WO2021102957A1 - 一种车道保持方法、车载设备和存储介质 - Google Patents

一种车道保持方法、车载设备和存储介质 Download PDF

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Publication number
WO2021102957A1
WO2021102957A1 PCT/CN2019/122100 CN2019122100W WO2021102957A1 WO 2021102957 A1 WO2021102957 A1 WO 2021102957A1 CN 2019122100 W CN2019122100 W CN 2019122100W WO 2021102957 A1 WO2021102957 A1 WO 2021102957A1
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WIPO (PCT)
Prior art keywords
vehicle
lane
information
line
path
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PCT/CN2019/122100
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English (en)
French (fr)
Inventor
胡子豪
王子涵
刘洋
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驭势（上海）汽车科技有限公司
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Application filed by 驭势（上海）汽车科技有限公司 filed Critical 驭势（上海）汽车科技有限公司
Priority to PCT/CN2019/122100 priority Critical patent/WO2021102957A1/zh
Priority to CN201980002776.8A priority patent/CN113677581A/zh
Publication of WO2021102957A1 publication Critical patent/WO2021102957A1/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
    • B60W30/14—Adaptive cruise control
    • B60W30/16—Control of distance between vehicles, e.g. keeping a distance to preceding vehicle
    • B60W30/165—Automatically following the path of a preceding lead vehicle, e.g. "electronic tow-bar"

