WO2023159839A1 - 换道路径规划方法、装置、智能驾驶汽车及存储介质 - Google Patents

换道路径规划方法、装置、智能驾驶汽车及存储介质 Download PDF

Info

Publication number
WO2023159839A1
WO2023159839A1 PCT/CN2022/102379 CN2022102379W WO2023159839A1 WO 2023159839 A1 WO2023159839 A1 WO 2023159839A1 CN 2022102379 W CN2022102379 W CN 2022102379W WO 2023159839 A1 WO2023159839 A1 WO 2023159839A1
Authority
WO
WIPO (PCT)
Prior art keywords
lane
information
target
lane change
changing
Prior art date
Application number
PCT/CN2022/102379
Other languages
English (en)
French (fr)
Inventor
徐磊
徐勇超
崔卫卫
朱頔卿
陈必成
李洪昌
Original Assignee
爱驰汽车（上海）有限公司
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 爱驰汽车（上海）有限公司 filed Critical 爱驰汽车（上海）有限公司
Publication of WO2023159839A1 publication Critical patent/WO2023159839A1/zh

Links

Images

Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • 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
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
    • B60W40/06—Road conditions
    • 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
    • B60W2552/05—Type of road, e.g. motorways, local streets, paved or unpaved roads
    • 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
    • B60W2552/30—Road curve radius
    • 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
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
    • B60W40/06—Road conditions
    • B60W40/072—Curvature of the road

Definitions

  • the present application relates to the technical field of intelligent driving control, in particular, to a lane-changing path planning method and device, an intelligent driving car and a storage medium.
  • Intelligent driving is the product of the deep integration of the automotive industry with new-generation information technologies such as artificial intelligence and high-performance computing platforms, and is an upgrade of the automotive industry. More and more automobile OEMs, suppliers, and technology companies are shifting their research and development focus to automotive intelligent driving technology, taking advantage of their respective advantages, seizing opportunities for industrial upgrading, and entering the autonomous driving industry.
  • the purpose of this application is to provide a lane-changing path planning method, device, intelligent driving car and storage medium, in order to provide a safe and stable lane-changing path planning method in view of the above-mentioned deficiencies in the prior art.
  • the embodiment of the present application provides a lane change path planning method, including:
  • the road information including: current lane, target lane, and road attributes;
  • the generating the pre-lane-changing path in the lane coordinate system according to the road information and the driving state information of the target vehicle includes:
  • the pre-lane change path in a lane coordinate system is generated according to the lane change time, the boundary of the lane change area, the first vehicle speed in the driving state information, and the lane change compression ratio.
  • the method before calculating the lane change compression ratio according to the road information and the driving state information of the target vehicle, the method further includes:
  • the calculating the lane change compression ratio according to the road information and the driving state information of the target vehicle includes:
  • the lane change compression ratio is calculated according to the road information, the driving state information and the heading.
  • the method further includes:
  • the state deviation information includes: speed deviation information; the speed deviation information is the deviation between the second vehicle's driving speed and the standard driving speed in the driving state information during the lane change process;
  • the judging whether the state deviation information satisfies the preset condition for successful lane change includes:
  • the state deviation information further includes: position deviation information, the position deviation information is the position deviation between the position of the target vehicle during the lane change process and a preset position point in the target lane change route ;
  • the judging whether the state deviation information satisfies the preset lane change success condition further includes:
  • the method also includes:
  • the embodiment of the present application also provides a lane change path planning device, including: an acquisition module, a generation module, and a coordinate conversion module;
  • the acquiring module is used to acquire road information of the target vehicle during driving, the road information including: current lane, target lane and road attributes;
  • the generating module is configured to generate a pre-lane change path in a lane coordinate system according to the road information and the driving state information of the target vehicle;
  • the coordinate conversion module is configured to map the pre-lane-changing path to a map coordinate system to obtain a target lane-changing path; the target lane-changing path is used to guide the target vehicle to switch from the current lane to the Describe the target lane.
  • the embodiment of the present application also provides a smart driving car, including: a lane changing path planning device, a smart driving car and a bus, the lane changing path planning device stores a lane changing path planning method, when the smart driving car During operation, the smart driving car communicates with the lane-changing path planning device through a bus, and the lane-changing path planning device executes program instructions to execute the lane-changing path planning as described in any one of the first aspect. method steps.
  • the embodiment of the present application also provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is run by a processor, the lane change described in any one of the first aspect is executed. Steps of the path planning method.
  • the embodiment of the present application provides a lane change route planning method, after obtaining the road information of the target vehicle during driving, and generating a lane coordinate system according to the road information and the driving state information of the target vehicle
  • the track priority can be adjusted, and a personalized lane changing style can be configured;
  • the trajectory is smooth and the time-space consistency is good.
  • the lane-changing path planning method of the present application performs pre-lane-changing path planning based on the lane coordinate system, which reduces the complexity of lane-changing path planning, has high timeliness, small computing power requirements, and strong scene adaptability, and can be widely used in assisting vehicles in target vehicles. Lane changing path planning.
  • FIG. 1 is a flow chart of a lane change path planning method provided by an embodiment of the present application
  • FIG. 2 is a flow chart of a lane change path planning method provided in another embodiment of the present application.
  • Fig. 3 is a schematic diagram of lane changing trajectories with different lane changing compression ratios under the same conditions provided by an embodiment of the present application;
  • FIG. 4 is a flowchart of a lane change path planning method provided by another embodiment of the present application.
  • FIG. 5 is a flow chart of a lane-changing route planning method provided in yet another embodiment of the present application.
  • FIG. 6 is a flow chart of a lane-changing route planning method provided in yet another embodiment of the present application.
  • FIG. 7 is a flow chart of a lane change path planning method provided in another embodiment of the present application.
  • FIG. 8 is a flow chart of a lane-changing path planning method provided by another fourth embodiment of the present application.
  • FIG. 9 is a schematic diagram of a re-planning location of a lane-changing trajectory provided by an embodiment of the present application.
  • Fig. 10 is a schematic diagram of a replanned heading of a lane change trajectory provided by an embodiment of the present application.
  • Fig. 11 is a schematic diagram of a re-planned curvature of a lane-changing trajectory provided by an embodiment of the present application.
  • Fig. 12 is a schematic diagram of a lane-changing path planning device provided by an embodiment of the present application.
  • Fig. 13 is a schematic diagram of an intelligent driving car provided by an embodiment of the present application.
  • first and second are only used for descriptive purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the indicated technical features quantity.
  • features defined as “first” and “second” may explicitly or implicitly include at least one feature.
  • plural means at least two, such as two, three, unless otherwise specifically defined.
  • the term “comprises”, “comprises” or any other variation thereof is intended to cover a non-exclusive inclusion such that a process, method, article or apparatus comprising a set of elements includes not only those elements but also other elements not expressly listed elements, or also elements inherent in such a process, method, article, or apparatus.
  • an element defined by the phrase “comprising a " does not exclude the presence of additional identical elements in the process, method, article or apparatus that includes the element.
  • intelligent driving is an upgrade of the automotive industry.
  • Intelligent driving technology includes multiple directions, among which decision-making and planning technology is the most important, mainly involving intelligent driving human-like decision-making and optimal path generation, reflecting the "high-level" of intelligent driving technology.
