WO2020135740A1 - 自动驾驶车辆的换道方法、***及车辆 - Google Patents

自动驾驶车辆的换道方法、***及车辆 Download PDF

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Publication number
WO2020135740A1
WO2020135740A1 PCT/CN2019/129282 CN2019129282W WO2020135740A1 WO 2020135740 A1 WO2020135740 A1 WO 2020135740A1 CN 2019129282 W CN2019129282 W CN 2019129282W WO 2020135740 A1 WO2020135740 A1 WO 2020135740A1
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Prior art keywords
vehicle
driving
lane
autonomous
distance
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PCT/CN2019/129282
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English (en)
French (fr)
Inventor
和林
甄龙豹
常仕伟
张凯
葛建勇
刘宏伟
张健
李卫
杨凯
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长城汽车股份有限公司
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Priority to EP19903772.2A priority Critical patent/EP3888985B1/en
Publication of WO2020135740A1 publication Critical patent/WO2020135740A1/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
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/18—Propelling the vehicle
    • B60W30/18009—Propelling the vehicle related to particular drive situations
    • B60W30/18163—Lane change; Overtaking manoeuvres
    • 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
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
    • B60W30/09—Taking automatic action to avoid collision, e.g. braking and steering
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/09—Arrangements for giving variable traffic instructions
    • G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
    • G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
    • G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
    • G08G1/096725—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control where the received information generates an automatic action on the vehicle control
    • 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
    • B60W2552/00—Input parameters relating to infrastructure
    • B60W2552/53—Road markings, e.g. lane marker or crosswalk
    • 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
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/40—Dynamic objects, e.g. animals, windblown objects
    • B60W2554/404—Characteristics
    • B60W2554/4042—Longitudinal speed
    • 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
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/80—Spatial relation or speed relative to objects
    • B60W2554/801—Lateral distance
    • 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
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/80—Spatial relation or speed relative to objects
    • B60W2554/802—Longitudinal distance
    • 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
    • B60W2556/00—Input parameters relating to data
    • B60W2556/45—External transmission of data to or from the vehicle
    • B60W2556/50—External transmission of data to or from the vehicle of positioning data, e.g. GPS [Global Positioning System] data

Definitions

  • the present application relates to the field of automotive technology, and in particular, to a lane change method, system, and vehicle for autonomous driving vehicles.
  • Self-driving car refers to: through visual sensors, lidar, millimeter wave radar, ultrasonic radar, monitoring devices and positioning systems and other devices cooperate with each other to realize the automatic operation of the vehicle without any human active operation.
  • the vehicle operation was liberated. It uses computers, sensors, information fusion, communication, artificial intelligence, and automatic control technologies to plan vehicle travel routes and arrive at preset locations in real time.
  • Autonomous vehicles need to change lanes while driving.
  • whether the lane can be changed is determined based on the speed and distance of the surrounding vehicles or obstacles.
  • the lane change condition is initially met and the vehicle performs Lane-changing action, a short time after the lane-changing is completed, due to changes in the working conditions around the vehicle, the vehicle has another lane-changing intention, and it is necessary to re-change the lane. In this case, frequent lane-changing may affect vehicle safety.
  • this application aims to propose a method for changing lanes of autonomous vehicles.
  • the lane-changing method of the self-driving vehicle can classify the objects in different areas in the front.
  • the virtual target can be used to reduce the frequency of the lane change of the vehicle during driving, and it can be previewed in the adjacent lane in advance.
  • the slowest vehicle can reduce the unnecessary lane-changing behavior of the vehicle, and can ensure the accurate detection of the target during the lane-changing driving of the self-driving vehicle, thereby improving the driving safety.
  • a lane changing method for an autonomous driving vehicle includes the following steps: dividing a plurality of driving areas according to road information and vehicle position, wherein the plurality of driving areas include a front side area; and acquiring a plurality of objects in the front side area The speed of the target and the distance to the self-driving vehicle; a virtual target is generated according to the minimum speed and the minimum distance to determine whether to change lanes according to the virtual target.
  • the plurality of driving areas further includes a side area
  • the method further includes: acquiring a lateral distance and a vertical distance between the plurality of object targets in the side area and the autonomous vehicle; according to the minimum lateral distance and the minimum longitudinal distance A virtual target is generated at a distance to determine whether to change lanes based on the virtual target.
  • the plurality of driving areas further include a rear side area
  • the method further includes: acquiring a danger level of collision of a plurality of object targets in the rear side area with the autonomous vehicle; according to the highest risk level corresponding to Objects determine whether to change lanes.
  • the danger level is determined by the collision time and collision distance.
  • the dividing of multiple driving areas according to road information and vehicle position includes: obtaining map information, extracting the road information from the map information, wherein the road information includes lane line data; The lane line data is mapped into the vehicle body coordinate system; the plurality of driving regions are divided according to the position of the autonomous vehicle in the vehicle body coordinate system.
  • the method of the self-driving vehicle of the present application can classify objects in different areas in the front.
  • the virtual target can be used to reduce the frequency of lane changes of the vehicle during driving, and it can preview the adjacent lane in advance.
  • the vehicle with the slowest internal speed reduces unnecessary lane changes of the vehicle.
