WO2020258277A1 - Procédé et appareil permettant à un véhicule à conduite intelligente de s'écarter, et dispositif monté sur le véhicule - Google Patents

Procédé et appareil permettant à un véhicule à conduite intelligente de s'écarter, et dispositif monté sur le véhicule Download PDF

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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
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WIPO (PCT)
Prior art keywords
vehicle
intelligent driving
driving vehicle
information
road
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PCT/CN2019/093809
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English (en)
Chinese (zh)
Inventor
赵世杰
马万里
周小成
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驭势科技（北京）有限公司
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Priority to CN201980001028.8A priority Critical patent/CN110603181B/zh
Priority to PCT/CN2019/093809 priority patent/WO2020258277A1/fr
Publication of WO2020258277A1 publication Critical patent/WO2020258277A1/fr

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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
    • 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.

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Abstract

L'invention concerne également un procédé et un appareil permettant à un véhicule à conduite intelligente (003) de s'écarter, ainsi qu'un dispositif monté sur le véhicule. Le véhicule à conduite intelligente (003) se déplace sur une route spéciale, la route spéciale étant équipée d'une pluralité d'unités de surveillance de route (002). Le procédé selon l'invention consiste : à déterminer une unité de surveillance de route (002) correspondante, en fonction d'informations d'état du véhicule à conduite intelligente (003) ; à recevoir des informations de surveillance de l'unité de surveillance de route (002) correspondante ; à déterminer la présence d'un véhicule qui passe (004) devant être évité, en fonction des informations de surveillance ; et à commander, le cas échéant, le véhicule à conduite intelligente (003) pour qu'il évite le véhicule qui passe. Selon l'invention, la coopération efficace entre les véhicules et la route permet d'améliorer l'efficacité de croissement et de garantir la sécurité routière.
PCT/CN2019/093809 2019-06-28 2019-06-28 Procédé et appareil permettant à un véhicule à conduite intelligente de s'écarter, et dispositif monté sur le véhicule WO2020258277A1 (fr)

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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系统
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系统

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系统
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系统

Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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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 路侧感知系统和交通控制方法
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 一种基于车辆管理系统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 一种基于车辆管理系统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
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