  • mainstream L2 functions mainly include lane keeping, adaptive cruise control and automatic parking, and rarely involve intelligent auxiliary lane changing functions. The main reason is that the complexity and challenges of lane changing scenarios are much higher than those of lane keeping conditions.
  • the current mainstream lane-changing path planning methods are difficult to achieve performance balance in scenarios with adjustable lane-changing speed, continuous curvature, time-space consistency, different vehicle speeds, different road curvatures, different lane widths, and trajectory tracking deviations.
  • Fig. 1 is a flow chart of a lane change route planning method provided by an embodiment of the present application.
  • the interactive control method can be realized by an electronic device running the above method.
  • the electronic device can be, for example, a terminal device or a server. As shown in Figure 1, the method includes:
  • Step 101 Obtain road information during driving of the target vehicle.
  • the road information includes: current lane, target lane, and road attributes.
  • the current lane represents the current lane of the target vehicle during driving, which can be obtained based on detection devices installed on the target vehicle, such as cameras, sensors, etc.; it can also be obtained based on high-precision map guidance, This application does not limit this.
  • the current lane may also include the deviation of the target vehicle relative to the center position of the current lane, so as to enhance the accuracy of subsequent calculations.
  • the target lane represents the lane change target lane for the target vehicle to change lanes during driving.
  • the target lane can be selected by the driver of the target vehicle or an intelligent control device.
  • the user can display screen, the lane-changing control device on the target vehicle, etc. to select the target lane; for another example, the intelligent control device of the target vehicle can intelligently select the target lane after analyzing the surrounding environment, vehicles and other information to achieve avoidance. Barriers or Fast Pass.
  • the target lane When the target lane is selected, it can select offset information relative to the current lane (for example, the target lane is selected as one lane on the right side of the current lane, or two lanes on the left, etc.), in order to improve the following based on the target lane
  • the accuracy of the calculation, after selection, can be based on detection devices (such as cameras, sensors, etc.) installed on the target vehicle, or high-precision map guidance to obtain the specific location of the target lane.
  • detection devices such as cameras, sensors, etc.
  • the road attributes represent the inherent attributes of the road during the driving process of the target vehicle, such as the width of the lane, the curvature of the road and other information. These road attributes can be based on the detection devices installed on the target vehicle (such as cameras, sensors, etc.) , high-precision map guidance, etc. for calculation and acquisition. The above is only an example. In actual implementation, there may be other ways to obtain the road attribute information, which is not limited in this application, as long as the road attribute information can be obtained.
  • Step 102 According to the road information and the driving state information of the target vehicle, generate a pre-lane-changing path in the lane coordinate system.
  • the driving state information of the target vehicle is a description of the driving state of the target vehicle, for example, may include vehicle speed, yaw rate, and the like.
  • the driving status information of the target vehicle can be obtained through the chassis information of the target vehicle, and this application does not limit the specific acquisition method of such driving status information.
  • the lane coordinate system describes the position of the car relative to the road.
  • the lane coordinate system can be, for example, a Frenet coordinate system (Frenet coordinate system).
  • Frenet coordinate system the distance along the road is taken as the ordinate ( s), and the displacement with the longitudinal line is called the abscissa (d). This ensures that at each point on the road, the horizontal axis and the vertical axis are vertical, the ordinate represents the distance traveled on the road, and the abscissa represents the distance the car deviates from the center line.
  • the target vehicle moves forward, and its trajectory in the lane is a straight line, which greatly simplifies the difficulty of trajectory planning.
  • Fig. 2 is a flow chart of a lane-changing path planning method provided by another embodiment of the present application; as shown in Fig. 2 , according to the road information and the driving state information of the target vehicle, a pre-lane-changing path under the lane coordinate system is generated ,include:
  • Step 201 Calculate the lane change time, the lane change compression ratio, and the lane change area boundary according to the road information and the driving state information of the target vehicle.
  • the average curvature of the road in step 101 can be calculated in the Frenet coordinate system in the following manner, for example, it can be calculated based on the detection device or high-precision map installed on the target vehicle in the following manner Curvature, first delineate the range of the preset width within the detection range of the detection device; or use the target vehicle as the reference on the high-precision map, and use the direction of travel of the target vehicle as the extension to delineate the range of the preset width.
  • the range of the preset width can be calculated by the upper and lower boundaries of the sampling time, for example, s min and s max respectively represent the upper and lower boundaries of the range of the preset width (in the s direction) in the traveling direction of the target vehicle , t min and t max respectively represent the upper and lower boundaries of the sampling time, where:
  • v is the velocity of the target vehicle in the Frenet coordinate system.
  • the curvature of the road within a preset width can be determined.
  • the calculated curvature of the road is getting closer to the preset width
  • the curvature of the road at other path curvature points on the road can be determined by repeating the above formula, and then the average curvature of the road can be calculated:
  • kappa avg represents the curvature of the target road
  • n represents the total number of calculation path curvature points
  • kappa i represents the curvature of the point whose longitudinal distance is i.
  • the following method can be used for calculation:
  • this lane change time data set T n represents the lane change time at different speeds, and calculate the lane change time t lc according to the lane change time data set T n :
  • k kappa is a constant coefficient
  • linearInterpolation is a first-order linear interpolation function
  • the input is the lane change time data set T n and the speed v of the target vehicle in the Frenet coordinate system.
  • Figure 3 shows a lane-changing with different lane-changing compression ratios provided by an embodiment of the present application Schematic diagram of the trajectory, the three overlapping curves in Figure 3 have the same lane change conditions, for example: the start point and end point of the lane change are the same, the speed of the vehicle is the same, etc.
  • the lane-changing compression ratio of the middle curve is set to 0.5, which means that in this case, the degree of steepness in the first half of the lane-changing path is the same as that in the second half, so it can be seen from the figure
  • the relationship between the first half and the second half of the journey is basically center-symmetrical; the lane-changing compression ratio of the curve above the middle curve is greater than 0.5, which means that compared with the middle curve, it is more urgent first and then slower.
  • the value range of the lane-changing compression ratio is normalized in the above-mentioned implementations so that it is between 0 and 1, but in other implementations, the lane-changing compression ratio
  • the value of the ratio may also have other forms, which are not limited in the present application, as long as it can reflect the degree of urgency in the front and rear half of the lane-changing path.
  • the lane-changing compression ratio can be set by the user, for example, through the human interaction display screen or the joystick of the target vehicle, or through the control system of the target vehicle according to daily usage habits. Intelligent settings are performed for situations such as these, which are not limited in this application.
  • FIG. 4 is a flow chart of a lane change path planning method provided in another embodiment of the present application; as shown in FIG. 4 , according to the road information and the driving State information, before calculating the lane change compression ratio, the method also includes:
  • Step 401 Obtain the heading of the target vehicle.
  • Calculate the lane change compression ratio according to the road information and the driving state information of the target vehicle including:
  • Step 402 Calculate the lane change compression ratio according to road information, driving state information and heading.
  • heading 0 represents the heading, which can be obtained based on the detection device installed on the target vehicle, high-precision map guidance, etc.
  • a represents the ratio constant term
  • k 0 represents the quadratic coefficient constant term
  • k 1 Indicates the first-order coefficient constant term.
  • Step 202 Generate a pre-lane change path in the lane coordinate system according to the lane change time, the boundary of the lane change area, the driving speed of the first vehicle in the driving state information, and the lane change compression ratio.