Definitions

  • the embodiments of the present disclosure relate to the technical field of intelligent driving, and in particular to a lane keeping method, vehicle-mounted equipment, and storage medium.
  • At least one embodiment of the present disclosure provides a lane keeping method, an in-vehicle device, and a storage medium.
  • an embodiment of the present disclosure proposes a lane keeping method, including:
  • the embodiments of the present disclosure also provide a vehicle-mounted device, including: a processor and a memory; the processor is used to execute the steps of the method described in the first aspect by calling a program or instruction stored in the memory.
  • the embodiments of the present disclosure also propose a non-transitory computer-readable storage medium for storing a program or instruction, and the program or instruction causes a computer to execute the steps of the method described in the first aspect.
  • FIG. 1 is an overall architecture diagram of an intelligent driving vehicle provided by an embodiment of the present disclosure
  • Fig. 2 is a block diagram of an intelligent driving system provided by an embodiment of the present disclosure
  • Fig. 3 is a block diagram of a lane keeping 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 lane keeping method provided by an embodiment of the present disclosure.
  • Fig. 6 is a schematic diagram of a traffic jam condition provided by an embodiment of the present disclosure.
  • the traffic jam conditions are common and complex conditions.
  • 101 is the vehicle
  • 102 to 107 are surrounding vehicles
  • 108 and 109 are lane lines.
  • the embodiments of the present disclosure provide a lane keeping solution suitable for traffic congestion conditions, and improve the safety of driving under traffic congestion conditions.
  • FIG. 1 is an overall architecture diagram of an intelligent driving vehicle provided by an embodiment of the disclosure.
  • the intelligent driving vehicle includes: a sensor group, an intelligent driving system 100, a vehicle underlying execution system, and other components that can be used to drive the vehicle and control the operation of the vehicle.
  • the sensor group is used to collect the data of the external environment of the vehicle and detect the position data of the vehicle.
  • the sensor group includes, but is not limited to, at least one of a camera, a lidar, a millimeter wave radar, an ultrasonic 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 further 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, for example.
  • 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 100 is also used for environmental perception and vehicle positioning based on the data of the sensor group, path planning and decision-making based on environmental perception information and vehicle positioning information, and generating vehicle control instructions based on the planned path, thereby controlling the vehicle according to the plan Route driving.
  • the intelligent driving system 100 is also used to obtain environmental information around the vehicle; and then determine lane-changing information of vehicles around the vehicle based on the environmental information; thereby determining the vehicle’s lane-changing information based on the environmental information and lane-changing information Bear mode; based on the follow mode, plan the driving path; control the vehicle to follow the driving path.
  • the intelligent driving system 100 may be a software system, a hardware system, or a combination of software and hardware.
  • the intelligent driving system 100 is a software system that runs on an operating system
  • the on-board hardware system is a hardware system that supports the operation of the operating system.
  • the intelligent driving system 100 is also used for wireless communication with a cloud server to exchange various information.
  • the intelligent driving system 100 and the cloud server perform wireless communication through wireless communication networks (for example, including but not limited to wireless communication networks such as GPRS network, Zigbee network, Wifi network, 3G network, 4G network, 5G network, etc.).
  • the cloud server is used to coordinate the management of intelligent driving vehicles. In some embodiments, the cloud server may be used to interact with one or more intelligent driving vehicles, to coordinate and manage the scheduling of multiple intelligent driving vehicles, and so on.
  • the cloud server is a cloud server established by a vehicle service provider to provide cloud storage and cloud computing functions.
  • the vehicle file is created in the cloud server.
  • various information uploaded by the intelligent driving system 100 is stored in the vehicle file.
  • the cloud server can synchronize the driving data generated by the vehicle in real time.
  • the cloud server may be a server or a server group.
  • Server groups can be centralized or distributed. Distributed server is conducive to task allocation and optimization among multiple distributed servers, and overcomes the shortcomings of traditional centralized server resource shortage and response bottleneck.
  • the cloud server may be local or remote.
  • the cloud server can be used to charge vehicles for parking, tolls, etc. In some embodiments, the cloud server is also used to analyze the driving behavior of the driver and evaluate the safety level of the driving behavior of the driver.
  • the cloud server may be used to obtain information about the road side unit (RSU: Road Side Unit) and the intelligent driving vehicle, and may send the information to the intelligent driving vehicle.
  • the cloud server may send the detection information corresponding to the intelligent driving vehicle in the road monitoring unit to the intelligent driving vehicle according to the information of the intelligent driving vehicle.
  • the road monitoring unit may be used to collect road monitoring information.
  • the road monitoring unit may be an environmental sensor, such as a camera, a lidar, etc., or a road device, such as a V2X device, a roadside traffic light device, and the like.
  • the road monitoring unit may monitor the road conditions subordinate to the corresponding road monitoring unit, for example, the type, speed, priority level, etc. of passing vehicles. After the road monitoring unit collects the road monitoring information, the road monitoring information can be sent to the cloud server, or can be sent to the intelligent driving vehicle passing the road.
  • the bottom-level execution system of the vehicle is used to receive vehicle control instructions to control the driving of the vehicle.
  • the vehicle bottom-level execution system includes, but is not limited to: a steering system, a braking system, and a driving system.
  • the steering system, braking system, and drive system are mature systems 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. 1, 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 traveling of the vehicle.
  • the devices that control the traveling of the vehicle include, but are not limited to, a brake pedal, a steering wheel, and an accelerator pedal, for example.
  • 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 vehicle is executed by the intelligent driving system 100.
  • FIG. 2 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. 1 for controlling the driving of the vehicle.
  • the intelligent driving system 200 can be divided into multiple modules, for example, it can include: a perception module 201, a planning module 202, a control module 203, a lane keeping module 204, and other modules that can be used for intelligent driving.
  • the perception module 201 is used for environmental perception and positioning.
  • the sensing module 201 is used to obtain data such as sensor data, V2X (Vehicle to X, wireless communication for vehicles) data, and high-precision maps.
  • 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.
  • the perception module 201 is used to generate perception positioning information to realize obstacle perception, recognition of the drivable area of the camera image, and positioning of the vehicle.
  • 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, and so on.
  • Localization is a part of perception, which is the ability to determine the position of an 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.
  • Localization can also use SLAM (Simultaneous Localization And Mapping, simultaneous localization 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 location and current observation features s position.
  • V2X is the key technology of the intelligent transportation system, which enables 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, etc., to improve the safety of intelligent driving and reduce 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 intersections. The location of road signs, etc.; 2) High-precision maps also include 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 starting position of the left-turn lane; 3) The high-precision map can reach centimeters Class precision to ensure the safe driving of intelligent driving vehicles.
  • the planning module 202 is configured to perform path planning and decision-making based on the perception positioning information generated by the perception module 201.
  • the planning module 202 is configured to perform path planning and decision-making based on the perception positioning information generated by the perception module 201 in combination with at least one of V2X data, high-precision maps and other data.
  • the planning module 202 is used to plan a route and make decisions: behaviors (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.
  • the control module 203 is configured to perform path tracking and trajectory tracking based on the planning decision information generated by the planning module 202.
  • control module 203 is used to generate control instructions for the vehicle's bottom-level execution system, and issue control instructions so that the vehicle's bottom-level execution system 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 lane keeping module 204 is used to obtain environmental information around the vehicle; and then determine lane-changing information of vehicles around the vehicle based on the environmental information; thereby determining the following mode of the vehicle based on the environmental information and lane-changing information; planning based on the following mode Driving path; controlling the vehicle to drive according to the driving path.
  • the function of the lane keeping 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 200, and the lane keeping module 204 can be a software module.
  • Hardware modules or a combination of software and hardware modules 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 200, and the lane keeping module 204 can be a software module. , Hardware modules or a combination of software and hardware modules.
  • the lane keeping module 204 is a software module running on an operating system
  • the on-board hardware system is a hardware system that supports the running of the operating system.
  • FIG. 3 is a block diagram of a lane keeping module 300 provided by an embodiment of the disclosure.
  • the lane keeping module 300 may be implemented as the lane keeping module 204 or a part of the lane keeping module 204 in FIG. 2.
  • the lane keeping module 300 may include but is not limited to the following units: an acquisition unit 301, a first determination unit 302, a second determination unit 303, a planning unit 304 and a control unit 305.
  • the obtaining unit 301 is used to obtain environmental information around the vehicle.
  • the environmental information is information obtained through perception based on sensor data, and the environmental information may include, but is not limited to, at least one of the following: lane line information, information of the vehicle ahead of the own lane, vehicle information in the left lane of the own vehicle, and own vehicle Vehicle information in the right lane.
  • the own lane can be understood as the lane where the vehicle is located;
  • the left lane of the vehicle can be understood as the lane adjacent to and on the left side of the lane;
  • the right lane of the vehicle can be understood as being adjacent to the lane and located on the right of the lane.
  • Side lane is used to obtain environmental information around the vehicle.
  • the environmental information is information obtained through perception based on sensor data
  • the environmental information may include, but is not limited to, at least one of the following: lane line information, information of the vehicle ahead of the own lane, vehicle information in the left lane of the own vehicle, and own vehicle Vehicle information in the right
  • the lane line information may include, but is not limited to: location, line shape, and credibility.
  • the information of the vehicle in front of the lane may include, but is not limited to: the relative distance and relative speed of two vehicles in front of the lane (for example, 102 and 103 in FIG. 6) and the vehicle.
  • the vehicle information in the left lane of the own vehicle may include, but is not limited to: the relative distance and relative speed between the left neighboring vehicle (such as 104 in Figure 6) and the own vehicle, and the left front vehicle of the own vehicle (such as 105 in Figure 6) and the own vehicle.
  • the relative distance and relative speed of the car may include, but is not limited to: location, line shape, and credibility.
  • the information of the vehicle in front of the lane may include, but is not limited to: the relative distance and relative speed of two vehicles in front of the lane (for example, 102 and 103 in FIG. 6) and the vehicle.
  • the vehicle information in the left lane of the own vehicle may include, but is not limited to
  • the vehicle information in the right lane of the own vehicle may include, but is not limited to: the relative distance and relative speed between the adjacent vehicle on the right of the vehicle (e.g. 106 in Figure 6) and the vehicle, the vehicle ahead of the vehicle on the right (e.g. 107 in Figure 6) and the vehicle.
  • the relative distance and relative speed of the car may include, but is not limited to: the relative distance and relative speed between the adjacent vehicle on the right of the vehicle (e.g. 106 in Figure 6) and the vehicle, the vehicle ahead of the vehicle on the right (e.g. 107 in Figure 6) and the vehicle.
  • the relative distance and relative speed of the car may include, but is not limited to: the relative distance and relative speed between the adjacent vehicle on the right of the vehicle (e.g. 106 in Figure 6) and the vehicle, the vehicle ahead of the vehicle on the right (e.g. 107 in Figure 6) and the vehicle.
  • the relative distance and relative speed of the car may include, but is not limited to: the relative distance and relative speed between the
  • the two vehicles in front of the lane may be two vehicles directly in front of the lane.
  • the front right is relative to the front left and front right.
  • the front vehicle can be understood as a vehicle driving in the lane where the vehicle is located and located in front of the vehicle.
  • the first determining unit 302 is configured to determine lane-changing information of vehicles around the vehicle based on the environmental information.