  • Information such as the current road curvature, width, and lane line type is introduced into the driving area division, which can achieve accurate division of object targets under straight and curved road conditions. Automated vehicles can accurately detect targets during lane changes, thereby improving driving safety.
  • the second object of the present application is to propose a lane change system for autonomous vehicles.
  • the system can classify the objects in different areas in the front.
  • the virtual target can be used to reduce the frequency of lane changes during driving. It can preview the slowest vehicles in the adjacent lane in advance, reducing The vehicle does not need to change lanes, which can ensure the accurate detection of the target during the lane change process of the automatic driving vehicle, thereby improving the driving safety.
  • a lane-changing system for an autonomous driving vehicle includes: a division module for dividing a plurality of driving areas according to road information and a vehicle position, wherein the plurality of driving areas include a front side area; and a target selection module for obtaining A plurality of object speeds and distances from the autonomous vehicle in the front side area, and a virtual target is generated according to the minimum speed and the minimum distance; a control module is used to determine whether to change lanes according to the virtual target.
  • the plurality of driving areas further include a side area
  • the target selection module is further configured to obtain a horizontal distance and a vertical distance between a plurality of object targets in the side area and the autonomous vehicle, and according to the minimum lateral distance and Virtual targets are generated with minimum vertical spacing.
  • the plurality of driving areas further include a rear side area
  • the target selection module is further used to obtain a hazard level of collision of a plurality of object targets in the rear side area with the autonomous vehicle, and the control module also uses To determine whether to change lanes based on the object corresponding to the object with the highest danger level.
  • the dividing module is used to obtain map information, extract the road information from the map information, wherein the road information includes lane line data, and map the lane line data to the vehicle body coordinate system And dividing the plurality of driving regions according to the position of the autonomous vehicle in the vehicle body coordinate system.
  • the lane change system of the self-driving vehicle has the same advantages as the above-mentioned lane change method of the self-driving vehicle compared to the prior art, and will not be repeated here.
  • the third purpose of the present application is to propose a vehicle that can classify objects in different areas in front of it.
  • the virtual target can be used to reduce the frequency of lane changes during driving. Aiming at the slowest vehicle in the adjacent lane to reduce the unnecessary lane-changing movement of the vehicle, it can ensure the accurate detection of the target during the lane-changing process of the autonomous driving vehicle, thereby improving the driving safety.
  • a vehicle is provided with a lane change system for an autonomous driving vehicle as described in any of the above embodiments.
  • the vehicle has the same advantages as the above-mentioned lane change system of the self-driving vehicle compared to the prior art, and will not be repeated here.
  • the fourth object of the present application is to propose a computer-readable storage medium.
  • FIG. 1 is a flowchart of a method for changing lanes of an autonomous driving vehicle according to an embodiment of the present application
  • FIG. 2 is a schematic diagram of a driving area division of a method for changing lanes of an autonomous driving vehicle according to an embodiment of the present application
  • FIG. 3 is a schematic diagram of selecting a virtual target in a front area of a method for changing lanes of an autonomous driving vehicle according to an embodiment of the present application;
  • FIG. 4 is a schematic diagram of selecting a virtual target in a side area of a method for changing lanes of an autonomous driving vehicle according to an embodiment of the present application
  • FIG. 5 is a structural block diagram of an automatic driving vehicle lane change system according to an embodiment of the present application.
  • FIG. 1 is a flowchart of a lane change method of an autonomous driving vehicle according to an embodiment of the present application.
  • a lane change method for an autonomous driving vehicle includes the following steps:
  • S101 Divide a plurality of driving areas according to road information and vehicle positions, where the plurality of driving areas include a front side area.
  • the front area may include three areas corresponding to the left front, the front, and the right front, such as the front left area, the front front area, and the right front area.
  • the multiple driving areas also include a side area and a rear area, where the side area includes two areas corresponding to the left side and the right side, such as called : The left area and the right area.
  • the rear area may include three areas corresponding to the left rear, the rear, and the right rear, such as the left rear area, the front rear area, and the right rear area.
  • dividing a plurality of driving areas according to road information and vehicle position includes: acquiring map information, extracting the road information from the map information, wherein the road information includes lane line data, and the lane The line data is mapped into the vehicle body coordinate system, and the plurality of driving regions are divided according to the position of the autonomous vehicle in the vehicle body coordinate system.
  • the map information includes: the latitude and longitude of the discrete point of the lane line (the latitude and longitude is based on the center of the earth), the heading angle of the discrete point (the clockwise direction is 0° in the true north direction), the type of lane line, the width of the lane, the number of lanes, and the boundary of the road
  • the coordinates are converted to the vehicle body coordinate system through coordinates to provide the road lane line information required during the vehicle lane change process, and the object information in the detection area can be mapped to different driving areas.
  • the points A and B of the longitudinal axis of the front and rear of the vehicle are projected onto the center line O1 and O2 of the lane.
  • the two points perpendicular to the lane of the projected point are perpendicular to the lane.
  • the current driving area of the vehicle is divided as shown in the figure.
  • the horizontal and vertical coordinate values are used to determine the driving area where the object target (such as other vehicles) is located.
  • S102 Acquire the speeds of multiple object targets in the front area and the distance to the autonomous vehicle.