  • the lane change trajectory is calculated from the head and tail point constraints, trajectory length, and lane change compression ratio, as follows:
  • Coeff [c 0 ,c 1 ,c 2 ,c 3 ,c 4 ,c 5 ,c 6 ,c 7 ];
  • the trajectory solution matrix is as follows:
  • the pre-lane change path in the lane coordinate system is generated. From the above method, it can be seen that by adjusting the lane change compression ratio, the zero-order, first-order, and second-order continuous and simultaneous trajectory adjustment of the lane change trajectory can be realized.
  • Step 103 Map the pre-lane-changing path to the map coordinate system to obtain the target lane-changing path; the target lane-changing path is used to guide the target vehicle to switch from the current lane to the target lane.
  • the map coordinate system is a description of map information.
  • the map coordinate system can be, for example, a Cartesian coordinate system (Cartesian coordinates).
  • the Cartesian coordinate system is composed of two number axes intersecting at the origin, forming a plane affine Coordinate system, usually, we are accustomed to use the map coordinate system to define the position of the spatial point. Therefore, after the pre-lane-changing path is planned in the lane coordinate system, the target lane-changing path can be obtained by mapping the pre-lane-changing path to the map coordinate system, and then the target lane-changing path can be used to guide the target vehicle from the current lane Switch to the target lane.
  • the solved pre-lane-changing path in the lane coordinate system can be mapped to the map coordinate system and dead reckoning (Dead Reckoning, DR reckoning) can be performed in real time, and the reckoning result can be delivered to the target The control layer of the vehicle.
  • dead reckoning Dead Reckoning, DR reckoning
  • the embodiment of the present application provides a lane change path planning method. After obtaining the road information of the target vehicle during driving, according to the road information and the driving state information of the target vehicle, a pre-change path in the lane coordinate system is generated. The lane change path; the pre-lane change path is mapped to the map coordinate system to obtain the target lane change path, which can be used to guide the target vehicle to change lanes. On the one hand, it realizes safe, comfortable and efficient intelligent lane changing under different vehicle speeds, different lane widths, and different road curvatures.
  • the track priority can be adjusted, and a personalized lane changing style can be configured;
  • the trajectory is smooth and the time-space consistency is good.
  • the lane-changing path planning method of the present application performs pre-lane-changing path planning based on the lane coordinate system, which reduces the complexity of lane-changing path planning, has high timeliness, small computing power requirements, and strong scene adaptability, and can be widely used in assisting vehicles in target vehicles. Lane changing path planning.
  • the present application also provides a possible implementation of a lane-changing route planning method
  • Figure 5 is a flow chart of a lane-changing route planning method provided in another embodiment of the present application Figure; As shown in Figure 5, after the pre-lane-changing path is mapped to the map coordinate system, and the target lane-changing path is obtained, the method also includes:
  • Step 501 Obtain state deviation information of the target vehicle during driving based on the target lane-changing route.
  • the target vehicle After the target lane-changing path is obtained, there are some unexpected situations in the target vehicle’s driving process based on the target lane-changing path, such as obstacles (vehicles, pedestrians, objects, etc.) appearing on the target lane-changing path, and then for example, when obtaining the current lane information, there is a deviation in the specific position of the target traffic vehicle on the current lane (for example, there is an error in the lateral deviation in the calculation), and for example, there is an instrument error in the sensor when obtaining the driving state information of the target vehicle, etc., may It causes a state deviation in the process of changing lanes.
  • the state deviation information may be, for example, speed deviation, position deviation, etc., which is not limited in the present application, as long as the deviation information can provide accuracy or safety reference for the lane change of the target vehicle.
  • Step 502 Judging whether the state deviation information satisfies the preset condition for successful lane change.
  • the state deviation information is within a certain range, it can be considered that the lane change process is reasonable, so it needs to be further judged, and the state deviation information is judged according to the preset lane change success conditions , if the state deviation information satisfies the preset condition for successful lane change, it can be judged based on this that the current lane change of the target vehicle is normal, no intervention correction is needed, and only need to continue to monitor and judge this.
  • Step 503 If the state deviation information does not satisfy the condition for a successful lane change, reacquire the road information of the target vehicle during driving, so as to regenerate the target lane change route.
  • the state deviation information does not meet the conditions for successful lane change, it means that if the target vehicle continues to change lanes according to the current state, the lane change may be unsuccessful or there may be safety hazards.
  • Road information regenerate the target lane-changing route according to the lane-changing route planning method in the above steps.
  • the present application also provides a possible implementation of a lane-changing route planning method
  • Figure 6 is a flow chart of a lane-changing route planning method provided in another second embodiment of the present application Figure;
  • the state deviation information comprises: speed deviation information;
  • the speed deviation information is the deviation of the second vehicle speed in the driving state information in the lane change process and the standard driving speed; whether the judgment state deviation information satisfies the expected
  • the set lane change success conditions include:
  • Step 601 Determine whether the speed deviation information is within the preset speed deviation range in the lane change success condition.
  • the planning of the lane-changing route in the present application is largely related to the driving speed of the target vehicle, and a very small speed change may not affect the Whether the lane change is successful or not will affect the success of the lane change.
  • the difference between the speed of the lane change and the planned speed is too large, it will have an impact on the success of the lane change.
  • the deviation of the second vehicle is judged, and it is judged whether the deviation between the second vehicle's running speed and the standard running speed is within the preset speed deviation range in the lane change success condition.
  • the standard driving speed can be the driving speed of the target vehicle when planning the lane-changing path, or the standard speed set corresponding to the driving speed of the target vehicle when planning the lane-changing path (for example, the speed in which the lane-changing path is planned The speed range centered on the traveling speed of the target vehicle at the time, etc.), which is not limited in this application.
  • the preset speed deviation threshold if the absolute value of the deviation between the second vehicle's driving speed and the standard driving speed is less than or equal to the preset speed deviation threshold, it means that the speed deviation information is within the preset speed deviation range .
  • the above is only an example for illustration. In actual implementation, there may be other ways of judging the speed deviation information, which is not limited in this application.
  • Step 602 If the speed deviation information is not within the preset speed deviation range, it is determined that the speed deviation information does not meet the lane change success condition.
  • the speed deviation threshold is preset. If the absolute value of the deviation between the second vehicle's running speed and the standard running speed is greater than the preset speed deviation threshold, it means that the speed deviation information is not within the preset speed deviation range.
  • the above is only an example for illustration. In actual implementation, there may be other ways of judging the speed deviation information, which is not limited in this application.
  • the present application also provides a possible implementation of a lane-changing route planning method
  • Figure 7 is a flow chart of a lane-changing route planning method provided by another third embodiment of the present application ;
  • the state deviation information also includes: position deviation information, the position deviation information is the position deviation between the position of the target vehicle in the lane change process and the preset position point in the target lane change path; determine the state deviation information Whether the preset conditions for successful lane change are met, including:
  • Step 701 If the speed deviation information is within the preset speed deviation range, determine whether the position deviation information is within the preset position deviation range in the lane change success condition.
  • the position deviation information is the position of the target vehicle during the lane change process and the current target change position under the guidance of the target lane change path. The position deviation between the preset position points in the road path position.
  • a very small position deviation may not affect the success of the lane change.