  • the lane-changing information of vehicles around the own vehicle may include, but is not limited to: vehicle information that cuts out the lane from the vehicle in front of the lane, for example, the logo of the vehicle that cuts out of the lane from the own lane to the left lane of the own vehicle, and For example, the identification of the vehicle cut from the own lane to the right lane of the vehicle.
  • vehicle information is not limited to the identification, but may also be other information, such as the direction of lane change (left or right lane change) ).
  • cutting out the own lane can be understood as changing lanes from the own lane to the adjacent lane.
  • the adjacent lane can be understood as the left lane of the vehicle or the right lane of the vehicle.
  • the lane-changing information of the vehicles surrounding the own vehicle may include, but is not limited to: information of vehicles that cut into the own lane from the left lane of the own vehicle and the right lane of the own vehicle, for example, the identification of the vehicle that cuts into the own lane from the left lane of the own vehicle , Another example is the identification of the vehicle that cuts into the lane from the right lane of the vehicle. Among them, cutting into the own lane can be understood as changing lanes from the adjacent lane to the own lane.
  • the first determining unit 302 determines whether the lane line is valid, and determines lane-changing information of vehicles around the vehicle based on the determination result, wherein the validity or invalidity of the lane line can be determined by the existing method, and will not be repeated.
  • the effective lane line can be understood as: at least one lane line on the left and right sides exists and is of good quality. Invalid lane line can be understood as: both left and right lane lines are invalid, where invalid can be understood as: lane line is missing or poor quality.
  • the quality of the lane line is determined based on the lane line information, that is, based on the position, line shape, and credibility of the lane line.
  • the credibility is not lower than the preset range. If the reliability threshold is used, the quality of the lane line is determined to be good; otherwise, the quality of the lane line is determined to be poor.
  • the preset distance range, preset curvature range, and preset credibility threshold can be set based on actual needs, and this embodiment does not limit specific values.
  • the first determining unit 302 is based on the validity of the lane line and uses the lane line information to determine lane-changing information of vehicles around the vehicle. In some embodiments, the first determining unit 302 determines the vehicle information of the vehicle in front of the vehicle lane that cuts out of the vehicle lane based on the lane line information in the environment information and the information of the vehicle ahead of the vehicle lane. In some embodiments, the first determining unit 302 determines that the left lane of the own vehicle and the right lane of the own vehicle cut into the own lane based on the lane line information in the environment information, the vehicle information in the left lane of the own vehicle, and the vehicle information in the right lane of the own vehicle. Vehicle information.
  • the first determining unit 302 uses the motion information of the own vehicle to determine lane-changing information of vehicles around the own vehicle based on the invalid lane line.
  • the motion information of the vehicle may include, but is not limited to: vehicle speed, steering wheel angle, yaw rate, etc.
  • the first determining unit 302 determines the motion trajectory of the own vehicle based on the motion information of the own vehicle; furthermore, determines lane-changing information of vehicles around the own vehicle based on the boundary of the motion trajectory.
  • the boundary of the motion trajectory is a lateral boundary of the motion trajectory, wherein the lateral direction is a direction perpendicular to the lane line.
  • the first determining unit 302 determines the information of the vehicle that cuts out the own lane among the vehicles in front of the lane based on the lateral boundary of the motion trajectory and the information of the vehicle ahead of the lane. In some embodiments, the first determining unit 302 determines the vehicle that cuts into the own lane in the left lane of the own vehicle and the right lane of the own vehicle based on the lateral boundary of the motion trajectory, the vehicle information in the left lane of the own vehicle, and the vehicle information in the right lane of the own vehicle. information.
  • the second determining unit 303 is configured to determine the following mode of the own vehicle based on the environment information around the own vehicle and the lane-changing information of the surrounding vehicles of the own vehicle. In some embodiments, the second determining unit 303 determines lane-changing information of vehicles around the vehicle under traffic jam conditions, and makes decisions from multiple following modes for lane keeping based on the environmental information around the vehicle. A follow mode.
  • the follow mode may include, but is not limited to: a follow mode, a follow mode, and a degraded mode.
  • the following mode includes: the vehicle follows the lane line to maintain the lane; the following mode includes: the vehicle follows the vehicle directly in front to maintain the lane; the degraded mode includes: the vehicle does not follow the vehicle directly in front to cut out of the lane, and keep other vehicles in the same lane.
  • the stability of the vehicle in the lane In some embodiments, the stability of the vehicle when other vehicles cut into the lane is maintained.
  • the braking force is not greater than the preset braking force threshold
  • the steering wheel angle is not greater than the preset angle threshold
  • neither the application of braking force nor the rotation of the steering wheel are greater than the preset braking force threshold. It can be completed at one time to prevent the occurrence of vehicle shaking and instability caused by sudden braking and sudden steering.
  • the preset braking force threshold and the preset angle threshold can be set according to actual needs, and the specific values are not limited in this embodiment. It can be understood that the way to maintain the stability of the vehicle can also be other ways to prevent unstable situations such as vehicle shaking, sudden turning, and emergency stopping.
  • the second determining unit 303 determines that the following mode is the line-following mode based on that the lane line is valid and there is no lane change information. In some embodiments, the second determining unit 303 determines that the following mode is the following mode based on the invalid lane line and no lane change information. In some embodiments, the second determining unit 303 determines to follow the lane based on the lane change information including the vehicle information of the vehicle in front of the vehicle that cuts out of the lane and/or the information of the vehicle that cuts into the lane in the left lane of the vehicle and the right lane of the vehicle. The mode is degraded mode.
  • the planning unit 304 is configured to plan a travel path based on the following mode determined by the second determining unit 303. In some embodiments, the planning unit 304 plans the travel path based on the lane line information and the state of the lane line when the following mode is the line-following mode. The state of the lane line includes valid and invalid. In some embodiments, the planning unit 304 determines the lane center line based on the lane line information and the state of the lane line; and then plans the travel path based on the lane center line.
  • the planning unit 304 determines the center line of the lane based on the lane line information and the state of the lane line, specifically: if the lane lines on both sides (for example, 108 and 109 in FIG. 6) are valid, it is based on both sides.
  • the lane line generates the lane center line; if one side lane line is valid and the other side lane line is invalid, the lane center line is generated based on the effective side lane line and the lane width.
  • Method 1 Generate the lane center line directly based on the effective side lane line and lane width;
  • Method 2 First generate the invalid side lane line based on the effective side lane line and lane width, and then generate the lane center line from the effective side lane line and the invalid side lane line .
  • the vehicle can be smoothly controlled to keep driving in the current lane.
  • the planning unit 304 plans the travel path based on the environmental information when the following mode is the following mode. In some embodiments, the planning unit 304 plans the travel path based on the environmental information, specifically: determining the relative position of the vehicle in front of the lane and the vehicle as the end of the path; and then generating multiple path curves from the vehicle to the end of the path; thereby filtering The path curve that satisfies the condition is the driving path; where the condition is the maximum average distance from the vehicle surrounding the vehicle (for example, 102 to 107 in FIG. 6) from the path curve. In some embodiments, multiple path curves from the vehicle to the end of the path can be generated based on the traditional spline function generation method, which will not be repeated here. In this embodiment, when the lane lines on both sides are invalid, a new follow-up mode is added, which enables the vehicle to follow the preceding vehicle and keep driving in the current lane.
  • the planning unit 304 plans the travel path based on the movement information of the vehicle and the lane-changing information of vehicles around the vehicle when the following mode is the degraded mode. In some embodiments, the planning unit 304 uses the vehicle's motion information, historical planning path and first information of vehicles around the vehicle to plan the travel path based on the vehicle information of the vehicle ahead of the vehicle in the vehicle lane. Follow the preceding vehicle to cut out of the own lane; wherein, the first information includes: vehicle information of the vehicle in front of the lane that does not cut out of the own lane, vehicle information of the own vehicle in the left lane, and vehicle information of the own vehicle in the right lane.
  • the planning unit 304 plans the travel path based on the vehicle information in the left lane of the own vehicle and the right lane of the own vehicle that cuts into the own lane, using the movement information of the own vehicle, the historical planning path, and the second information of the surrounding vehicles of the own vehicle. , To prevent the planned route jump of the own vehicle caused by the change of the path end point; wherein, the second information includes: information of the vehicle ahead of the own lane, information of the adjacent vehicle on the left of the own vehicle, and information of the adjacent vehicle on the right of the own vehicle.
  • the control unit 305 is used to control the vehicle to drive according to the driving path. In some embodiments, the control unit 305 controls the vehicle to keep driving in the current lane based on the planned travel path. In some embodiments, the control unit 305 generates the lateral control instruction and the longitudinal control instruction of the vehicle based on the driving path; and then sends the lateral control instruction and the longitudinal control instruction to the vehicle chassis controller to control the vehicle to maintain the lane.
  • the vehicle chassis controller belongs to a part of the vehicle bottom-level execution system shown in FIG. 1.
  • the control unit 305 generates a lateral control instruction based on the driving path, specifically: determining the preview longitudinal distance based on the motion information of the vehicle and the road curvature; and then determining the lateral direction corresponding to the preview longitudinal distance based on the driving path Relative position; thus based on the preview longitudinal distance and the lateral relative position, the vehicle lateral control command is generated.
  • the preview longitudinal distance is the longitudinal distance of the front aiming point relative to the vehicle related to the vehicle speed and preview time coefficient. It is a key parameter in the traditional geometric vehicle lateral control method.
  • the preview longitudinal distance can also be used. The method is determined and will not be repeated.
  • the lateral control commands may include, but are not limited to: steering wheel angle commands and torque control commands.
  • the torque control command is a lateral control command sent to the steering mechanism controller for execution.
  • the control unit 305 generates a longitudinal control instruction based on the driving path, specifically: determining the acceleration and driving path of the vehicle based on the motion information of the vehicle, lane-changing information of vehicles around the vehicle, road curvature, and driving path. The speed of the vehicle in front of the lane; and based on the acceleration of the vehicle and the speed of the vehicle in front of the lane, a longitudinal control command is generated.
  • the longitudinal control command may include, but is not limited to: a shaft end torque command and a brake deceleration command. Among them, the shaft end torque command is a longitudinal control command sent to the engine for execution.
  • each unit in the lane keeping module 300 is only a logical function division, and there may be other division methods in actual implementation, such as the acquisition unit 301, the first determination unit 302, the second determination unit 303,
  • the planning unit 304 and the control unit 305 may be implemented as one unit; the acquisition unit 301, the first determination unit 302, the second determination unit 303, the planning unit 304, or the control unit 305 may also be divided into multiple sub-units.
  • each unit or subunit 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. Those skilled in the art can use different methods for each specific application to realize the described functions.
  • Fig. 4 is a schematic structural diagram of a vehicle-mounted device provided by an embodiment of the present disclosure.
  • the 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. Understandably, 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 clear description, 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 lane keeping method provided by the embodiments 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, and the processor 401 is configured to execute each lane keeping method provided by the embodiment of the present disclosure. Example steps.
  • the lane keeping 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 an integrated logic circuit of hardware in the processor 401 or instructions in the form of software.
