾驶车辆让行的通行车辆；
    让行路径生成单元，用于基于存在需要让行的通行车辆，根据所述智能驾驶车辆的状态信息确定可让行区域；基于所述智能驾驶车辆的状态信息和所述可让行区域，规划生成所述智能驾驶车辆的让行路径；
    让行路径行驶单元，用于控制智能驾驶车辆按照让行路径行驶。
  17. 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16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/CN2019/093808 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 WO2020258276A1 (zh)

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Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111429741B (zh) * 2020-03-24 2022-04-01 江苏徐工工程机械研究院有限公司 交通管理方法、装置和***、服务器和存储介质
CN111506076A (zh) * 2020-05-11 2020-08-07 张学志 一种用于移动服务的自动驾驶车辆与***
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
CN114170803B (zh) * 2021-12-15 2023-06-16 阿波罗智联(北京)科技有限公司 路侧感知***和交通控制方法

Citations (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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***
CN106125731A (zh) * 2016-07-21 2016-11-16 上海海事大学 一种基于后方车辆行驶意图识别的无人驾驶车辆运动控制***及方法
CN106683465A (zh) * 2017-01-03 2017-05-17 北京汽车集团有限公司 生成及发送车辆避让提醒的方法、装置
KR101769533B1 (ko) * 2016-02-18 2017-08-18 세연아이넷(주) 재난지역 소방도로 확보를 위한 자동 차량 이동명령시스템
CN107134160A (zh) * 2016-02-29 2017-09-05 法拉第未来公司 紧急信号检测和响应
CN206889986U (zh) * 2017-05-09 2018-01-16 安徽文康科技有限公司 车载视频记录取证设备的固定结构
CN107633684A (zh) * 2017-11-22 2018-01-26 河南大学 一种用于无人驾驶车的特种车辆识别方法
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 (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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***
KR101769533B1 (ko) * 2016-02-18 2017-08-18 세연아이넷(주) 재난지역 소방도로 확보를 위한 자동 차량 이동명령시스템
CN107134160A (zh) * 2016-02-29 2017-09-05 法拉第未来公司 紧急信号检测和响应
CN106125731A (zh) * 2016-07-21 2016-11-16 上海海事大学 一种基于后方车辆行驶意图识别的无人驾驶车辆运动控制***及方法
CN106683465A (zh) * 2017-01-03 2017-05-17 北京汽车集团有限公司 生成及发送车辆避让提醒的方法、装置
CN206889986U (zh) * 2017-05-09 2018-01-16 安徽文康科技有限公司 车载视频记录取证设备的固定结构
CN107633684A (zh) * 2017-11-22 2018-01-26 河南大学 一种用于无人驾驶车的特种车辆识别方法
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***

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