  • the position deviation between the current position of the lane change driving and the preset position point is too large, it will affect the success of the lane change. Therefore, it is necessary to judge the position deviation between the position of the target vehicle during the lane change process and the preset position point in the target lane change path, and determine the distance between the position of the target vehicle during the lane change process and the preset position point in the target lane change path. Whether the position deviation information is within the preset position deviation range in the lane change success condition.
  • the preset location point may be an exact location point on the target lane-changing route, or may be a coordinate area centered on the location point, which is not limited in this application.
  • the preset position deviation threshold if the absolute value of the deviation between the position of the target vehicle during the lane change process and the preset position point in the target lane change path is less than or equal to the preset position deviation threshold, it means The position deviation information is within the preset position deviation range.
  • the above is only an example for illustration. In actual implementation, there may be other judgment methods for the position deviation information, which is not limited in this application.
  • Step 702 If the position deviation information is not within the preset position deviation range, then determine that the position deviation information does not satisfy the lane change success condition.
  • the preset position deviation threshold err_thres if the absolute value of the deviation between the position of the target vehicle and the preset position point in the target lane change path during the lane change process is greater than err_thres, it means that the position deviation information is not in within the preset position deviation range.
  • err_thres if the absolute value of the deviation between the position of the target vehicle and the preset position point in the target lane change path during the lane change process is greater than err_thres, it means that the position deviation information is not in within the preset position deviation range.
  • the present application also provides a possible implementation of a lane-changing route planning method
  • Figure 8 is a flow chart of a lane-changing route planning method provided by another fourth embodiment of the present application ; As shown in Figure 8, the method includes:
  • Step 801 Determine the execution ratio of the target lane change route and the historical lane change planning times for the target lane.
  • the execution ratio of the target lane change path is judged.
  • the execution ratio of the target lane change path indicates the ratio between the currently completed target lane change path and the total path of the target lane change path. The higher the execution ratio of the target lane change path, the more target lane change paths have been completed so far. It can be understood that when the execution ratio of the target lane change path reaches a certain preset ratio, it can be considered that the lane change has been completed. Therefore no re-planning is required.
  • the number of historical lane-changing plans for the target lane is judged. It can be understood that the lane-changing can be completed after a certain number of planning in general. When it cannot be completed, it can be considered that there are other unexpected factors on the current road, or that the target vehicle may be faulty, etc. Continued re-planning is not only difficult to achieve lane change, but may also have a negative impact on driving safety. The number of road planning is judged.
  • Step 802 If the execution ratio is smaller than the preset ratio, and the number of times of re-planning is smaller than the number threshold, re-plan the lane change path;
  • the lane-changing path When re-planning the lane-changing path, it can be calculated based on real-time DR to match the current position with the trajectory point in the target lane-changing path of the previous cycle of lane-changing planning, and set it as the starting point constraint of the currently planned lane-changing trajectory (ie (l 0 ,dl 0 ,ddl 0 ,dddl 0 )) in the above steps, at the same time, according to the execution ratio of the target lane change path of the lane change planning in the previous cycle, determine the target end point and end point constraints, and generate the predicted lane coordinate system again.
  • FIG. 9 is a schematic diagram of a lane-changing trajectory re-planning position provided by an embodiment of the present application; as shown in FIG. 9 , re-planning is performed at an execution ratio of 50% due to a large speed difference.
  • Fig. 10 is a schematic diagram of a replanning heading of a lane changing trajectory provided by an embodiment of the present application;
  • Fig. 11 is a schematic diagram of a replanning curvature of a lane changing trajectory provided by an embodiment of the present application; as shown in Fig. 10 and Fig. 11 , replanning The lateral deviation, tangential direction, and curvature of the planned trajectory are continuous to effectively ensure a smooth and comfortable lane change process.
  • the lane changing trajectory can be continuous and smooth, and at the same time, the speed and slowness of the lane changing trajectory can be adjusted; the lane changing trajectory re-planning mechanism can be designed to ensure that the lane changing trajectory is continuous in special scenarios (large tracking deviation, etc.); The comfort, smoothness, safety and time consistency of the lane change function.
  • Fig. 12 is a schematic diagram of a lane-changing path planning device provided by an embodiment of the present application.
  • the above-mentioned lane-changing path planning device 100 includes: an acquisition module 121, a generation module 123, and a coordinate transformation module 125;
  • An acquisition module 121 configured to acquire road information of the target vehicle during driving, the road information including: current lane, target lane and road attributes;
  • a generating module 123 configured to generate a pre-lane-changing path in the lane coordinate system according to the road information and the driving state information of the target vehicle;
  • the coordinate conversion module 125 is configured to map the pre-lane-changing path to the map coordinate system to obtain a target lane-changing path; the target lane-changing path is used to guide the target vehicle to switch from the current lane to the target lane.
  • the generation module 123 is configured to calculate the lane change time, the lane change compression ratio, and the lane change area boundary according to the road information and the driving state information of the target vehicle; The first vehicle speed in the state information and the lane change compression ratio generate a pre-lane change path in the lane coordinate system.
  • the acquiring module 121 is configured to acquire the heading of the target vehicle
  • the generation module 123 is used to calculate the lane change compression ratio according to road information, driving state information and heading.
  • the lane change path planning device 100 further includes: a judging module;
  • An acquisition module 121 configured to acquire state deviation information of the target vehicle during driving based on the target lane-changing route
  • a judging module used to judge whether the state deviation information meets the preset lane change success conditions; if the state deviation information does not meet the lane change success conditions, reacquire the road information of the target vehicle during driving to regenerate the target lane change path .
  • the state deviation information includes: speed deviation information; the speed deviation information is the deviation between the second vehicle's driving speed and the standard driving speed in the driving state information during the lane change process;
  • the judging module is used to judge whether the speed deviation information is within the preset speed deviation range in the lane change success condition; if the speed deviation information is not within the preset speed deviation range, it is determined that the speed deviation information does not meet the lane change success condition.
  • the state deviation information further includes: position deviation information, the position deviation information is the position deviation between the position of the target vehicle during the lane change process and the preset position point in the target lane change route;
  • the judging module is used to determine whether the position deviation information is within the preset position deviation range in the lane change success condition if the speed deviation information is within the preset speed deviation range; if the position deviation information is not within the preset position deviation range, determine The position deviation information does not meet the conditions for a successful lane change.
  • the judging module is used to determine the execution ratio of the target lane-changing path and the number of historical lane-changing planning times for the target lane; if the execution ratio is smaller than the preset ratio, and the number of re-planning times is smaller than the number threshold, re-change road path planning.
  • the above modules may be one or more integrated circuits configured to implement the above method, for example: one or more specific integrated circuits (Application Specific Integrated Circuit, referred to as ASIC), or, one or more microprocessors (digital signal processor, DSP for short), or, one or more Field Programmable Gate Arrays (Field Programmable Gate Array, FPGA for short), etc.
  • ASIC Application Specific Integrated Circuit
  • DSP digital signal processor
  • FPGA Field Programmable Gate Array
  • the processing element may be a general-purpose processor, such as a central processing unit (Central Processing Unit, referred to as CPU) or other processors that can call program codes.
  • CPU central processing unit
  • these modules can be integrated together and implemented in the form of a system-on-a-chip (SOC for short).