  • S103 Generate a virtual target according to the minimum speed and the minimum distance to determine whether to change lanes according to the virtual target.
  • FIG. 3 shows the object targets in the left front area of the vehicle, that is, there are three object targets in the left front area, such as object targets G1, G2, and G3. Extract the object targets G1, G2 and G3 in the front left area, then arrange the speed of the object targets G1, G2 and G3 from small to large, obtain the velocity sequence Vely_Array1, and carry out the longitudinal distance of the object targets G1, G2 and G3 from near to far Arrange and get the distance sequence Dis_Array1. Extract the minimum velocity in the Vely_Array1 sequence again, extract the minimum distance in the Dis_Array1 sequence, and determine the virtual target G0 based on the minimum velocity and the minimum distance.
  • the speeds of G1, G2, and G3 are 100 km/h, 90 km/h, and 95 km/h, respectively, and the longitudinal distance between G1, G2, and G3 and the vehicle (that is, the self-driving vehicle G4) At 65 meters, 110 meters and 160 meters respectively, the distance between the virtual target G0 and the autonomous driving vehicle G4 is 65 meters, and the virtual target speed is 90 km/h.
  • taking the object targets in the side area as an example then: acquiring the horizontal and vertical distances between the multiple object targets in the side area and the autonomous vehicle; generating a virtual target according to the minimum horizontal distance and the minimum vertical distance To determine whether to change lanes based on the virtual target.
  • the object targets G5 and G6 in the left area first extract the object targets G5 and G6 in the left area, and secondly, the object targets G5 and G6 and the own vehicle (ie: autonomous driving vehicle G4) The longitudinal distance between them is arranged from near to far, and the distance sequence Dis_Array2 is obtained.
  • the horizontal distance between the object target and the own vehicle ie: autonomous driving vehicle G4 is arranged from near to far, to obtain the distance sequence Dis_Array3, then extract the minimum longitudinal distance in Dis_Array2, extract the minimum lateral distance in Dis_Array3, according to the minimum The vertical distance and the minimum horizontal distance acquire the virtual target G0.
  • the longitudinal distance between G5 and G6 and the vehicle is 2 meters and 3 meters respectively, and the lateral distance between G5 and G6 and the vehicle is 2 meters and 1 meter respectively, then the minimum lateral distance is 1 meter and the minimum longitudinal The distance is 2 meters, the minimum horizontal distance between the virtual target G0 and the vehicle is 1 meter, and the minimum longitudinal distance is 2 meters.
  • the longitudinal distance refers to the longitudinal distance from the center point of the vehicle.
  • the danger level of collision of a plurality of object targets in the rear area with the autonomous vehicle is obtained; whether to change lanes is determined according to the object target corresponding to the highest danger level.
  • the danger level is determined by the collision time and collision distance.
  • the object with the highest danger level in the ranking is the final target.
  • the recommended value of TTC is 2.5 seconds. When TTC is less than 2.5 seconds, it is a dangerous working condition, and when it is greater than 2.5 seconds, it is a safe working condition.
  • the distance between the vehicles also needs to be considered.
  • the speed of the rear car is not much different from that of the car.
  • the speed of the car is 80 km/h
  • the speed of the rear car is 81 km/h
  • the distance between the two cars is 3 meters
  • the calculated TTC value is 10.8, but this situation is still very dangerous, so you can set a safe distance, such as 6 meters, in this case, although the TTC value is greater than 2.5, but because the distance is less than 6 Meters, therefore, are also dangerous conditions.
  • a collision may occur when the speed of the following vehicle is greater than the positive value of the vehicle's TTC, and when the rear vehicle is slow, the collision will not occur.
  • TTC the speed of the following vehicle is greater than the positive value of the vehicle's TTC
  • the relative distance> 6 meters it is considered to be a safe working condition, otherwise it is a dangerous working condition.
  • the virtual vehicle can be used to reduce the lane change frequency of the vehicle during driving.
  • the accurate division of the target of the lower object can ensure the accurate detection of the target during the lane change of the automatic driving vehicle, thereby improving the driving safety.
  • FIG. 5 is a structural block diagram of an automatic driving vehicle lane change system according to an embodiment of the present application.
  • a lane change system 600 for an autonomous driving vehicle includes: a division module 610, a target selection module 620, and a control module 630.
  • the dividing module 610 is used to divide a plurality of driving areas according to road information and vehicle positions, wherein the plurality of driving areas include a front side area.
  • the target selection module 620 is used to obtain the speeds and distances of the multiple object targets in the front side area and the autonomous driving vehicle, and generate a virtual target according to the minimum speed and the minimum distance.
  • the control module 630 is used to determine whether to change lanes according to the virtual target.
  • the plurality of driving areas further include a lateral area
  • the target selection module 620 is further configured to obtain a lateral distance and a longitudinal distance between the multiple object targets of the lateral area and the autonomous vehicle And generate virtual targets based on the minimum horizontal spacing and the minimum vertical spacing.
  • the plurality of driving areas further include a rear side area
  • the target selection module 620 is further used to obtain a danger level that a plurality of object targets in the rear side area collide with an autonomous vehicle
  • the control module 630 is also used to determine whether to change lanes according to the object target corresponding to the highest danger level.