  • the foregoing processor 401 may be a general-purpose processor, a digital signal processor (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 gates 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 lane keeping method provided by the embodiments of the present disclosure may be directly embodied as executed and completed by a hardware decoding processor, or executed and completed 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 a flowchart of a lane keeping method provided by an embodiment of the disclosure.
  • 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 lane keeping method may include but is not limited to the following steps 501 to 505:
  • the environmental information is information obtained through perception based on sensor data, and the environmental information may include, but is not limited to, at least one of the following: lane line information, information of the vehicle ahead of the own lane, vehicle information in the left lane of the own vehicle, and own vehicle Vehicle information in the right lane.
  • the own lane can be understood as the lane where the vehicle is located;
  • the left lane of the vehicle can be understood as the lane adjacent to and on the left side of the lane;
  • the right lane of the vehicle can be understood as being adjacent to the lane and located on the right of the lane.
  • Side lane is lane line information, information of the vehicle ahead of the own lane, vehicle information in the left lane of the own vehicle, and own vehicle Vehicle information in the right lane.
  • the lane line information may include, but is not limited to: location, line shape, and credibility.
  • the information of the vehicle in front of the lane may include, but is not limited to: the relative distance and relative speed of two vehicles in front of the lane (for example, 102 and 103 in FIG. 6) and the vehicle.
  • the vehicle information in the left lane of the own vehicle may include, but is not limited to: the relative distance and relative speed between the left neighboring vehicle (such as 104 in Figure 6) and the own vehicle, and the left front vehicle of the own vehicle (such as 105 in Figure 6) and the own vehicle.
  • the relative distance and relative speed of the car may include, but is not limited to: location, line shape, and credibility.
  • the information of the vehicle in front of the lane may include, but is not limited to: the relative distance and relative speed of two vehicles in front of the lane (for example, 102 and 103 in FIG. 6) and the vehicle.
  • the vehicle information in the left lane of the own vehicle may include, but is not limited to
  • the vehicle information in the right lane of the own vehicle may include, but is not limited to: the relative distance and relative speed between the adjacent vehicle on the right of the vehicle (e.g. 106 in Figure 6) and the vehicle, the vehicle ahead of the vehicle on the right (e.g. 107 in Figure 6) and the vehicle.
  • the relative distance and relative speed of the car may include, but is not limited to: the relative distance and relative speed between the adjacent vehicle on the right of the vehicle (e.g. 106 in Figure 6) and the vehicle, the vehicle ahead of the vehicle on the right (e.g. 107 in Figure 6) and the vehicle.
  • the relative distance and relative speed of the car may include, but is not limited to: the relative distance and relative speed between the adjacent vehicle on the right of the vehicle (e.g. 106 in Figure 6) and the vehicle, the vehicle ahead of the vehicle on the right (e.g. 107 in Figure 6) and the vehicle.
  • the relative distance and relative speed of the car may include, but is not limited to: the relative distance and relative speed between the
  • the two vehicles in front of the lane may be two vehicles directly in front of the lane.
  • the front right is relative to the front left and front right.
  • the front vehicle can be understood as a vehicle driving in the lane where the vehicle is located and located in front of the vehicle.
  • the lane-changing information of vehicles around the own vehicle may include, but is not limited to: vehicle information that cuts out the lane from the vehicle in front of the lane, for example, the logo of the vehicle that cuts out of the lane from the own lane to the left lane of the own vehicle, and For example, the identification of the vehicle cut from the own lane to the right lane of the vehicle.
  • vehicle information is not limited to the identification, but may also be other information, such as the direction of lane change (left or right lane change) ).
  • cutting out the own lane can be understood as changing lanes from the own lane to the adjacent lane.
  • the adjacent lane can be understood as the left lane of the vehicle or the right lane of the vehicle.
  • the lane-changing information of the vehicles surrounding the own vehicle may include, but is not limited to: information of vehicles that cut into the own lane from the left lane of the own vehicle and the right lane of the own vehicle, for example, the identification of the vehicle that cuts into the own lane from the left lane of the own vehicle , Another example is the identification of the vehicle that cuts into the lane from the right lane of the vehicle. Among them, cutting into the own lane can be understood as changing lanes from the adjacent lane to the own lane.
  • the effective lane line can be understood as: at least one lane line on the left and right sides exists and is of good quality.
  • Invalid lane line can be understood as: both left and right lane lines are invalid, where invalid can be understood as: lane line is missing or poor quality.
  • the quality of the lane line is determined based on the lane line information, that is, based on the position, line shape, and credibility of the lane line.
  • the credibility is not lower than the preset range. If the reliability threshold is used, the quality of the lane line is determined to be good; otherwise, the quality of the lane line is determined to be poor.
  • the preset distance range, preset curvature range, and preset credibility threshold can be set based on actual needs, and this embodiment does not limit specific values.
  • the lane line information is used to determine the lane-changing information of the vehicles around the vehicle. In some embodiments, based on the lane line information in the environment information and the information of the vehicle ahead of the own lane, the information of the vehicle that cuts out the own lane among the vehicles ahead of the own lane is determined. In some embodiments, based on the lane line information in the environment information, the vehicle information in the left lane of the own vehicle, and the vehicle information in the right lane of the own vehicle, the information of the vehicle that cuts into the own lane in the left lane of the own vehicle and the right lane of the own vehicle is determined.
  • the motion information of the own vehicle is used to determine the lane-changing information of vehicles around the own vehicle.
  • the motion information of the vehicle may include, but is not limited to: vehicle speed, steering wheel angle, yaw rate, etc.
  • the motion trajectory of the own vehicle is determined based on the motion information of the own vehicle; furthermore, the lane-changing information of vehicles around the own vehicle is determined based on the boundary of the motion trajectory.
  • the boundary of the motion trajectory is a lateral boundary of the motion trajectory, wherein the lateral direction is a direction perpendicular to the lane line.
  • the vehicle information of the vehicle ahead of the own lane is determined.
  • the vehicle information in the left lane of the own vehicle, and the vehicle information in the right lane of the own vehicle the information of the vehicle that cuts into the own lane in the left lane of the own vehicle and the right lane of the own vehicle is determined.
  • a follow-up mode is decided from among multiple follow-up modes for lane keeping.
  • the follow mode may include, but is not limited to: a follow mode, a follow mode, and a degraded mode.
  • the following mode includes: the vehicle follows the lane line to maintain the lane; the following mode includes: the vehicle follows the vehicle directly in front to maintain the lane; the degraded mode includes: the vehicle does not follow the vehicle directly in front to cut out of the lane, and keep other vehicles in the same lane.
  • the stability of the vehicle in the lane In some embodiments, the stability of the vehicle when other vehicles cut into the lane is maintained.
  • the braking force is not greater than the preset braking force threshold
  • the steering wheel angle is not greater than the preset angle threshold
  • neither the application of braking force nor the rotation of the steering wheel are greater than the preset braking force threshold. It can be completed at one time to prevent the occurrence of vehicle shaking and instability caused by sudden braking and sudden steering.
  • the preset braking force threshold and the preset angle threshold can be set according to actual needs, and the specific values are not limited in this embodiment. It can be understood that the way to maintain the stability of the vehicle can also be other ways to prevent unstable situations such as vehicle shaking, sudden turning, and emergency stopping.
  • the following mode is the line-following mode. In some embodiments, based on the invalid lane line and no lane change information, it is determined that the following mode is the following mode. In some embodiments, it is determined that the following mode is a degraded mode based on the lane-changing information including the vehicle information of the vehicle in front of the lane that cuts out of the lane and/or the information of the vehicle that cuts into the lane in the left lane of the vehicle and the right lane of the vehicle.
  • Plan a driving route based on the determined following mode.
  • the travel path is planned based on the lane line information and the state of the lane line.
  • the state of the lane line includes valid and invalid.
  • the lane center line is determined based on the lane line information and the state of the lane line; and then the travel path is planned based on the lane center line.
  • the center line of the lane is determined based on the lane line information and the state of the lane line, specifically: if the lane lines on both sides (such as 108 and 109 in Figure 6) are valid, then generate based on the lane lines on both sides Lane center line; if one side lane line is valid and the other side lane line is invalid, the lane center line is generated based on the effective side lane line and lane width. In some embodiments, there are two ways to generate the lane centerline based on the effective side lane line and the lane width.
  • Method 1 Generate the lane center line directly based on the effective side lane line and lane width;
  • Method 2 First generate the invalid side lane line based on the effective side lane line and lane width, and then generate the lane center line from the effective side lane line and the invalid side lane line .
  • the vehicle can be smoothly controlled to keep driving in the current lane.
  • the driving route is planned based on the environmental information.
  • planning the travel path is specifically: determining the relative position of the vehicle in front of the lane and the vehicle as the end of the path; then generating multiple path curves from the vehicle to the end of the path; thereby filtering those that meet the conditions
  • the path curve is the driving path; wherein, the condition is the maximum average distance between the vehicles around the vehicle (for example, 102 to 107 in FIG. 6) from the path curve.
  • multiple path curves from the vehicle to the end of the path can be generated based on the traditional spline function generation method, which will not be repeated here.
  • a new follow-up mode is added, which enables the vehicle to follow the preceding vehicle and keep driving in the current lane.
  • the travel path is planned based on the movement information of the vehicle and the lane-changing information of vehicles around the vehicle.
  • the motion information of the vehicle, the historical planning path and the first information of the vehicles surrounding the vehicle are used to plan the driving path to prevent the vehicle from following the vehicle in front Cut out the own lane; wherein, the first information includes: vehicle information in the vehicle ahead of the lane that does not cut out the lane, vehicle information in the left lane of the vehicle, and vehicle information in the right lane of the vehicle.
  • the movement information of the own vehicle, the historical planning path and the second information of the surrounding vehicles of the own vehicle are used to plan the driving path to prevent The planned path jump of the own vehicle caused by the change of the path end point; wherein the second information includes: information of the vehicle ahead of the own lane, information of the adjacent vehicle on the left of the own vehicle, and information of the adjacent vehicle on the right of the own vehicle.
  • the vehicle chassis controller belongs to a part of the vehicle bottom-level execution system shown in FIG. 1.
  • generating the lateral control instruction is specifically: determining the preview longitudinal distance based on the motion information of the vehicle and the road curvature; and then determining the horizontal relative position corresponding to the preview longitudinal distance based on the driving path;
  • the vehicle lateral control command is generated.
  • the preview longitudinal distance is the longitudinal distance of the front aiming point relative to the vehicle related to the vehicle speed and preview time coefficient. It is a key parameter in the traditional geometric vehicle lateral control method.
  • the preview longitudinal distance can also be used. The method is determined and will not be repeated.
  • the lateral control commands may include, but are not limited to: steering wheel angle commands and torque control commands.
  • the torque control command is a lateral control command sent to the steering mechanism controller for execution.
  • the longitudinal control instruction is generated based on the driving path, specifically: determining the acceleration of the own vehicle and the vehicle ahead of the own lane based on the motion information of the own vehicle, the lane-changing information of the surrounding vehicles, the road curvature and the traveling path Based on the acceleration of the vehicle and the speed of the vehicle in front of the lane, a longitudinal control command is generated.
  • the longitudinal control command may include, but is not limited to: a shaft end torque command and a brake deceleration command. Among them, the shaft end torque command is a longitudinal control command sent to the engine for execution.
  • the embodiment of the present disclosure also proposes a non-transitory computer-readable storage medium, which stores a program or instruction, and the program or instruction causes a computer to execute the steps of the various embodiments of the lane keeping method, In order to avoid repetitive descriptions, I will not repeat them here.
  • a follow-up mode is determined from multiple follow-up modes for lane keeping. Thereby planning the driving path and controlling the vehicle to keep driving in the current lane, which has industrial applicability.