  • Fig. 13 is a schematic diagram of a smart driving car provided by the embodiment of the present application, the smart driving car includes: a lane-changing path planning device 131, a smart driving car 132 and a bus, and the executable program instructions of the lane-changing path planning device 131 run At this time, the lane-changing path planning device 131 communicates with the smart driving car 132 through the bus, and the lane-changing path planning device 131 executes program instructions to execute the steps of the above-mentioned lane-changing path planning method.
  • the specific implementation manner and technical effect are similar, and will not be repeated here.
  • the embodiment of the present application provides a possible implementation example of a computer-readable storage medium, capable of executing the lane-changing path planning method provided in the above-mentioned embodiments, and a computer program is stored on the storage medium, and the computer program executes the above-mentioned lane-changing path when run by a processor Steps of the planning method.
  • a computer program stored in a storage medium may include several instructions to enable a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (English: processor) to execute the methods of the various embodiments of the present application partial steps.
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (English: Read-Only Memory, abbreviated: ROM), random access memory (English: Random Access Memory, abbreviated: RAM), magnetic disk or optical disc, etc.
  • the disclosed devices and methods may be implemented in other ways.
  • the device embodiments described above are only illustrative.
  • the division of units is only a logical function division. In actual implementation, there may be other division methods.
  • multiple units or components can be combined or integrated. to another system, or some features may be ignored, or not implemented.
  • the mutual coupling or direct coupling or communication connection shown or discussed may be through some interfaces, and the indirect coupling or communication connection of devices or units may be in electrical, mechanical or other forms.
  • a unit described as a separate component may or may not be physically separated, and a component displayed as a unit may or may not be a physical unit, that is, it may be located in one place, or may be distributed to multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • each functional unit in each embodiment of the present application may be integrated into one processing unit, each unit may exist separately physically, or two or more units may be integrated into one unit.
  • the above-mentioned integrated units can be implemented in the form of hardware, or in the form of hardware plus software functional units.
  • the above-mentioned integrated units implemented in the form of software functional units may be stored in a computer-readable storage medium.
  • the above-mentioned software functional units are stored in a storage medium, and include several instructions to enable a computer device (which may be a personal computer, server, or network device, etc.) or a processor (English: processor) to execute the methods of the various embodiments of the present application. partial steps.
  • the aforementioned storage media include: U disk, mobile hard disk, read-only memory (English: Read-Only Memory, abbreviated: ROM), random access memory (English: Random Access Memory, abbreviated: RAM), magnetic disk or optical disc, etc.