  • the dividing module 610 is used to obtain map information, extract the road information from the map information, wherein the road information includes lane line data, and the lane line data Mapping into the vehicle body coordinate system, and dividing the plurality of driving regions according to the position of the autonomous vehicle in the vehicle body coordinate system.
  • the object targets in different areas in the front, side, and rear can be classified and processed.
  • the frequency of the lane change of the vehicle during driving can be reduced by the virtual target.
  • the accurate division of the target of the lower object can ensure the accurate detection of the target during the lane change of the automatic driving vehicle, thereby improving the driving safety.
  • an embodiment of the present application discloses a vehicle provided with a lane change system for an autonomous driving vehicle as in any of the above embodiments.
  • the vehicle can classify objects in different areas in front, side, and rear.
  • the virtual target can be used to reduce the frequency of the lane change of the vehicle during driving. It can preview the speed in the adjacent lane in advance.
  • the slowest vehicle reducing the unnecessary lane change of the vehicle, introducing the current road curvature, width, lane line type and other information into the driving area division, which can achieve accurate division of object targets under straight and curved road conditions, and can ensure automatic driving
  • the target is accurately detected during the course of the lane change, thereby improving driving safety.
  • the computer-readable storage medium of the embodiment of the present application has stored thereon a lane change program for an autonomous driving vehicle.
  • the lane change program of the autonomous driving vehicle is executed by a processor, the automatic driving as described in any of the foregoing embodiments of the present application is realized The vehicle's lane change method.
  • a "computer-readable medium” may be any device that can contain, store, communicate, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device.
  • computer-readable media include the following: electrical connections (electronic devices) with one or more wires, portable computer cartridges (magnetic devices), random access memory (RAM), Read only memory (ROM), erasable and editable read only memory (EPROM or flash memory), fiber optic devices, and portable compact disk read only memory (CDROM).