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Abstract

一种车道保持方法、车载设备和存储介质，该方法包括：获取本车周围的环境信息（501）；基于环境信息，确定本车周围车辆的换道信息（502）；基于环境信息和换道信息，确定本车的跟随模式（503）；基于跟随模式，规划行驶路径（504）；控制本车按照行驶路径行驶（505）。在交通拥堵工况下，该方法通过确定本车周围车辆的换道信息，并基于本车周围的环境信息，从多种用于车道保持的跟随模式中决策一种跟随模式，从而规划行驶路径并控制车辆保持在当前车道内行驶。

Description

一种车道保持方法、车载设备和存储介质 技术领域
本公开实施例涉及智能驾驶技术领域，具体涉及一种车道保持方法、车载设备和存储介质。
背景技术
随着智能驾驶技术的发展，提高了驾驶员和乘客的驾乘体验。交通拥堵工况属于常见且复杂的工况，为此，亟需提供一种适用于交通拥堵工况下的车道保持方案，提高在交通拥堵工况下行车安全性。
上述对问题的发现过程的描述，仅用于辅助理解本公开的技术方案，并不代表承认上述内容是现有技术。
发明内容
为了解决现有技术存在的至少一个问题，本公开的至少一个实施例提供了一种车道保持方法、车载设备和存储介质。
第一方面，本公开实施例提出一种车道保持方法，包括：
获取本车周围的环境信息；
基于所述环境信息，确定本车周围车辆的换道信息；
基于所述环境信息和所述换道信息，确定本车的跟随模式；
基于所述跟随模式，规划行驶路径；
控制本车按照所述行驶路径行驶。
第二方面，本公开实施例还提出一种车载设备，包括：处理器和存储器；所述处理器通过调用所述存储器存储的程序或指令，用于执行如第一方面所述方法的步骤。
第三方面，本公开实施例还提出一种非暂态计算机可读存储介质，用于存储程序或指令，所述程序或指令使计算机执行如第一方面所述方法的步骤。
可见，本公开的至少一个实施例中，在交通拥堵工况下，通过确定本车周围车辆的换道信息，并基于本车周围的环境信息，从多种用于车道保持的跟随模式中决策一种跟随模式，从而规划行驶路径并控制车辆保持在当前车道内行驶。
附图说明
为了更清楚地说明本公开实施例的技术方案，下面将对实施例或现有技术描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本公开的一些实施例，对于本领域普通技术人员来讲，还可以根据这些附图获得其他的附图。
图1是本公开实施例提供的一种智能驾驶车辆的整体架构图；
图2是本公开实施例提供的一种智能驾驶***的框图；
图3是本公开实施例提供的一种车道保持模块的框图；
图4是本公开实施例提供的一种车载设备的框图；
图5是本公开实施例提供的一种车道保持方法流程图；
图6是本公开实施例提供的一种交通拥堵工况示意图。
具体实施方式
为了能够更清楚地理解本公开的上述目的、特征和优点，下面结合附图和实施例对本公开作进一步的详细说明。可以理解的是，所描述的实施例是本公开的一部分实施例，而不是全部的实施例。此处所描述的具体实施例仅仅用于解释本公开，而非对本公开的限定。基于所描述的本公开的实施例，本领域普通技术人员所获得的所有其他实施例，都属于本公开保护的范围。
需要说明的是，在本文中，诸如“第一”和“第二”等之类的关系术语仅仅用来将一个实体或者操作与另一个实体或操作区分开来，而不一定要求或者暗示这些实体或操作之间存在任何这种实际的关系或者顺序。
针对交通拥堵工况属于常见且复杂的工况，如图6所示，图6中，101为本车，102至107为本车周围车辆，108和109为车道线。本公开实施例提供一种适用于交通拥堵工况下的车道保持方案，提高在交通拥堵工况下行车安全性。
在一些实施例中，本公开实施例提供的车道保持方案，可应用于智能驾驶车辆。图1为本公开实施例提供的一种智能驾驶车辆的整体架构图。
如图1所示，智能驾驶车辆包括：传感器组、智能驾驶***100、车辆底层执行***以及其他可用于驱动车辆和控制车辆运行的部件。
传感器组，用于采集车辆外界环境的数据和探测车辆的位置数据。传感器组例如包括但不限于摄像头、激光雷达、毫米波雷达、超声波雷达、GPS(Global Positioning System，全球定位***)和IMU(Inertial Measurement Unit，惯性测量单元)中的至少一个。
在一些实施例中，传感器组，还用于采集车辆的动力学数据，传感器组例如还包括但不限于车轮转速传感器、速度传感器、加速度传感器、方向盘转角传感器、前轮转角传感器中的至少一个。
智能驾驶***100，用于获取传感器组的数据，传感器组中所有传感器在智能驾驶车辆行驶过程中都以较高的频率传送数据。
智能驾驶***100，还用于基于传感器组的数据进行环境感知和车辆定位，并基于环境感知信息和车辆定位信息进行路径规划和决策，以及基于规划的路径生成车辆控制指令，从而控制车辆按照规 划路径行驶。
在一些实施例中，智能驾驶***100，还用于获取本车周围的环境信息；进而基于环境信息，确定本车周围车辆的换道信息；从而基于环境信息和换道信息，确定本车的跟随模式；基于跟随模式，规划行驶路径；控制本车按照所述行驶路径行驶。
在一些实施例中，智能驾驶***100可以为软件***、硬件***或者软硬件结合的***。例如，智能驾驶***100是运行在操作***上的软件***，车载硬件***是支持操作***运行的硬件***。
在一些实施例中，智能驾驶***100，还用于与云端服务器无线通信，交互各种信息。在一些实施例中，智能驾驶***100与云端服务器通过无线通讯网络(例如包括但不限于GPRS网络、Zigbee网络、Wifi网络、3G网络、4G网络、5G网络等无线通讯网络)进行无线通信。
在一些实施例中，云端服务器用于统筹协调管理智能驾驶车辆。在一些实施例中，云端服务器可以用于与一个或多个智能驾驶车辆进行交互，统筹协调管理多个智能驾驶车辆的调度等。
在一些实施例中，云端服务器是由车辆服务商所建立的云端服务器，提供云存储和云计算的功能。在一些实施例中，云端服务器中建立车辆端档案。在一些实施例中，车辆端档案中储存智能驾驶***100上传的各种信息。在一些实施例中，云端服务器可以实时同步车辆端产生的驾驶数据。
在一些实施例中，云端服务器可以是一个服务器，也可以是一个服务器群组。服务器群组可以是集中式的，也可以是分布式的。分布式服务器，有利于任务在多个分布式服务器进行分配与优化，克服传统集中式服务器资源紧张与响应瓶颈的缺陷。在一些实施例中，云端服务器可以是本地的或远程的。
在一些实施例中，云端服务器可用于对车辆端进行停车收费、过路收费等。在一些实施例中，云端服务器还用于分析驾驶员的驾驶行为，并且对驾驶员的驾驶行为进行安全等级评估。
在一些实施例中，云端服务器可用于获取道路监测单元(RSU：Road Side Unit)和智能驾驶车辆的信息，以及可以发送信息至智能驾驶车辆。在一些实施例中，云端服务器可以根据智能驾驶车辆的信息将道路监测单元中的与智能驾驶车辆相对应的检测信息发送给智能驾驶车辆。
在一些实施例中，道路监测单元可以用于收集道路监测信息。在一些实施例中，道路监测单元可以是环境感知传感器，例如，摄像头、激光雷达等，也可以是道路设备，例如V2X设备，路边红绿灯装置等。在一些实施例中，道路监测单元可以监控隶属于相应道路监测单元的道路情况，例如，通过车辆的类型、速度、优先级别等。道路监测单元在收集到道路监测信息后，可将所述道路监测信息发送给云端服务器，也可以发送给通过道路的智能驾驶车辆。
车辆底层执行***，用于接收车辆控制指令，实现对车辆行驶的控制。在一些实施例中，车辆底层执行***包括但不限于：转向***、制动***和驱动***。转向***、制动***和驱动***属于车辆领域成熟***，在此不再赘述。
在一些实施例中，智能驾驶车辆还可包括图1中未示出的车辆CAN总线，车辆CAN总线连接车辆底层执行***。智能驾驶***100与车辆底层执行***之间的信息交互通过车辆CAN总线进行传递。
在一些实施例中，智能驾驶车辆既可以通过驾驶员又可以通过智能驾驶***100控制车辆行驶。在人工驾驶模式下，驾驶员通过操作控制车辆行驶的装置驾驶车辆，控制车辆行驶的装置例如包括但不限于制动踏板、方向盘和油门踏板等。控制车辆行驶的装置可直接操作车辆底层执行***控制车辆行驶。
在一些实施例中，智能驾驶车辆也可以为无人车，车辆的驾驶控制由智能驾驶***100来执行。
图2为本公开实施例提供的一种智能驾驶***200的框图。在一些实施例中，智能驾驶***200可以实现为图1中的智能驾驶***100或者智能驾驶***100的一部分，用于控制车辆行驶。
如图2所示，智能驾驶***200可划分为多个模块，例如可包括：感知模块201、规划模块202、控制模块203、车道保持模块204以及其他一些可用于智能驾驶的模块。
感知模块201用于进行环境感知与定位。在一些实施例中，感知模块201用于获取传感器数据、V2X(Vehicle to X，车用无线通信)数据、高精度地图等数据。在一些实施例中，感知模块201用于基于获取的传感器数据、V2X(Vehicle to X，车用无线通信)数据、高精度地图等数据中的至少一种，进行环境感知与定位。
在一些实施例中，感知模块201用于生成感知定位信息，实现对障碍物感知、摄像头图像的可行驶区域识别以及车辆的定位等。
环境感知(Environmental Perception)可以理解为对于环境的场景理解能力，例如障碍物的位置，道路标志/标记的检测，行人/车辆的检测等数据的语义分类。在一些实施例中，环境感知可采用融合摄像头、激光雷达、毫米波雷达等多种传感器的数据进行环境感知。
定位(Localization)属于感知的一部分，是确定智能驾驶车辆相对于环境的位置的能力。定位可采用：GPS定位，GPS的定位精度在数十米到厘米级别，定位精度高；定位还可采用融合GPS和惯性导航***(Inertial Navigation System)的定位方法。定位还可采用SLAM(Simultaneous Localization And Mapping，同步定位与地图构建)，SLAM的目标即构建地图的同时使用该地图进行定位，SLAM通过利用已经观测到的环境特征确定当前车辆的位置以及当前观测特征的位置。
V2X是智能交通运输***的关键技术，使得车与车、车与基站、基站与基站之间能够通信，从而获得实时路况、道路信息、行人信息等一系列交通信息，提高智能驾驶安全性、减少拥堵、提高交通效率、提供车载娱乐信息等。
高精度地图是智能驾驶领域中使用的地理地图，与传统地图相比，不同之处在于：1)高精度地图包括大量的驾驶辅助信息，例如依托道路网的精确三维表征：包括交叉路口局和路标位置等；2) 高精度地图还包括大量的语义信息，例如报告交通灯上不同颜色的含义，又例如指示道路的速度限制，以及左转车道开始的位置；3)高精度地图能达到厘米级的精度，确保智能驾驶车辆的安全行驶。
规划模块202用于基于感知模块201生成的感知定位信息，进行路径规划和决策。
在一些实施例中，规划模块202用于基于感知模块201生成的感知定位信息，并结合V2X数据、高精度地图等数据中的至少一种，进行路径规划和决策。
在一些实施例中，规划模块202用于规划路径，决策：行为(例如包括但不限于跟车、超车、停车、绕行等)、车辆航向、车辆速度、车辆的期望加速度、期望的方向盘转角等，生成规划决策信息。
控制模块203用于基于规划模块202生成的规划决策信息，进行路径跟踪和轨迹跟踪。
在一些实施例中，控制模块203用于生成车辆底层执行***的控制指令，并下发控制指令，以使车辆底层执行***控制车辆按照期望路径行驶，例如通过控制方向盘、刹车以及油门对车辆进行横向和纵向控制。
在一些实施例中，控制模块203还用于基于路径跟踪算法计算前轮转角。
在一些实施例中，路径跟踪过程中的期望路径曲线与时间参数无关，跟踪控制时，可以假设智能驾驶车辆以当前速度匀速前进，以一定的代价规则使行驶路径趋近于期望路径；而轨迹跟踪时，期望路径曲线与时间和空间均相关，并要求智能驾驶车辆在规定的时间内到达某一预设好的参考路径点。
路径跟踪不同于轨迹跟踪，不受制于时间约束，只需要在一定误差范围内跟踪期望路径。
车道保持模块204用于获取本车周围的环境信息；进而基于环境信息，确定本车周围车辆的换道信息；从而基于环境信息和换道信息，确定本车的跟随模式；基于跟随模式，规划行驶路径；控制本车按照所述行驶路径行驶。