Landscapes

  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Traffic Control Systems (AREA)
  • Control Of Driving Devices And Active Controlling Of Vehicle (AREA)

Abstract

本申请提供一种换道路径规划方法、装置、智能驾驶汽车及存储介质，涉及智能驾驶控制技术领域。该换道路径规划方法包括：获取目标交通工具在行驶过程中的道路信息后，根据道路信息以及目标交通工具的行驶状态信息，生成车道坐标系下的预换道路径；将预换道路径映射到地图坐标系下，得到目标换道路径，该目标换道路径可以用于引导目标交通工具进行换道。一方面，实现了在不同车速、不同车道宽度、不同道路曲率下安全、舒适且高效地智能换道。另一方面，在换道中，由于换道压缩比可调，与传统换道路径方法相比，在保证切向连续、曲率连续基础上实现轨迹缓急可调，可配置私人化换道风格；换道轨迹平缓，时间空间一致性好。

Description

换道路径规划方法、装置、智能驾驶汽车及存储介质
相关申请
本申请要求于2022年2月22日提交中国专利局、申请号为202210159774.3、名称为“换道路径规划方法、装置、智能驾驶汽车及存储介质”的中国专利申请的优先权.
技术领域
本申请涉及智能驾驶控制技术领域，具体而言，涉及一种换道路径规划方法、装置、智能驾驶汽车及存储介质。
背景技术
智能驾驶是汽车产业与人工智能、高性能计算平台等新一代信息技术深度融合的产物，是汽车产业的升级。越来越多的汽车主机厂、供应商以及科技公司等将研发的重点转至汽车智能驾驶技术，利用各自优势，纷纷抓住产业升级机会，切入自动驾驶行业。
由于自动驾驶中换道场景复杂程度与挑战性远高于车道保持工况，现有的自动驾驶领域鲜有涉及智能辅助换道功能的设计。因此为了实现汽车在结构化道路高效、安全完成换道，减轻驾驶员在换道场景下心理与生理负担，提升该工况下汽车行驶的安全性，需要一种安全稳定的换道路径规划方法。
发明内容
本申请的目的在于，针对上述现有技术中的不足，提供一种换道路径规划方法、装置、智能驾驶汽车及存储介质，以便提供一种安全稳定的换道路径规划方法。
为实现上述目的，本申请实施例采用的技术方案如下：
第一方面，本申请实施例提供了一种换道路径规划方法，包括：
获取目标交通工具在行驶过程中的道路信息，所述道路信息包括：当前车道、目标车道以及道路属性；
根据所述道路信息以及所述目标交通工具的行驶状态信息，生成车道坐标系下的预换道路径；
将所述预换道路径映射到地图坐标系下，得到目标换道路径；所述目标换道路径用于引导所述目标交通工具从所述当前车道切换至所述目标车道。
在一实施例中，所述根据所述道路信息以及所述目标交通工具的行驶状态信息，生成车道坐标系下的预换道路径，包括：
根据所述道路信息以及所述目标交通工具的行驶状态信息，计算换道时间、换道压缩比以及换道区域边界；
根据所述换道时间、所述换道区域边界、所述行驶状态信息中的第一车辆行驶速度，以及所述换道压缩比，生成车道坐标系下的所述预换道路径。
在一实施例中，所述根据所述道路信息以及所述目标交通工具的行驶状态信息，计算换道压缩比之前，所述方法还包括：
获取所述目标交通工具航向；
所述根据所述道路信息以及所述目标交通工具的行驶状态信息，计算换道压缩比，包括：
根据所述道路信息、所述行驶状态信息以及所述航向，计算所述换道压缩比。
在一实施例中，所述将所述预换道路径映射到地图坐标系下，得到目标 换道路径之后，所述方法还包括：
获取所述目标交通工具在基于所述目标换道路径的行驶过程中的状态偏差信息；
判断所述状态偏差信息是否满足预设的换道成功条件；
若所述状态偏差信息不满足所述换道成功条件，重新获取所述目标交通工具在行驶过程中的道路信息，以重新生成所述目标换道路径。
在一实施例中，所述状态偏差信息包括：速度偏差信息；所述速度偏差信息为换道过程中的所述行驶状态信息中的第二车辆行驶速度与标准行驶速度的偏差；
所述判断所述状态偏差信息是否满足预设的换道成功条件，包括：
判断所述速度偏差信息是否在所述换道成功条件中的预设速度偏差范围内；
若所述速度偏差信息不在所述预设速度偏差范围内，则确定所述速度偏差信息不满足所述换道成功条件。
在一实施例中，所述状态偏差信息还包括：位置偏差信息，所述位置偏差信息为换道过程中目标交通工具所在位置与所述目标换道路径中预设位置点之间的位置偏差；
所述判断所述状态偏差信息是否满足预设的换道成功条件，还包括：
若所述速度偏差信息在所述预设速度偏差范围内，判断所述位置偏差信息是否在所述换道成功条件中的预设位置偏差范围内；
若所述位置偏差信息不在所述预设位置偏差范围内，则确定所述位置偏差信息不满足所述换道成功条件。
在一实施例中，所述方法还包括：
确定所述目标换道路径的执行比例以及针对所述目标车道的历史换道规划次数；
若所述执行比例小于预设比例，且重新规划次数小于次数阈值，则重新进行换道路径规划。
第二方面，本申请实施例还提供了一种换道路径规划装置，包括：获取模块、生成模块、坐标转换模块；
所述获取模块，用于获取目标交通工具在行驶过程中的道路信息，所述道路信息包括：当前车道、目标车道以及道路属性；
所述生成模块，用于根据所述道路信息以及所述目标交通工具的行驶状态信息，生成车道坐标系下的预换道路径；
所述坐标转换模块，用于将所述预换道路径映射到地图坐标系下，得到目标换道路径；所述目标换道路径用于引导所述目标交通工具从所述当前车道切换至所述目标车道。
第三方面，本申请实施例还提供了一种智能驾驶汽车，包括：换道路径规划装置，智能驾驶汽车和总线，所述换道路径规划装置存储有换道路径规划方法，当智能驾驶汽车运行时，所述智能驾驶汽车与所述换道路径规划装置之间通过总线通信，所述换道路径规划装置执行程序指令，以执行时执行如第一方面任一所述的换道路径规划方法的步骤。
第四方面，本申请实施例还提供了一种计算机可读存储介质，所述存储介质上存储有计算机程序，所述计算机程序被处理器运行时执行如第一方面任一所述的换道路径规划方法的步骤。
本申请的有益效果是：本申请实施例提供一种换道路径规划方法，在获取目标交通工具在行驶过程中的道路信息后，根据道路信息以及目标交通工具的行驶状态信息，生成车道坐标系下的预换道路径；将预换道路径映射到地图坐标系下，得到目标换道路径，该目标换道路径可以用于引导目标交通工具进行换道。一方面，实现了在不同车速、不同车道宽度、不同道路曲率下安全、舒适且高效地智能换道。另一方面，在换道中，由于换道压缩比可调，与传统换路径方法相比，在保证切向连续、曲率连续基础上实现轨迹缓急可调，可配置私人化换道风格；换道轨迹平缓，时间空间一致性好。此外，本申请的换道路径规划方法基于车道坐标系进行预换道路径规划，降低换道路径规划复杂度、时效性高、算力需求小、场景适应强，可广泛应用于目标交通工具辅助换道路径规划中。
附图说明
为了更清楚地说明本申请实施例的技术方案，下面将对实施例中所需要使用的附图作简单地介绍，应当理解，以下附图仅示出了本申请的某些实施例，因此不应被看作是对范围的限定，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他相关的附图。
图1为本申请一实施例提供的一种换道路径规划方法的流程图；
图2为本申请又一实施例提供的一种换道路径规划方法的流程图；
图3为本申请一实施例提供的一种相同条件下不同换道压缩比的换道轨迹示意图；
图4为本申请另一实施例提供的一种换道路径规划方法的流程图；
图5为本申请再一实施例提供的一种换道路径规划方法的流程图；
图6为本申请再二实施例提供的一种换道路径规划方法的流程图；
图7为本申请再三实施例提供的一种换道路径规划方法的流程图；
图8为本申请再四实施例提供的一种换道路径规划方法的流程图；
图9为本申请一实施例提供的一种换道轨迹重规划位置示意图；
图10为本申请一实施例提供的一种换道轨迹重规划航向示意图；
图11为本申请一实施例提供的一种换道轨迹重规划曲率示意图；
图12为本申请一实施例提供的一种换道路径规划装置的示意图；
图13为本申请实施例提供的一种智能驾驶汽车的示意图。
具体实施方式
为使本申请实施例的目的、技术方案和优点更加清楚，下面将结合本申请实施例中的附图，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例是本申请一部分实施例，而不是全部的实施例。