  • the computer-readable medium may even be paper or other suitable medium on which the program can be printed, because, for example, by optically scanning the paper or other medium, followed by editing, interpretation, or other appropriate if necessary Process to obtain the program electronically and then store it in computer memory.
  • each part of the present application may be implemented by hardware, software, firmware, or a combination thereof.
  • multiple steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system.
  • a logic gate circuit for implementing a logic function on a data signal
  • PGA programmable gate arrays
  • FPGA field programmable gate arrays

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Abstract

一种自动驾驶车辆的换道方法、***及车辆。其中，自动驾驶车辆的换道方法包括：根据道路信息和车辆位置，划分多个行驶区域，该多个行驶区域包括前侧区域（S101）；获取该前侧区域的多个物体目标的速度和与自动驾驶车辆的距离（S102）；根据最小速度和最小距离生成虚拟目标，以根据该虚拟目标判断是否换道（S103），该自动驾驶车辆的换道方法可以对前方不同区域物体目标进行分类处理，在前方区域物体目标提取中通过虚拟目标降低本车在行驶过程中换道频率，能够提前预瞄到相邻本车道内速度最慢车辆，减少本车不必要换道动作，保证自动驾驶车辆在换道行驶过程中目标准确检测，从而提升行车安全。

Description

自动驾驶车辆的换道方法、***及车辆
相关申请的交叉引用
本申请要求长城汽车股份有限公司于2018年12月29日提交的、申请名称为“自动驾驶车辆的换道方法、***及车辆”的、中国专利申请号为“201811636150.6”的优先权。
技术领域
本申请涉及汽车技术领域，特别涉及一种自动驾驶车辆的换道方法、***及车辆。
背景技术
自动驾驶汽车指：通过视觉传感器、激光雷达、毫米波雷达、超声波雷达、监控装置和定位***等设备相互协同合作，实现没有任何人类主动的操作的情况下，自动地操作车辆将驾驶员从繁重的车辆操作中解放出来。它集中运用了计算机、传感器、信息融合、通讯、人工智能及自动控制等技术，实时规划车辆出行路线并到达预设地点。
自动驾驶车辆在行驶过程中需要换道行驶，相关技术中，根据周围的车辆或者障碍物的速度和距离等信息来判断是否能够换道，然而，有时会出现起初满足换道条件，本车进行换道动作，换道完成很短时间内，由于车辆周围工况变化使车辆又产生换道意图，需要进行重新换道，此时，频繁的换道可能会影响车辆安全。
发明内容
有鉴于此，本申请旨在提出一种自动驾驶车辆的换道方法。该自动驾驶车辆的换道方法可以对前方不同区域物体目标进行分类处理，在前方区域物体目标提取中通过虚拟目标降低本车在行驶过程中换道频率，能够提前预瞄到相邻本车道内速度最慢车辆，减少本车不必要换道动作，能够保证自动驾驶车辆在换道行驶过程中目标准确检测，从而提升行车安全。
为达到上述目的，本申请的技术方案是这样实现的：
一种自动驾驶车辆的换道方法，包括以下步骤：根据道路信息和车辆位置，划分多个行驶区域，其中，所述多个行驶区域包括前侧区域；获取所述前侧区域的多个物体目标的速度和与自动驾驶车辆的距离；根据最小速度和最小距离生成虚拟目标，以根据所述虚拟目标判断是否换道。
进一步的，所述多个行驶区域还包括侧方区域，所述方法，还包括：获取所述侧方区域的多个物体目标与自动驾驶车辆横向间距和纵向间距；根据最小横向间距和最小纵向间距生成虚拟目标，以根据所述虚拟目标判断是否换道。
进一步的，所述多个行驶区域还包括后侧区域，所述方法，还包括：获取所述后侧区域的多个物体目标与自动驾驶车辆发生碰撞的危险等级；根据对应于危险等级最高的物体目标判断是否换道。
进一步的，所述危险等级由碰撞时间及碰撞距离确定。
进一步的，所述根据道路信息和车辆位置，划分多个行驶区域，包括：获取地图信息，从所述地图信息中提取所述道路信息，其中，所述道路信息包括车道线数据；将所述车道线数据映射至车体坐标系中；根据所述自动驾驶车辆在所述车体坐标系中的位置划分所述多个行驶区域。
本申请的自动驾驶车辆的方法，可以对前方不同区域物体目标进行分类处理，在前方区域物体目标提取中通过虚拟目标降低本车在行驶过程中换道频率，能够提前预瞄到相邻本车道内速度最慢车辆，减少本车不必要换道动作，行驶区域划分中引入当前道路曲率、宽度、车道线的类型等信息，能够实现直道及不同曲率弯道条件下物体目标准确划分，能够保证自动驾驶车辆在换道行驶过程中目标准确检测，从而提升行车安全。