在一些实施例中，车道保持模块204的功能可集成到感知模块201、规划模块202或控制模块203中，也可配置为与智能驾驶***200相独立的模块，车道保持模块204可以为软件模块、硬件模块或者软硬件结合的模块。例如，车道保持模块204是运行在操作***上的软件模块，车载硬件***是支持操作***运行的硬件***。
图3为本公开实施例提供的一种车道保持模块300的框图。在一些实施例中，车道保持模块300可以实现为图2中的车道保持模块204或者车道保持模块204的一部分。
如图3所示，车道保持模块300可包括但不限于以下单元：获取单元301、第一确定单元302、第二确定单元303、规划单元304和控制单元305。
获取单元301，用于获取车辆周围的环境信息。在一些实施例中，环境信息为基于传感器数据进行感知得到的信息，且环境信息可以包括但不限于以下至少一个：车道线信息、本车道前方车辆信息、本车左车道的车辆信息和本车右车道的车辆信息。其中，本车道可以理解为本车所在车道；本车左车道可以理解为与本车道相邻且位于本车道左侧的车道；本车右车道可以理解为与本车道相邻且位于本 车道右侧的车道。
在一些实施例中，车道线信息可包括但不限于：位置、线形和可信度。本车道前方车辆信息可包括但不限于：本车道前方两辆车辆(例如图6中的102和103)与本车的相对距离和相对速度。本车左车道的车辆信息可包括但不限于：本车左邻车(例如图6中的104)与本车的相对距离和相对速度、本车左前车(例如图6中的105)与本车的相对距离和相对速度。本车右车道的车辆信息可包括但不限于：本车右邻车(例如图6中的106)与本车的相对距离和相对速度、本车右前车(例如图6中的107)与本车的相对距离和相对速度。在一些实施例中，车道线信息、本车道前方车辆信息、本车左车道的车辆信息和本车右车道的车辆信息均基于传感器数据进行感知得到，具体方式可沿用现有方式，不再赘述。
在一些实施例中，本车道前方两辆车辆可以为本车道正前方两辆车辆。正前方是相对于左前和右前而言，正前方车辆可以理解为行驶在本车所在车道且位于本车前方的车辆。
第一确定单元302，用于基于环境信息，确定本车周围车辆的换道信息。在一些实施例中，本车周围车辆的换道信息可包括但不限于：本车道前方车辆中切出本车道的车辆信息，例如从本车道向本车左车道切出的车辆的标识，又例如从本车道向本车右车道切出的车辆的标识，本领域技术人员可以理解，车辆信息不限于标识，还可以是其他的信息，例如换道方向(向左换道还是向右换道)。其中，切出本车道可以理解为由本车道向相邻车道换道。其中，相邻车道可以理解为本车左车道或本车右车道。
在一些实施例中，本车周围车辆的换道信息可包括但不限于：本车左车道和本车右车道中切入本车道的车辆信息，例如从本车左车道切入本车道的车辆的标识，又例如从本车右车道切入本车道的车辆的标识。其中，切入本车道可以理解为由相邻车道向本车道换道。
在一些实施例中，第一确定单元302判断车道线是否有效，并基于判断结果确定本车周围车辆的换道信息，其中，车道线有效或无效可沿用现有方式判断，不再赘述。在一些实施例中，车道线有效可以理解为：左右两侧车道线至少有一条存在且质量较好。车道线无效可以理解为：左右两侧车道线均无效，其中，无效可以理解为：车道线丢失或质量较差。在一些实施例中，车道线的质量基于车道线信息确定，即基于车道线的位置、线形和可信度确定。在一些实施例中，若车道线的位置与本车之间的相对距离处于预设距离范围、车道线的线形为直线或曲率处于预设曲率范围的曲线、可信度不低于预设可信度阈值，则确定车道线的质量较好；否则确定车道线的质量较差。其中，预设距离范围、预设曲率范围和预设可信度阈值可基于实际需要进行设置，本实施例不限定具体取值。
在一些实施例中，第一确定单元302基于车道线有效，利用车道线信息确定本车周围车辆的换道信息。在一些实施例中，第一确定单元302基于环境信息中的车道线信息和本车道前方车辆信息，确定本车道前方车辆中切出本车道的车辆信息。在一些实施例中，第一确定单元302基于环境信息中的 车道线信息、本车左车道的车辆信息和本车右车道的车辆信息，确定本车左车道和本车右车道中切入本车道的车辆信息。
在一些实施例中，第一确定单元302基于车道线无效，利用本车的运动信息确定本车周围车辆的换道信息。其中，本车的运动信息可包括但不限于：本车车速、方向盘转角、偏航角速度等。在一些实施例中，第一确定单元302基于本车的运动信息，确定本车的运动轨迹；进而基于运动轨迹的边界，确定本车周围车辆的换道信息。在一些实施例中，运动轨迹的边界为运动轨迹的横向边界，其中，横向为与车道线垂直的方向。进而第一确定单元302基于运动轨迹的横向边界和本车道前方车辆信息，确定本车道前方车辆中切出本车道的车辆信息。在一些实施例中，第一确定单元302基于运动轨迹的横向边界、本车左车道的车辆信息和本车右车道的车辆信息，确定本车左车道和本车右车道中切入本车道的车辆信息。
第二确定单元303，用于基于本车周围的环境信息和本车周围车辆的换道信息，确定本车的跟随模式。在一些实施例中，第二确定单元303在交通拥堵工况下，通过确定本车周围车辆的换道信息，并基于本车周围的环境信息，从多种用于车道保持的跟随模式中决策一种跟随模式。
在一些实施例中，跟随模式可包括但不限于：跟线模式、跟车模式和降级模式。其中，跟线模式包括：本车跟随车道线保持车道；跟车模式包括：本车跟随正前方车辆保持车道；降级模式包括：本车不跟随正前方车辆切出本车道，保持其他车辆切入本车道时的本车稳定性。在一些实施例中，保持其他车辆切入本车道时的本车稳定性例如：制动力不大于预设制动力阈值，方向盘转角不大于预设角度阈值，且制动力的施加、方向盘的转动均非一次性完成，防止急刹、急转向等造成车辆晃动、不稳定的情况发生。其中，预设制动力阈值和预设角度阈值可根据实际需要进行设置，本实施例不限定具体取值。可以理解，保持本车稳定性的方式还可以为其他防止车辆晃动、急转、急停等不稳定情况发生的方式。
在一些实施例中，第二确定单元303基于车道线有效，且没有换道信息，确定跟随模式为跟线模式。在一些实施例中，第二确定单元303基于车道线无效，且没有换道信息，确定跟随模式为跟车模式。在一些实施例中，第二确定单元303基于换道信息包括本车道前方车辆中切出本车道的车辆信息和/或本车左车道及本车右车道中切入本车道的车辆信息，确定跟随模式为降级模式。
规划单元304，用于基于第二确定单元303确定的跟随模式，规划行驶路径。在一些实施例中，规划单元304在跟随模式为跟线模式时，基于车道线信息和车道线的状态，规划行驶路径。其中车道线的状态包括有效和无效两种。在一些实施例中，规划单元304基于车道线信息和车道线的状态，确定车道中心线；进而基于车道中心线规划行驶路径。
在一些实施例中，规划单元304基于车道线信息和车道线的状态，确定车道的中心线，具体为：若两侧车道线(例如图6中的108和109)均有效，则基于两侧车道线生成车道中心线；若一侧车道 线有效且另一侧车道线无效，则基于有效侧车道线和车道宽度生成车道中心线。在一些实施例中，基于有效侧车道线和车道宽度生成车道中心线有两种方式。方式一：基于有效侧车道线和车道宽度直接生成车道中心线；方式二：基于有效侧车道线和车道宽度先生成无效侧车道线，再由有效侧车道线和无效侧车道线生成车道中心线。本实施例中，仅单侧车道线有效时，也能平稳控制本车保持在当前车道内行驶。
在一些实施例中，规划单元304在跟随模式为跟车模式时，基于环境信息，规划行驶路径。在一些实施例中，规划单元304基于环境信息，规划行驶路径，具体为：确定本车道前方车辆与本车的相对位置为路径终点；进而生成本车至路径终点的多条路径曲线；从而筛选满足条件的路径曲线为行驶路径；其中，所述条件为本车周围车辆(例如图6中的102至107)距离路径曲线的平均距离最大。在一些实施例中，可基于传统样条函数的生成方法生成本车至路径终点的多条路径曲线，不再赘述。本实施例中，在两侧车道线均无效时，新增跟车模式，能够使本车跟随前方车辆保持在当前车道内行驶。
在一些实施例中，规划单元304在跟随模式为降级模式时，基于本车的运动信息和本车周围车辆的换道信息，规划行驶路径。在一些实施例中，规划单元304基于本车道前方车辆中切出本车道的车辆信息，利用本车的运动信息、历史规划路径和本车周围车辆的第一信息，规划行驶路径，防止本车跟随前方车辆切出本车道；其中，所述第一信息包括：本车道前方车辆中不切出本车道的车辆信息、本车左车道的车辆信息和本车右车道的车辆信息。在一些实施例中，规划单元304基于本车左车道和本车右车道中切入本车道的车辆信息，利用本车的运动信息、历史规划路径和本车周围车辆的第二信息，规划行驶路径，防止由于路径终点变化导致的本车规划路径跳变；其中，所述第二信息包括：本车道前方车辆信息、本车左邻车的信息和本车右邻车的信息。
控制单元305，用于控制本车按照行驶路径行驶。在一些实施例中，控制单元305基于规划的行驶路径，控制本车保持在当前车道内行驶。在一些实施例中，控制单元305基于行驶路径，生成车辆的横向控制指令和纵向控制指令；进而将横向控制指令和纵向控制指令发送至车辆底盘控制器，控制车辆保持车道。其中，车辆底盘控制器属于图1所示的车辆底层执行***中的一部分。
在一些实施例中，控制单元305基于行驶路径，生成横向控制指令，具体为：基于本车的运动信息和道路曲率，确定预瞄纵向距离；进而基于行驶路径，确定预瞄纵向距离对应的横向相对位置；从而基于预瞄纵向距离和横向相对位置，生成车辆横向控制指令。其中，预瞄纵向距离为与本车车速和预瞄时间系数相关的前方瞄点相对本车的纵向距离，属于传统基于几何车辆横向控制方法中的关键参数，预瞄纵向距离也可采用现有方式进行确定，不再赘述。在一些实施例中，横向控制指令可包括但不限于：方向盘转角指令、扭矩控制指令。其中，扭矩控制指令是发送给转向机构控制器去执行的横向控制指令。
在一些实施例中，控制单元305基于行驶路径，生成纵向控制指令，具体为：基于本车的运动信息、本车周围车辆的换道信息、道路曲率和行驶路径，确定本车的加速度和本车道前方车辆的速度；进而基于本车的加速度和本车道前方车辆的速度，生成纵向控制指令。在一些实施例中，纵向控制指令可包括但不限于：轴端扭矩指令、制动减速度指令。其中，轴端扭矩指令是发送给发动机去执行的纵向控制指令。
在一些实施例中，车道保持模块300中各单元的划分仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如获取单元301、第一确定单元302、第二确定单元303、规划单元304和控制单元305可以实现为一个单元；获取单元301、第一确定单元302、第二确定单元303、规划单元304或控制单元305也可以划分为多个子单元。可以理解的是，各个单元或子单元能够以电子硬件、或者计算机软件和电子硬件的结合来实现。这些功能究竟以硬件还是软件方式来执行，取决于技术方案的特定应用和设计约束条件。本领域技术人员可以对每个特定的应用来使用不同方法来实现所描述的功能。
图4是本公开实施例提供的一种车载设备的结构示意图。车载设备可支持智能驾驶***的运行。
如图4所示，车载设备包括：至少一个处理器401、至少一个存储器402和至少一个通信接口403。车载设备中的各个组件通过总线***404耦合在一起。通信接口403，用于与外部设备之间的信息传输。可理解地，总线***404用于实现这些组件之间的连接通信。总线***404除包括数据总线之外，还包括电源总线、控制总线和状态信号总线。但为了清楚说明起见，在图4中将各种总线都标为总线***404。
可以理解，本实施例中的存储器402可以是易失性存储器或非易失性存储器，或可包括易失性和非易失性存储器两者。
在一些实施方式中，存储器402存储了如下的元素，可执行单元或者数据结构，或者他们的子集，或者他们的扩展集：操作***和应用程序。
其中，操作***，包含各种***程序，例如框架层、核心库层、驱动层等，用于实现各种基础业务以及处理基于硬件的任务。应用程序，包含各种应用程序，例如媒体播放器(Media Player)、浏览器(Browser)等，用于实现各种应用业务。实现本公开实施例提供的车道保持方法的程序可以包含在应用程序中。