在本申请中，除非另有明确的规定和限定，术语“第一”、“第二”仅用于描述目的，而不能理解为指示或暗示相对重要性或者隐含指明所指示的技术特征的数量。由此，限定有“第一”、“第二”的特征可以明示或者隐含地包含至少一个特征。在本申请中的描述中，“多个”的含义是至少两个，例如两个、三个，除非另有明确具体的限定。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、物品或者设备不仅包括那些要素，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、物品或者设备所固有的要素。在没有更多限制的情况下，由语句“包括一个……”限定的要素，并不排除在包括要素的过程、方法、物品或 者设备中还存在另外的相同要素。
作为汽车产业与人工智能、高性能计算平台等新一代信息技术深度融合的产物，智能驾驶是汽车产业的升级。智能驾驶技术包含多个方向，其中决策规划技术更是重中之重，主要涉及智能驾驶类人决策以及最优路径生成，体现智能驾驶技术的“高阶”。目前，主流L2级功能主要包含车道保持、自适应巡航以及自动泊车，鲜有涉及智能辅助换道功能，主要原因是换道场景复杂程度与挑战性远高于车道保持工况。
此外，目前主流的换道路径规划方法难以在换道急缓可调、曲率连续、时间空间一致性、不同车速、不同道路曲率、不同车道宽度及轨迹跟踪存在偏差下场景实现性能平衡。
针对目前的智能驾驶汽车场景需求，本申请实施例提供了多种可能的实现方式，以实现汽车在结构化道路高效、安全完成换道，减轻驾驶员在换道场景下心理与生理负担，提升换道工况下汽车行驶的安全性。如下结合附图通过多个示例进行解释说明。图1为本申请一实施例提供的一种换道路径规划方法的流程图，该互动控制方法可由运行有上述方法的电子设备实现，该电子设备例如可以为终端设备，也可以为服务器。如图1所示，该方法包括：
步骤101：获取目标交通工具在行驶过程中的道路信息，道路信息包括：当前车道、目标车道，以及道路属性。
需要说明的是，当前车道表示的是目标交通工具在行驶过程中当前所在的车道，可以基于安装在目标交通工具上的检测装置，例如摄像头、传感器等获取；也可以基于高精地图引导获取，本申请对此不做限定。当前车道除了包括车道位置信息之外，还可以包括目标交通工具相对于当前车道中心位置的偏移情况，以增强后续计算的准确性。
目标车道表示的是目标交通工具在行驶过程中换道的换道目标车道，目标车道可以由目标交通工具的驾驶人员或者智能控制设备选定，例如用户可以通过目标交通工具上的人机交互显示屏、目标交通工具上的换道操纵装置等进行目标车道选定；再例如，目标交通工具的智能控制设备在对附近的环境、车辆等信息进行分析后，可以智能选择目标车道，以实现避障或者快速通行。由于进行目标车道选定时，其选择的可以是相对于当前车道的偏移信息(例如目标车道选定为当前车道右侧一车道，或者左侧两车道等)，为了提高之后基于目标车道进行的计算的准确度，在选择后，可以基于安装在目标交通工具上的检测装置(例如摄像头、传感器等)，或者高精地图引导获取目标车道的具体位置。上述仅为示例说明，当前车道、目标车道的相关信息还可以有其他的获取方式，本申请对此不做限定。
道路属性表示的是目标交通工具在行驶过程中的道路的固有属性，例如车道的宽度、道路的曲率等信息，这些道路属性可以基于安装在目标交通工具上的检测装置(例如摄像头、传感器等)、高精地图引导等进行计算获取。上述仅为示例说明，在实际实现中，道路属性信息还可以有其他的获取方式，本申请对此不做限定，能够获取到道路属性信息即可。
步骤102：根据道路信息以及目标交通工具的行驶状态信息，生成车道坐标系下的预换道路径。
需要说明的是，目标交通工具的行驶状态信息是对目标交通工具的行驶状态的描述，例如可以包括车速、横摆角速度等。目标交通工具的行驶状态信息可以通过目标交通工具自车底盘信息获取，本申请对此类行驶状态信息 的具体获取方式不做限定。
还需要说明的是，车道坐标系描述了汽车相对于道路的位置，车道坐标系例如可以为弗莱纳坐标系(Frenet坐标系)，在Frenet坐标系中，以沿道路的距离为纵坐标(s)，以与纵向线的位移称为横坐标(d)。从而保证了在道路的每个点上，横轴和纵轴都是垂直的，纵坐标表示在道路中的行驶距离，横坐标表示汽车偏离中心线的距离。在Frenet坐标系下目标交通工具向前行驶，其在车道内的轨迹呈一条直线，从而大大简化轨迹规划的难度。
图2为本申请又一实施例提供的一种换道路径规划方法的流程图；如图2所示，根据道路信息以及目标交通工具的行驶状态信息，生成车道坐标系下的预换道路径，包括：
步骤201：根据道路信息以及目标交通工具的行驶状态信息，计算换道时间、换道压缩比以及换道区域边界。
在一种具体的实现方式中，步骤101中道路的平均曲率可以通过如下方式在Frenet坐标系进行计算，例如可以通过如下方式基于安装在目标交通工具上的检测装置或者高精地图等计算道路的曲率，首先在检测装置检测范围内划定预设广度的范围；或者在高精地图上以目标交通工具为基准，以目标交通工具的行进方向为延伸划定预设广度的范围。其中，预设广度的范围可以通过采样时间的上下边界进行计算，例如用s min、s max分别代表在目标交通工具的行进方向上预设广度的范围(s方向上)的上边界、下边界，t min和t max分别代表采样时间的上下边界，其中：
s max＝t max*v；
s min＝t min*v；
v为目标交通工具在Frenet坐标系下的速度。
通过上述方式，可以确定一个预设广度的范围内的道路的曲率，本领域技术人员可以理解，当采样时间的上下边界趋于无限小时，计算得到的道路的曲率越来越接近于预设广度的范围内一个路径曲率点的曲率。同样地，重复使用上述公式可以确定道路上其他路径曲率点的道路的曲率，进而可以计算得到道路的平均曲率：
Figure PCTCN2022102379-appb-000001
其中，kappa avg代表目标道路曲率，n代表计算路径曲率点的总个数，kappa i代表纵向距离为i的点曲率。
得到道路的平均曲率后，对换道时间进行计算，例如可以采用如下方法进行计算：
预先设定换道时间数据集T n，此换道时间数据集T n表示在不同速度下换道时间，根据换道时间数据集T n计算换道时间t lc：
t lc＝linearInterpolation(T n,v)+k kappa*v 2*kappa avg；
其中，k kappa为常系数，linearInterpolation为与一阶线性插值函数，输入为换道时间数据集T n和目标交通工具在Frenet坐标系下的速度v。
下面，计算换道压缩比，所谓换道压缩比表示的是换道路径前后半程的急缓程度，图3为本申请一实施例提供的一种相同条件下不同换道压缩比的换道轨迹示意图，图3中三条首尾重合的曲线的换道条件相同，例如：换道起点和换道终点相同，换道车速相同等，在此情况下，通过调整换道压缩比可以看出其换道轨迹存在差异，设定位于中间的曲线的换道压缩比为0.5，表示在这种情况下换道路径前半程的急缓程度与后半程的急缓程度相同，因此从图中可以看出前半程与后半程基本呈现中心对称关系；中间曲线上方曲线的换道压缩比大于0.5，表示相较于中间曲线而言，其先急后缓的程度越大，从图3最上方曲线可以明显看出这种路径下换道时前半程换道急，后半程平缓；同样地，中间曲线下方曲线的换道压缩比小于0.5，表示相较于中间曲线而言，其先急后缓的程度越小，从图3最下方曲线可以明显看出这种路径下换道时前半程换道平缓，后半程急。为了说明换道压缩比的具体含义，上述实现方式中对换道压缩比的取值范围进行了归一化设定，使其在0-1之间，但是在其他实现方式中，换道压缩比的取值还可以有其他形式，本申请对此不做限定，只要其能够体现换道路径前后半程的急缓程度即可。需要说明的是，换道压缩比可以由用户进行设定，例如通过人际交互显示屏设定或者通过目标交通工具的操纵杆进行设定等，也可以通过目标交通工具的控制系统根据日常使用习惯等情况进行智能化设定，本申请对此不做限定。
具体的，可以通过以下方式计算换道压缩比，图4为本申请另一实施例提供的一种换道路径规划方法的流程图；如图4所示，根据道路信息以及目标交通工具的行驶状态信息，计算换道压缩比之前，方法还包括：
步骤401：获取目标交通工具航向。