本申请的第二个目的在于提出一种自动驾驶车辆的换道***。该***可以对前方不同区域物体目标进行分类处理，在前方区域物体目标提取中通过虚拟目标降低本车在行驶过程中换道频率，能够提前预瞄到相邻本车道内速度最慢车辆，减少本车不必要换道动作，能够保证自动驾驶车辆在换道行驶过程中目标准确检测，从而提升行车安全。
为达到上述目的，本申请的技术方案是这样实现的：
一种自动驾驶车辆的换道***，包括：划分模块，用于根据道路信息和车辆位置，划分多个行驶区域，其中，所述多个行驶区域包括前侧区域；目标选择模块，用于获取所述前侧区域的多个物体目标的速度和与自动驾驶车辆的距离，并根据最小速度和最小距离生成虚拟目标；控制模块，用于根据所述虚拟目标判断是否换道。
进一步的，所述多个行驶区域还包括侧方区域，所述目标选择模块还用于获取所述侧方区域的多个物体目标与自动驾驶车辆横向间距和纵向间距，并根据最小横向间距和最小纵向间距生成虚拟目标。
进一步的，所述多个行驶区域还包括后侧区域，所述目标选择模块还用于获取所述后侧区域的多个物体目标与自动驾驶车辆发生碰撞的危险等级，所述控制模块还用于根据对应于危险等级最高的物体目标判断是否换道。
进一步的，所述划分模块用于获取地图信息，从所述地图信息中提取所述道路信息，其中，所述道路信息包括车道线数据，并将所述车道线数据映射至车体坐标系中，以及根据所述自动驾驶车辆在所述车体坐标系中的位置划分所述多个行驶区域。
所述的自动驾驶车辆的换道***与上述的自动驾驶车辆的换道方法相对于现有技术所具有的优势相同，在此不再赘述。
本申请的第三个目的在于提出一种车辆，该车辆可以对前方不同区域物体目标进行分类处理，在前方区域物体目标提取中通过虚拟目标降低本车在行驶过程中换道频率，能够提前预瞄到相邻本车道内速度最慢车辆，减少本车不必要换道动作，能够保证自动驾驶车辆在换道行驶过程中目标准确检测，从而提升行车安全。
为达到上述目的，本申请的技术方案是这样实现的：
一种车辆，设置有如上述任意一个实施例所述的自动驾驶车辆的换道***。
所述的车辆与上述的自动驾驶车辆的换道***相对于现有技术所具有的优势相同，在此不再赘述。
本申请的第四个目的在于提出一种计算机可读存储介质。
为达到上述目的，本申请的技术方案是这样实现的：
一种计算机可读存储介质，其上存储有自动驾驶车辆的换道程序，该自动驾驶车辆的换道程序被处理器执行时实现根据上述第一方面所述的自动驾驶车辆的换道方法。
附图说明
构成本申请的一部分的附图用来提供对本申请的进一步理解，本申请的示意性实施例及其说明用于解释本申请，并不构成对本申请的不当限定。在附图中：
图1为本申请一个实施例所述的自动驾驶车辆的换道方法的流程图；
图2为本申请一个实施例所述的自动驾驶车辆的换道方法的行驶区域划分示意图；
图3为本申请一个实施例所述的自动驾驶车辆的换道方法的前侧区域的虚拟目标选择示意图；
图4为本申请一个实施例所述的自动驾驶车辆的换道方法的侧方区域的虚拟目标选择示意图；
图5为本申请一个实施例所述的自动驾驶车辆的换道***的结构框图。
具体实施方式
需要说明的是，在不冲突的情况下，本申请中的实施例及实施例中的特征可以相互组 合。
下面将参考附图并结合实施例来详细说明本申请。
图1是根据本申请一个实施例的自动驾驶车辆的换道方法的流程图。
如图1所示，并结合图2，根据本申请一个实施例的自动驾驶车辆的换道方法，包括如下步骤：
S101：根据道路信息和车辆位置，划分多个行驶区域，其中，多个行驶区域包括前侧区域。
其中，如图2所示，前侧区域可包括左前方、前方和右前方对应的三个区域，例如称为：左前区域、正前区域和右前区域。
在本申请的其它示例中，如图2所示，多个行驶区域还包括侧方区域和后侧区域，其中，侧方区域包括左侧方和右侧方对应的两个区域，例如称为：左侧区域和右侧区域，后侧区域可包括左后方、后方和右后方对应的三个区域，例如称为：左后区域、正后区域和右后区域。
具体地说，根据道路信息和车辆位置，划分多个行驶区域，包括：获取地图信息，从所述地图信息中提取所述道路信息，其中，所述道路信息包括车道线数据，将所述车道线数据映射至车体坐标系中，根据所述自动驾驶车辆在所述车体坐标系中的位置划分所述多个行驶区域。
例如：地图信息包括：车道线离散点经纬度(经纬度以地心为原点)、离散点航向角(以正北方向为0°顺时针为证)、车道线类型、车道宽度、车道数量、道路边界等信息，根据车道线数据通过坐标转换至车体坐标系下，提供车辆换道过程中所需的道路车道线信息，并可将探测区域内物体目标信息映射至到不同的行驶区域中。
结合图2所示，过车头、车尾纵轴中心A、B点投影到本车道中心线O1、O2，过投影点作两条垂直于本车道垂线，将车辆当前行驶区域划分为如图2所示的八个不同区域，根据车体坐标系下道路数据与车体坐标系物体目标，横纵向坐标值判断区分物体目标(如其他车辆)所在的行驶区域。
S102：获取前侧区域的多个物体目标的速度和与自动驾驶车辆的距离。
S103：根据最小速度和最小距离生成虚拟目标，以根据所述虚拟目标判断是否换道。
如图3所示，以前侧区域的物体目标为例，图3中示出了位于车辆左前区域的物体目标，即：左前区域的物体目标为3个，如物体目标G1、G2和G3，首先提取左前区域的物体目标G1、G2和G3，其次将物体目标G1、G2和G3的速度由小到大排列，获取速度序列Vely_Array1，将物体目标G1、G2和G3的纵向距离由近及远进行排列，获取距离序列 Dis_Array1。再次提取Vely_Array1序列中速度最小值，提取Dis_Array1序列中距离最小值，根据速度最小值与距离最小值确定虚拟目标G0。