在本公开实施例中，处理器401通过调用存储器402存储的程序或指令，具体的，可以是应用程序中存储的程序或指令，处理器401用于执行本公开实施例提供的车道保持方法各实施例的步骤。
本公开实施例提供的车道保持方法可以应用于处理器401中，或者由处理器401实现。处理器401可以是一种集成电路芯片，具有信号的处理能力。在实现过程中，上述方法的各步骤可以通过处理器401中的硬件的集成逻辑电路或者软件形式的指令完成。上述的处理器401可以是通用处理器、 数字信号处理器(Digital Signal Processor，DSP)、专用集成电路(Application Specific Integrated Circuit，ASIC)、现成可编程门阵列(Field Programmable Gate Array，FPGA)或者其他可编程逻辑器件、分立门或者晶体管逻辑器件、分立硬件组件。通用处理器可以是微处理器或者该处理器也可以是任何常规的处理器等。
本公开实施例提供的车道保持方法的步骤可以直接体现为硬件译码处理器执行完成，或者用译码处理器中的硬件及软件单元组合执行完成。软件单元可以位于随机存储器，闪存、只读存储器，可编程只读存储器或者电可擦写可编程存储器、寄存器等本领域成熟的存储介质中。该存储介质位于存储器402，处理器401读取存储器402中的信息，结合其硬件完成方法的步骤。
图5为本公开实施例提供的一种车道保持方法流程图。该方法的执行主体为车载设备，在一些实施例中，该方法的执行主体为车载设备所支持的智能驾驶***。
如图5所示，车道保持方法可包括但不限于以下步骤501至505：
501、获取车辆周围的环境信息。在一些实施例中，环境信息为基于传感器数据进行感知得到的信息，且环境信息可以包括但不限于以下至少一个：车道线信息、本车道前方车辆信息、本车左车道的车辆信息和本车右车道的车辆信息。其中，本车道可以理解为本车所在车道；本车左车道可以理解为与本车道相邻且位于本车道左侧的车道；本车右车道可以理解为与本车道相邻且位于本车道右侧的车道。
在一些实施例中，车道线信息可包括但不限于：位置、线形和可信度。本车道前方车辆信息可包括但不限于：本车道前方两辆车辆(例如图6中的102和103)与本车的相对距离和相对速度。本车左车道的车辆信息可包括但不限于：本车左邻车(例如图6中的104)与本车的相对距离和相对速度、本车左前车(例如图6中的105)与本车的相对距离和相对速度。本车右车道的车辆信息可包括但不限于：本车右邻车(例如图6中的106)与本车的相对距离和相对速度、本车右前车(例如图6中的107)与本车的相对距离和相对速度。在一些实施例中，车道线信息、本车道前方车辆信息、本车左车道的车辆信息和本车右车道的车辆信息均基于传感器数据进行感知得到，具体方式可沿用现有方式，不再赘述。
在一些实施例中，本车道前方两辆车辆可以为本车道正前方两辆车辆。正前方是相对于左前和右前而言，正前方车辆可以理解为行驶在本车所在车道且位于本车前方的车辆。
502、基于环境信息，确定本车周围车辆的换道信息。在一些实施例中，本车周围车辆的换道信息可包括但不限于：本车道前方车辆中切出本车道的车辆信息，例如从本车道向本车左车道切出的车辆的标识，又例如从本车道向本车右车道切出的车辆的标识，本领域技术人员可以理解，车辆信息不限于标识，还可以是其他的信息，例如换道方向(向左换道还是向右换道)。其中，切出本车道可以理解为由本车道向相邻车道换道。其中，相邻车道可以理解为本车左车道或本车右车道。
在一些实施例中，本车周围车辆的换道信息可包括但不限于：本车左车道和本车右车道中切入本车道的车辆信息，例如从本车左车道切入本车道的车辆的标识，又例如从本车右车道切入本车道的车辆的标识。其中，切入本车道可以理解为由相邻车道向本车道换道。
在一些实施例中，判断车道线是否有效，并基于判断结果确定本车周围车辆的换道信息，其中，车道线有效或无效可沿用现有方式判断，不再赘述。在一些实施例中，车道线有效可以理解为：左右两侧车道线至少有一条存在且质量较好。车道线无效可以理解为：左右两侧车道线均无效，其中，无效可以理解为：车道线丢失或质量较差。在一些实施例中，车道线的质量基于车道线信息确定，即基于车道线的位置、线形和可信度确定。在一些实施例中，若车道线的位置与本车之间的相对距离处于预设距离范围、车道线的线形为直线或曲率处于预设曲率范围的曲线、可信度不低于预设可信度阈值，则确定车道线的质量较好；否则确定车道线的质量较差。其中，预设距离范围、预设曲率范围和预设可信度阈值可基于实际需要进行设置，本实施例不限定具体取值。
在一些实施例中，基于车道线有效，利用车道线信息确定本车周围车辆的换道信息。在一些实施例中，基于环境信息中的车道线信息和本车道前方车辆信息，确定本车道前方车辆中切出本车道的车辆信息。在一些实施例中，基于环境信息中的车道线信息、本车左车道的车辆信息和本车右车道的车辆信息，确定本车左车道和本车右车道中切入本车道的车辆信息。
在一些实施例中，基于车道线无效，利用本车的运动信息确定本车周围车辆的换道信息。其中，本车的运动信息可包括但不限于：本车车速、方向盘转角、偏航角速度等。在一些实施例中，基于本车的运动信息，确定本车的运动轨迹；进而基于运动轨迹的边界，确定本车周围车辆的换道信息。在一些实施例中，运动轨迹的边界为运动轨迹的横向边界，其中，横向为与车道线垂直的方向。进而基于运动轨迹的横向边界和本车道前方车辆信息，确定本车道前方车辆中切出本车道的车辆信息。在一些实施例中，基于运动轨迹的横向边界、本车左车道的车辆信息和本车右车道的车辆信息，确定本车左车道和本车右车道中切入本车道的车辆信息。
503、基于本车周围的环境信息和本车周围车辆的换道信息，确定本车的跟随模式。在一些实施例中，在交通拥堵工况下，通过确定本车周围车辆的换道信息，并基于本车周围的环境信息，从多种用于车道保持的跟随模式中决策一种跟随模式。
在一些实施例中，跟随模式可包括但不限于：跟线模式、跟车模式和降级模式。其中，跟线模式包括：本车跟随车道线保持车道；跟车模式包括：本车跟随正前方车辆保持车道；降级模式包括：本车不跟随正前方车辆切出本车道，保持其他车辆切入本车道时的本车稳定性。在一些实施例中，保持其他车辆切入本车道时的本车稳定性例如：制动力不大于预设制动力阈值，方向盘转角不大于预设角度阈值，且制动力的施加、方向盘的转动均非一次性完成，防止急刹、急转向等造成车辆晃动、不稳定的情况发生。其中，预设制动力阈值和预设角度阈值可根据实际需要进行设置，本实施例不限定具 体取值。可以理解，保持本车稳定性的方式还可以为其他防止车辆晃动、急转、急停等不稳定情况发生的方式。
在一些实施例中，基于车道线有效，且没有换道信息，确定跟随模式为跟线模式。在一些实施例中，基于车道线无效，且没有换道信息，确定跟随模式为跟车模式。在一些实施例中，基于换道信息包括本车道前方车辆中切出本车道的车辆信息和/或本车左车道及本车右车道中切入本车道的车辆信息，确定跟随模式为降级模式。
504、基于确定的跟随模式，规划行驶路径。在一些实施例中，在跟随模式为跟线模式时，基于车道线信息和车道线的状态，规划行驶路径。其中车道线的状态包括有效和无效两种。在一些实施例中，基于车道线信息和车道线的状态，确定车道中心线；进而基于车道中心线规划行驶路径。
在一些实施例中，基于车道线信息和车道线的状态，确定车道的中心线，具体为：若两侧车道线(例如图6中的108和109)均有效，则基于两侧车道线生成车道中心线；若一侧车道线有效且另一侧车道线无效，则基于有效侧车道线和车道宽度生成车道中心线。在一些实施例中，基于有效侧车道线和车道宽度生成车道中心线有两种方式。方式一：基于有效侧车道线和车道宽度直接生成车道中心线；方式二：基于有效侧车道线和车道宽度先生成无效侧车道线，再由有效侧车道线和无效侧车道线生成车道中心线。本实施例中，仅单侧车道线有效时，也能平稳控制本车保持在当前车道内行驶。
在一些实施例中，在跟随模式为跟车模式时，基于环境信息，规划行驶路径。在一些实施例中，基于环境信息，规划行驶路径，具体为：确定本车道前方车辆与本车的相对位置为路径终点；进而生成本车至路径终点的多条路径曲线；从而筛选满足条件的路径曲线为行驶路径；其中，所述条件为本车周围车辆(例如图6中的102至107)距离路径曲线的平均距离最大。在一些实施例中，可基于传统样条函数的生成方法生成本车至路径终点的多条路径曲线，不再赘述。本实施例中，在两侧车道线均无效时，新增跟车模式，能够使本车跟随前方车辆保持在当前车道内行驶。
在一些实施例中，在跟随模式为降级模式时，基于本车的运动信息和本车周围车辆的换道信息，规划行驶路径。在一些实施例中，基于本车道前方车辆中切出本车道的车辆信息，利用本车的运动信息、历史规划路径和本车周围车辆的第一信息，规划行驶路径，防止本车跟随前方车辆切出本车道；其中，所述第一信息包括：本车道前方车辆中不切出本车道的车辆信息、本车左车道的车辆信息和本车右车道的车辆信息。在一些实施例中，基于本车左车道和本车右车道中切入本车道的车辆信息，利用本车的运动信息、历史规划路径和本车周围车辆的第二信息，规划行驶路径，防止由于路径终点变化导致的本车规划路径跳变；其中，所述第二信息包括：本车道前方车辆信息、本车左邻车的信息和本车右邻车的信息。
505、控制本车按照行驶路径行驶。在一些实施例中，基于规划的行驶路径，控制本车保持在当前车道内行驶。在一些实施例中，基于行驶路径，生成车辆的横向控制指令和纵向控制指令；进而将 横向控制指令和纵向控制指令发送至车辆底盘控制器，控制车辆保持车道。其中，车辆底盘控制器属于图1所示的车辆底层执行***中的一部分。
在一些实施例中，基于行驶路径，生成横向控制指令，具体为：基于本车的运动信息和道路曲率，确定预瞄纵向距离；进而基于行驶路径，确定预瞄纵向距离对应的横向相对位置；从而基于预瞄纵向距离和横向相对位置，生成车辆横向控制指令。其中，预瞄纵向距离为与本车车速和预瞄时间系数相关的前方瞄点相对本车的纵向距离，属于传统基于几何车辆横向控制方法中的关键参数，预瞄纵向距离也可采用现有方式进行确定，不再赘述。在一些实施例中，横向控制指令可包括但不限于：方向盘转角指令、扭矩控制指令。其中，扭矩控制指令是发送给转向机构控制器去执行的横向控制指令。
在一些实施例中，基于行驶路径，生成纵向控制指令，具体为：基于本车的运动信息、本车周围车辆的换道信息、道路曲率和行驶路径，确定本车的加速度和本车道前方车辆的速度；进而基于本车的加速度和本车道前方车辆的速度，生成纵向控制指令。在一些实施例中，纵向控制指令可包括但不限于：轴端扭矩指令、制动减速度指令。其中，轴端扭矩指令是发送给发动机去执行的纵向控制指令。
需要说明的是，对于前述的各方法实施例，为了简单描述，故将其都表述为一系列的动作组合，但是本领域技术人员能够理解，本公开实施例并不受所描述的动作顺序的限制，因为依据本公开实施例，某些步骤可以采用其他顺序或者同时进行。另外，本领域技术人员能够理解，说明书中所描述的实施例均属于可选实施例。
本公开实施例还提出一种非暂态计算机可读存储介质，所述非暂态计算机可读存储介质存储程序或指令，所述程序或指令使计算机执行如车道保持方法各实施例的步骤，为避免重复描述，在此不再赘述。
需要说明的是，在本文中，术语“包括”、“包含”或者其任何其他变体意在涵盖非排他性的包含，从而使得包括一系列要素的过程、方法、物品或者装置不仅包括那些要素，而且还包括没有明确列出的其他要素，或者是还包括为这种过程、方法、物品或者装置所固有的要素。在没有更多限制的情况下，由语句“包括……”限定的要素，并不排除在包括该要素的过程、方法、物品或者装置中还存在另外的相同要素。
本领域的技术人员能够理解，尽管在此所述的一些实施例包括其它实施例中所包括的某些特征而不是其它特征，但是不同实施例的特征的组合意味着处于本公开的范围之内并且形成不同的实施例。
本领域的技术人员能够理解，对各个实施例的描述都各有侧重，某个实施例中没有详述的部分，可以参见其他实施例的相关描述。
虽然结合附图描述了本公开的实施方式，但是本领域技术人员可以在不脱离本公开的精神和范围的情况下做出各种修改和变型，这样的修改和变型均落入由所附权利要求所限定的范围之内。
工业实用性
本公开实施例中，在交通拥堵工况下，通过确定本车周围车辆的换道信息，并基于本车周围的环境信息，从多种用于车道保持的跟随模式中决策一种跟随模式，从而规划行驶路径并控制车辆保持在当前车道内行驶，具有工业实用性。