例如：G1、G2和G3的速度分别为100千米/小时、90千米/小时和95千米/小时，G1、G2和G3与本车(即：自动驾驶车辆G4)之间的纵向距离分别为65米、110米和160米，则虚拟目标G0距离自动驾驶车辆G4之间的距离为65米，虚拟目标速度为90千米/小时。最后，根据虚拟目标G0计算车辆TTC值(TTC即自动驾驶车辆G4与前方车辆碰撞时间，TTC＝相对速度/相对距离；相对速度＝本车速度-前车速度)。
如图4所示，以侧方区域的物体目标为例，则：获取所述侧方区域的多个物体目标与自动驾驶车辆横向间距和纵向间距；根据最小横向间距和最小纵向间距生成虚拟目标，以根据所述虚拟目标判断是否换道。
如图4所示，以左侧区域中的物理目标G5和G6为例，首先提取左侧区域的物体目标G5和G6，其次将物体目标G5和G6与本车(即：自动驾驶车辆G4)之间的纵向距离由近及远进行排列，获取距离序列Dis_Array2。物体目标与本车(即：自动驾驶车辆G4)之间的横向距离由近及远进行排列，获取距离序列Dis_Array3，然后，提取Dis_Array2中的最小纵向距离，提取Dis_Array3中的最小横向距离，根据最小纵向距离和最小横向距离获取虚拟目标G0。
例如：G5和G6与本车之间的纵向距离分别为2米和3米，G5和G6与本车之间的横向距离分别为2米和1米，则最小横向距离为1米，最小纵向距离为2米，虚拟目标G0与本车之间的最小横向距离为1米，最小纵向距离为2米。其中，在该示例中，纵向距离指与本车的中心点的纵向距离。
以后侧区域的物体目标为例，则获取所述后侧区域的多个物体目标与自动驾驶车辆发生碰撞的危险等级；根据对应于危险等级最高的物体目标判断是否换道。其中，危险等级由碰撞时间及碰撞距离确定。
即：左后(正后/右后)区域物体目标选择，首先提取左后(正后/右后)区域物体目标(简称目标)，其次根据车辆行驶工况危险等级，确定不同目标对本车行驶时的影响严重程度，根据危险等级对目标进行排序。将排序中危险等级最高的目物体作为最终的目标。其中，危险等级由碰撞时间及碰撞距离确定，其中，碰撞时间表示为TTC，即自动驾驶车辆与前方车辆的碰撞时间，TTC＝相对速度/相对距离；相对速度＝后车速度-本车速度。
为提供给驾驶员足够反应时间，TTC推荐值为2.5秒，当TTC小于2.5秒时属于危险工况，大于2.5秒时属安全工况。
当然，还需要考虑辆车之间的距离，例如，后车与本车的车速相差不大，如本车的车 速为80千米/小时，后车的车速81千米/小时，两车相距3米，此时，计算的TTC值为10.8，但这种情况仍然非常危险，因此，可以设置一个安全距离，例如6米，则该种情况下，虽然TTC值大于2.5，但是由于距离小于6米，因此，也属于危险工况。
需要说明的是，当后车速度大于本车TTC为正值可能发生碰撞，当后车慢时TTC为负值不会发生碰撞。例如：设定TTC>2.5秒，且相对距离>6米，认为是安全工况，否则属于危险工况。
根据本申请实施例的自动驾驶车辆的方法，可以对前方、侧方、后方不同区域物体目标进行分类处理，在前方区域物体目标提取中通过虚拟目标降低本车在行驶过程中换道频率，能够提前预瞄到相邻本车道内速度最慢车辆，减少本车不必要换道动作，行驶区域划分中引入当前道路曲率、宽度、车道线的类型等信息，能够实现直道及不同曲率弯道条件下物体目标准确划分，能够保证自动驾驶车辆在换道行驶过程中目标准确检测，从而提升行车安全。
图5是根据本申请一个实施例的自动驾驶车辆的换道***的结构框图。如图5所示，根据本申请一个实施例的自动驾驶车辆的换道***600，包括：划分模块610、目标选择模块620和控制模块630。
其中，划分模块610用于根据道路信息和车辆位置，划分多个行驶区域，其中，所述多个行驶区域包括前侧区域。目标选择模块620用于获取所述前侧区域的多个物体目标的速度和与自动驾驶车辆的距离，并根据最小速度和最小距离生成虚拟目标。控制模块630用于根据所述虚拟目标判断是否换道。
在本申请的一个实施例中，所述多个行驶区域还包括侧方区域，所述目标选择模块620还用于获取所述侧方区域的多个物体目标与自动驾驶车辆横向间距和纵向间距，并根据最小横向间距和最小纵向间距生成虚拟目标。
在本申请的一个实施例中，所述多个行驶区域还包括后侧区域，所述目标选择模块620还用于获取所述后侧区域的多个物体目标与自动驾驶车辆发生碰撞的危险等级，所述控制模块630还用于根据对应于危险等级最高的物体目标判断是否换道。
在本申请的一个实施例中，所述划分模块610用于获取地图信息，从所述地图信息中提取所述道路信息，其中，所述道路信息包括车道线数据，并将所述车道线数据映射至车体坐标系中，以及根据所述自动驾驶车辆在所述车体坐标系中的位置划分所述多个行驶区域。
根据本申请实施例的自动驾驶车辆的***，可以对前方、侧方、后方不同区域物体目标进行分类处理，在前方区域物体目标提取中通过虚拟目标降低本车在行驶过程中换道频 率，能够提前预瞄到相邻本车道内速度最慢车辆，减少本车不必要换道动作，行驶区域划分中引入当前道路曲率、宽度、车道线的类型等信息，能够实现直道及不同曲率弯道条件下物体目标准确划分，能够保证自动驾驶车辆在换道行驶过程中目标准确检测，从而提升行车安全。
需要说明的是，本申请实施例的自动驾驶车辆的换道***的具体实现方式与本申请实施例的自动驾驶车辆的换道方法的具体实现方式类似，具体请参见方法部分的描述，为了减少冗余，此处不做赘述。
进一步地，本申请的实施例公开了一种车辆，设置有如上述任意一个实施例中的自动驾驶车辆的换道***。该车辆可以对前方、侧方、后方不同区域物体目标进行分类处理，在前方区域物体目标提取中通过虚拟目标降低本车在行驶过程中换道频率，能够提前预瞄到相邻本车道内速度最慢车辆，减少本车不必要换道动作，行驶区域划分中引入当前道路曲率、宽度、车道线的类型等信息，能够实现直道及不同曲率弯道条件下物体目标准确划分，能够保证自动驾驶车辆在换道行驶过程中目标准确检测，从而提升行车安全。