Claims (19)

  1. 一种车道保持方法，其特征在于，所述方法包括：
    获取本车周围的环境信息；
    基于所述环境信息，确定本车周围车辆的换道信息；
    基于所述环境信息和所述换道信息，确定本车的跟随模式；
    基于所述跟随模式，规划行驶路径；
    控制本车按照所述行驶路径行驶。
  2. 根据权利要求1所述的方法，其特征在于，所述环境信息包括以下至少一个：
    车道线信息、本车道前方车辆信息、本车左车道的车辆信息和本车右车道的车辆信息。
  3. 根据权利要求2所述的方法，其特征在于，
    所述车道线信息包括：位置、线形和可信度；
    所述本车道前方车辆信息包括：本车道前方两辆车辆与本车的相对距离和相对速度；
    所述本车左车道的车辆信息包括：本车左邻车与本车的相对距离和相对速度、本车左前车与本车的相对距离和相对速度；
    所述本车右车道的车辆信息包括：本车右邻车与本车的相对距离和相对速度、本车右前车与本车的相对距离和相对速度。
  4. 根据权利要求1所述的方法，其特征在于，所述换道信息，包括：
    本车道前方车辆中切出本车道的车辆信息；
    本车左车道和本车右车道中切入本车道的车辆信息。
  5. 根据权利要求2所述的方法，其特征在于，基于所述环境信息，确定本车周围车辆的换道信息，包括：
    判断车道线是否有效；
    基于判断结果确定本车周围车辆的换道信息。
  6. 根据权利要求5所述的方法，其特征在于，所述基于判断结果确定本车周围车辆的换道信息，包括：
    基于所述车道线有效，利用所述车道线信息确定本车周围车辆的换道信息；
    基于所述车道线无效，利用本车的运动信息确定本车周围车辆的换道信息。
  7. 根据权利要求6所述的方法，其特征在于，所述利用本车的运动信息确定本车周围车辆的换道信息，包括：
    基于本车的运动信息，确定本车的运动轨迹；
    基于所述运动轨迹的边界，确定本车周围车辆的换道信息。
  8. 根据权利要求1所述的方法，其特征在于，所述跟随模式包括：跟线模式、跟车模式和降级模式；
    其中，所述跟线模式包括：本车跟随车道线保持车道；
    所述跟车模式包括：本车跟随正前方车辆保持车道；
    所述降级模式包括：本车不跟随正前方车辆切出本车道，保持其他车辆切入本车道时的本车稳定性。
  9. 根据权利要求8所述的方法，其特征在于，基于所述环境信息和所述换道信息，确定本车的跟随模式，包括：
    基于车道线有效，且没有换道信息，确定跟随模式为跟线模式；
    基于车道线无效，且没有换道信息，确定跟随模式为跟车模式；
    基于换道信息包括本车道前方车辆中切出本车道的车辆信息和/或本车左车道及本车右车道中切入本车道的车辆信息，确定跟随模式为降级模式。
  10. 根据权利要求8所述的方法，其特征在于，所述基于所述跟随模式，规划行驶路径，包括：
    所述跟随模式为跟线模式时，基于车道线信息和车道线的状态，规划行驶路径；
    所述跟随模式为跟车模式时，基于环境信息，规划行驶路径；
    所述跟随模式为降级模式时，基于本车的运动信息和本车周围车辆的换道信息，规划行驶路径。
  11. 根据权利要求10所述的方法，其特征在于，所述基于车道线信息和车道线的状态，规划行驶路径，包括：
    基于车道线信息和车道线的状态，确定车道中心线；
    基于车道中心线规划行驶路径。
  12. 根据权利要求11所述的方法，其特征在于，所述基于车道线信息和车道线的 状态，确定车道中心线，包括：
    若两侧车道线均有效，则基于两侧车道线生成车道中心线；
    若一侧车道线有效且另一侧车道线无效，则基于有效侧车道线和车道宽度生成车道中心线。
  13. 根据权利要求10所述的方法，其特征在于，所述基于环境信息，规划行驶路径，包括：
    确定本车道前方车辆与本车的相对位置为路径终点；
    生成本车至所述路径终点的多条路径曲线；
    筛选满足条件的路径曲线为行驶路径；其中，所述条件为本车周围车辆距离路径曲线的平均距离最大。
  14. 根据权利要求10所述的方法，其特征在于，所述基于本车的运动信息和本车周围车辆的换道信息，规划行驶路径，包括：
    基于本车道前方车辆中切出本车道的车辆信息，利用本车的运动信息、历史规划路径和本车周围车辆的第一信息，规划行驶路径；其中，所述第一信息包括：本车道前方车辆中不切出本车道的车辆信息、本车左车道的车辆信息和本车右车道的车辆信息；
    基于本车左车道和本车右车道中切入本车道的车辆信息，利用本车的运动信息、历史规划路径和本车周围车辆的第二信息，规划行驶路径；其中，所述第二信息包括：本车道前方车辆信息、本车左邻车的信息和本车右邻车的信息。
  15. 根据权利要求1所述的方法，其特征在于，所述控制本车按照所述行驶路径行驶，包括：
    基于所述行驶路径，生成车辆的横向控制指令和纵向控制指令；
    将所述车辆横向控制指令和纵向控制指令发送至车辆底盘控制器，控制车辆保持车道。
  16. 根据权利要求15所述的方法，其特征在于，基于所述行驶路径，生成横向控制指令，包括：
    基于本车的运动信息和道路曲率，确定预瞄纵向距离；
    基于所述行驶路径，确定所述预瞄纵向距离对应的横向相对位置；
    基于所述预瞄纵向距离和所述横向相对位置，生成车辆横向控制指令。
  17. 根据权利要求15所述的方法，其特征在于，基于所述行驶路径，生成纵向控制指令，包括：
    基于本车的运动信息、本车周围车辆的换道信息、道路曲率和所述行驶路径，确定本车的加速度和本车道前方车辆的速度；
    基于本车的加速度和本车道前方车辆的速度，生成纵向控制指令。
  18. 一种车载设备，其特征在于，包括：处理器和存储器；
    所述处理器通过调用所述存储器存储的程序或指令，用于执行如权利要求1至17任一项所述方法的步骤。
  19. 一种非暂态计算机可读存储介质，其特征在于，所述非暂态计算机可读存储介质存储程序或指令，所述程序或指令使计算机执行如权利要求1至17任一项所述方法的步骤。
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Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113353078A (zh) * 2021-06-24 2021-09-07 中汽创智科技有限公司 一种无车道线自动跟车轨迹确定方法及装置
CN113415274A (zh) * 2021-07-14 2021-09-21 重庆长安汽车股份有限公司 自动驾驶的跟车轨迹规划***、方法、车辆及存储介质
CN113515038A (zh) * 2021-09-07 2021-10-19 西南交通大学 一种车辆换道方法、装置、设备及可读存储介质
CN113665588A (zh) * 2021-09-10 2021-11-19 苏州轻棹科技有限公司 一种自动驾驶车辆基于无线道路数据控制车辆行驶的方法
CN113722835A (zh) * 2021-09-14 2021-11-30 吉林大学 拟人化随机换道驾驶行为建模方法
CN114013439A (zh) * 2021-11-15 2022-02-08 合众新能源汽车有限公司 车辆主动跟车方法和***
CN114084158A (zh) * 2021-12-17 2022-02-25 吉林大学 一种自动驾驶冗余控制***
CN114255594A (zh) * 2021-12-28 2022-03-29 吉林大学 一种自主代客泊车运动规划与运动控制方法
CN115223131A (zh) * 2021-11-09 2022-10-21 广州汽车集团股份有限公司 一种自适应巡航的跟随目标车辆检测方法、装置及汽车
WO2023173713A1 (zh) * 2022-03-16 2023-09-21 广州小鹏自动驾驶科技有限公司 一种车辆的转向控制方法、装置、车辆及存储介质

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115114729B (zh) * 2022-06-21 2024-06-04 重庆长安汽车股份有限公司 一种驾驶辅助***用户案例的设计方法及存储介质

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160039458A1 (en) * 2012-01-17 2016-02-11 Ford Global Technologies, Llc Autonomous lane control system
CN106218636A (zh) * 2016-07-21 2016-12-14 浙江吉利汽车研究院有限公司 一种交通拥堵辅助***
CN109606346A (zh) * 2018-12-29 2019-04-12 武汉超控科技有限公司 一种自动驾驶的车道保持***
CN109649390A (zh) * 2018-12-19 2019-04-19 清华大学苏州汽车研究院（吴江） 一种自动驾驶汽车自主跟车***及方法
CN110341707A (zh) * 2019-08-07 2019-10-18 浙江吉利汽车研究院有限公司 车道保持控制***及车道保持控制方法、车辆

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP6327719B2 (ja) * 2016-02-04 2018-05-23 株式会社Ｓｕｂａｒｕ 車両の走行制御装置
WO2018055773A1 (ja) * 2016-09-26 2018-03-29 日産自動車株式会社 走路設定方法及び走路設定装置
KR20190067040A (ko) * 2017-12-06 2019-06-14 현대자동차주식회사 선행차량 차로 변경을 고려한 차선 중앙 주행 보조 시스템 및 그 방법
CN110239518B (zh) * 2019-05-20 2023-09-01 福瑞泰克智能***有限公司 一种车辆横向位置控制方法及装置
CN110293970B (zh) * 2019-05-22 2020-10-16 重庆长安汽车股份有限公司 一种自动驾驶汽车的行驶控制方法、装置及汽车

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160039458A1 (en) * 2012-01-17 2016-02-11 Ford Global Technologies, Llc Autonomous lane control system
CN106218636A (zh) * 2016-07-21 2016-12-14 浙江吉利汽车研究院有限公司 一种交通拥堵辅助***
CN109649390A (zh) * 2018-12-19 2019-04-19 清华大学苏州汽车研究院（吴江） 一种自动驾驶汽车自主跟车***及方法
CN109606346A (zh) * 2018-12-29 2019-04-12 武汉超控科技有限公司 一种自动驾驶的车道保持***
CN110341707A (zh) * 2019-08-07 2019-10-18 浙江吉利汽车研究院有限公司 车道保持控制***及车道保持控制方法、车辆

Cited By (15)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113353078A (zh) * 2021-06-24 2021-09-07 中汽创智科技有限公司 一种无车道线自动跟车轨迹确定方法及装置
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CN113415274A (zh) * 2021-07-14 2021-09-21 重庆长安汽车股份有限公司 自动驾驶的跟车轨迹规划***、方法、车辆及存储介质
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CN113515038B (zh) * 2021-09-07 2021-11-26 西南交通大学 一种车辆换道方法、装置、设备及可读存储介质
CN113665588A (zh) * 2021-09-10 2021-11-19 苏州轻棹科技有限公司 一种自动驾驶车辆基于无线道路数据控制车辆行驶的方法
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CN113722835B (zh) * 2021-09-14 2023-10-20 吉林大学 拟人化随机换道驾驶行为建模方法
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