另外，根据本申请实施例的车辆的其它构成以及作用对于本领域的普通技术人员而言都是已知的，为了减少冗余，此处不做赘述。
本申请实施例的计算机可读存储介质，其上存储有自动驾驶车辆的换道程序，该自动驾驶车辆的换道程序被处理器执行时实现如本申请前述任意一个实施例所述的自动驾驶车辆的换道方法。
需要说明的是，在流程图中表示或在此以其他方式描述的逻辑和/或步骤，例如，可以被认为是用于实现逻辑功能的可执行指令的定序列表，可以具体实现在任何计算机可读介质中，以供指令执行***、装置或设备(如基于计算机的***、包括处理器的***或其他可以从指令执行***、装置或设备取指令并执行指令的***)使用，或结合这些指令执行***、装置或设备而使用。就本说明书而言，"计算机可读介质"可以是任何可以包含、存储、通信、传播或传输程序以供指令执行***、装置或设备或结合这些指令执行***、装置或设备而使用的装置。计算机可读介质的更具体的示例(非穷尽性列表)包括以下：具有一个或多个布线的电连接部(电子装置)，便携式计算机盘盒(磁装置)，随机存取存储器(RAM)，只读存储器(ROM)，可擦除可编辑只读存储器(EPROM或闪速存储器)，光纤装置，以及便携式光盘只读存储器(CDROM)。另外，计算机可读介质甚至可以是可在其上打印所述程序的纸或其他合适的介质，因为可以例如通过对纸或其他介质进行光学扫描，接着进行编辑、解译或必要时以其他合适方式进行处理来以电子方式获得所述程序，然后将其存储在计算机存储器中。
应当理解，本申请的各部分可以用硬件、软件、固件或它们的组合来实现。在上述实施方式中，多个步骤或方法可以用存储在存储器中且由合适的指令执行***执行的软件或固件来实现。例如，如果用硬件来实现，和在另一个实施方式中一样，可用本领域公知的下列技术中的任一项或他们的组合来实现：具有用于对数据信号实现逻辑功能的逻辑门电路的离散逻辑电路，具有合适的组合逻辑门电路的专用集成电路，可编程门阵列(PGA)，现场可编程门阵列(FPGA)等。
本技术领域的普通技术人员可以理解实现上述实施例的方法携带的全部或部分步骤是可以通过程序来指令相关的硬件完成，所述的程序可以存储于一种计算机可读存储介质中，该程序在执行时，包括方法实施例的步骤之一或其组合。
尽管上面已经示出和描述了本申请的实施例，可以理解的是，上述实施例是示例性的，不能理解为对本申请的限制，本领域的普通技术人员在本申请的范围内可以对上述实施例进行变化、修改、替换和变型。

Claims (11)

  1. 一种自动驾驶车辆的换道方法，其特征在于，包括以下步骤：
    根据道路信息和车辆位置，划分多个行驶区域，其中，所述多个行驶区域包括前侧区域；
    获取所述前侧区域的多个物体目标的速度和与自动驾驶车辆的距离；
    根据最小速度和最小距离生成虚拟目标，以根据所述虚拟目标判断是否换道。
  2. 根据权利要求1所述的自动驾驶车辆的换道方法，其特征在于，所述多个行驶区域还包括侧方区域，所述方法，还包括：
    获取所述侧方区域的多个物体目标与自动驾驶车辆横向间距和纵向间距；
    根据最小横向间距和最小纵向间距生成虚拟目标，以根据所述虚拟目标判断是否换道。
  3. 根据权利要求1所述的自动驾驶车辆的换道方法，其特征在于，所述多个行驶区域还包括后侧区域，所述方法，还包括：
    获取所述后侧区域的多个物体目标与自动驾驶车辆发生碰撞的危险等级；
    根据对应于危险等级最高的物体目标判断是否换道。
  4. 根据权利要求3所述的自动驾驶车辆的换道方法，其特征在于，所述危险等级由碰撞时间及碰撞距离确定。
  5. 根据权利要求1-4任一项所述的自动驾驶车辆的换道方法，其特征在于，所述根据道路信息和车辆位置，划分多个行驶区域，包括：
    获取地图信息，从所述地图信息中提取所述道路信息，其中，所述道路信息包括车道线数据；
    将所述车道线数据映射至车体坐标系中；
    根据所述自动驾驶车辆在所述车体坐标系中的位置划分所述多个行驶区域。
  6. 一种自动驾驶车辆的换道***，其特征在于，包括：
    划分模块，用于根据道路信息和车辆位置，划分多个行驶区域，其中，所述多个行驶区域包括前侧区域；
    目标选择模块，用于获取所述前侧区域的多个物体目标的速度和与自动驾驶车辆的距离，并根据最小速度和最小距离生成虚拟目标；
    控制模块，用于根据所述虚拟目标判断是否换道。
  7. 根据权利要求6所述的自动驾驶车辆的换道***，其特征在于，所述多个行驶区域还包括侧方区域，所述目标选择模块还用于获取所述侧方区域的多个物体目标与自动驾驶 车辆横向间距和纵向间距，并根据最小横向间距和最小纵向间距生成虚拟目标。
  8. 根据权利要求6所述的自动驾驶车辆的换道***，其特征在于，所述多个行驶区域还包括后侧区域，所述目标选择模块还用于获取所述后侧区域的多个物体目标与自动驾驶车辆发生碰撞的危险等级，所述控制模块还用于根据对应于危险等级最高的物体目标判断是否换道。
  9. 根据权利要求6-8任一项所述的自动驾驶车辆的换道***，其特征在于，所述划分模块用于获取地图信息，从所述地图信息中提取所述道路信息，其中，所述道路信息包括车道线数据，并将所述车道线数据映射至车体坐标系中，以及根据所述自动驾驶车辆在所述车体坐标系中的位置划分所述多个行驶区域。
  10. 一种车辆，其特征在于，设置有如权利要求6-9任一项所述的自动驾驶车辆的换道***。
  11. 一种计算机可读存储介质，其上存储有自动驾驶车辆的换道程序，其特征在于，该自动驾驶车辆的换道程序被处理器执行时实现根据权利要求1-5中任一所述的自动驾驶车辆的换道方法。
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