根据道路信息以及目标交通工具的行驶状态信息，计算换道压缩比，包括：
步骤402：根据道路信息、行驶状态信息以及航向，计算换道压缩比。
ratio＝a+k 0*v 2*kappa avg+k 1*heading 0；
其中，heading 0表示的是航向，可以基于安装在目标交通工具上的检测装置、高精地图引导等方式获取，a表示的是ratio常数项，k 0表示的是二次系数常数项，k 1表示的是一次系数常数项。
接着设定换道区域边界，即换道轨迹的首尾点约束(l 0,dl 0,ddl 0,dddl 0)和(l 1,dl 1,ddl 1,dddl 1)，其中，l 0、dl 0、ddl 0、dddl 0分别代表frenet坐标系中换道轨迹首位置点的l轴偏差(目标交通工具与当前车道中心线之间的侧向偏差)、偏差一阶导、偏差二阶导和偏差三阶导，l 1(目标交通工具的目标路径末位置与目标车道中心线之间的侧向偏差)、dl 1、ddl 1、dddl 1分别代表frenet坐标系中换道轨迹末位置点的l轴偏差、偏差一阶导、偏差二阶导和偏差三阶导。
在一实施例中，还可以计算得到换道轨迹长度p：p＝t lc*v。
步骤202：根据换道时间、换道区域边界、行驶状态信息中的第一车辆行驶速度，以及换道压缩比，生成车道坐标系下的预换道路径。
获取上述信息后，由首尾点约束、轨迹长度、换道压缩比求解换道轨迹，具体如下：
设置求解目标七阶多项式系数集合为：
Coeff＝[c 0,c 1,c 2,c 3,c 4,c 5,c 6,c 7]；
其中，c 0,c 1,c 2,c 3,c 4,c 5,c 6,c 7依次从低阶到高阶，并设置中间控制点，中间控制点的距离通过换道轨迹长度与换道压缩比决定，中间控制点的距离p m＝p*ratio；
轨迹求解矩阵如下：
Figure PCTCN2022102379-appb-000002
通过上述方法，生成车道坐标系下的预换道路径，由上述方法可知，通过调整换道压缩比可以实现换道轨迹零阶、一阶、二阶连续同时轨迹急缓可调。
步骤103：将预换道路径映射到地图坐标系下，得到目标换道路径；目标换道路径用于引导目标交通工具从当前车道切换至目标车道。
需要说明的是，地图坐标系是对地图信息的描述，地图坐标系例如可以为笛卡尔坐标系(Cartesian coordinates)，笛卡尔坐标系是由相交于原点的两条数轴，构成了平面仿射坐标系，通常，我们习惯使用地图坐标系来定义空间点的位置。因此，在车道坐标系中规划好预换道路径后，可以通过将预换道路径映射到地图坐标系下，得到目标换道路径，进而通过目标换道路径用于引导目标交通工具从当前车道切换至目标车道。
在一种具体的实现方式中，可以将求解后的车道坐标系下的预换道路径映射至地图坐标系下并实时进行航位推算(Dead Reckoning，DR推算)，将推算结果下发至目标交通工具的控制层。上述仅为示例说明，在实际实现中还可以有其他方式，本申请对此不做限定。
综上，本申请实施例提供一种换道路径规划方法，在获取目标交通工具在行驶过程中的道路信息后，根据道路信息以及目标交通工具的行驶状态信息，生成车道坐标系下的预换道路径；将预换道路径映射到地图坐标系下，得到目标换道路径，该目标换道路径可以用于引导目标交通工具进行换道。一方面，实现了在不同车速、不同车道宽度、不同道路曲率下安全、舒适且高效地智能换道。另一方面，在换道中，由于换道压缩比可调，与传统换路 径方法相比，在保证切向连续、曲率连续基础上实现轨迹缓急可调，可配置私人化换道风格；换道轨迹平缓，时间空间一致性好。此外，本申请的换道路径规划方法基于车道坐标系进行预换道路径规划，降低换道路径规划复杂度、时效性高、算力需求小、场景适应强，可广泛应用于目标交通工具辅助换道路径规划中。
在一实施例中，在上述图1的基础上，本申请还提供一种换道路径规划方法的可能实现方式，图5为本申请再一实施例提供的一种换道路径规划方法的流程图；如图5所示，将预换道路径映射到地图坐标系下，得到目标换道路径之后，该方法还包括：
步骤501：获取目标交通工具在基于目标换道路径的行驶过程中的状态偏差信息。
得到目标换道路径之后，目标交通工具在基于目标换道路径行驶过程中，存在一些突发情况，例如目标换道路径上出现障碍物(车辆、行人、物体等)，再例如在获取当前车道信息时，对目标交通车辆在当前车道上的具体位置定位存在偏差(例如在计算中的侧向偏差存在误差)，再例如获取目标交通工具的行驶状态信息时传感器等存在的仪器误差等，可能造成换道形式过程中出现状态偏差。状态偏差信息例如可以为速度偏差、位置偏差等，本申请对此不做限定，只要该偏差信息能够对目标交通工具的换道提供准确度或者安全性参考即可。
步骤502：判断状态偏差信息是否满足预设的换道成功条件。
在一种可能的实现方式中，若状态偏差信息在一定的范围内是可以认为换道行驶过程合理的，因此需要对此进行进一步判定，根据预设的换道成功条件对状态偏差信息进行判断，若状态偏差信息满足预设的换道成功条件，则可以基于此判断目前目标交通工具的换道行驶正常，不需要进行介入修正，只需要继续对此进行监测判断即可。
步骤503：若状态偏差信息不满足换道成功条件，重新获取目标交通工具在行驶过程中的道路信息，以重新生成目标换道路径。
若状态偏差信息不满足换道成功条件，说明若目标交通工具继续按照当前状态进行换道行驶，则换道可能不成功或者存在安全隐患等问题，因此需要重新获取目标交通工具在行驶过程中的道路信息，按照上述步骤中的换道路径规划方法重新生成目标换道路径。
通过对行驶状态信息进行判断，当状态偏差信息不满足换道成功条件时进行重新规划，增强了换道路径规划方法的安全性。
在一实施例中，在上述图5的基础上，本申请还提供一种换道路径规划方法的可能实现方式，图6为本申请再二实施例提供的一种换道路径规划方法的流程图；如图6所示，状态偏差信息包括：速度偏差信息；速度偏差信息为换道过程中的行驶状态信息中的第二车辆行驶速度与标准行驶速度的偏差；判断状态偏差信息是否满足预设的换道成功条件，包括：
步骤601：判断速度偏差信息，是否在换道成功条件中的预设速度偏差范围内。
通过对上述实施例中目标换道路径的生成计算过程可以看出，本申请对换道路径的规划很大程度上是与目标交通工具的行驶速度相关的，极小的速度变化可能不会对换道成功与否带来影响，但是，若换道行驶速度与规划时的速度之间的差异太大时，对换道成功会带来影响，因此需要对第二车辆行 驶速度与标准行驶速度的偏差进行判断，判断第二车辆行驶速度与标准行驶速度的偏差是否在换道成功条件中的预设速度偏差范围内。需要说明的是，标准行驶速度可以是在进行换道路径规划时目标交通工具的行驶速度，也可是与换道路径规划时目标交通工具的行驶速度对应设置的标准速度(例如以换道路径规划时目标交通工具的行驶速度为中心的速度范围等)，本申请对此不做限定。
在一种具体的实现方式中，预设速度偏差阈值，若第二车辆行驶速度与标准行驶速度的偏差的绝对值小于等于预设速度偏差阈值，则表示速度偏差信息在预设速度偏差范围内。上述仅为示例说明，在实际实现中，对速度偏差信息还可以有其他判断方式，本申请对此不做限定。
步骤602：若速度偏差信息不在预设速度偏差范围内，则确定速度偏差信息不满足换道成功条件。
在一种具体的实现方式中，预设速度偏差阈值，若第二车辆行驶速度与标准行驶速度的偏差的绝对值大于预设速度偏差阈值，则表示速度偏差信息不在预设速度偏差范围内。上述仅为示例说明，在实际实现中，对速度偏差信息还可以有其他判断方式，本申请对此不做限定。
通过上述方法实现对速度状态异常的判断。
在一实施例中，在上述图6的基础上，本申请还提供一种换道路径规划方法的可能实现方式，图7为本申请再三实施例提供的一种换道路径规划方法的流程图；如图7所示，状态偏差信息还包括：位置偏差信息，位置偏差信息为换道过程中目标交通工具所在位置与目标换道路径中预设位置点之间的位置偏差；判断状态偏差信息是否满足预设的换道成功条件，还包括：
步骤701：若速度偏差信息在预设速度偏差范围内，判断位置偏差信息是否在换道成功条件中的预设位置偏差范围内。
对速度偏差信息进行判断，若速度偏差信息在预设速度偏差范围内，则保证了目标交通工具的速度状态的正常，但是速度正常仍不能保证换道的成功，因为在车辆换道中由于各种位置偏差仍可能存在换道不成功的问题，因此需要对位置偏差信息进行进一步判断，位置偏差信息即为换道过程中目标交通工具所在位置与其按照目标换道路径引导下当前应处的目标换道路径位置中的预设位置点之间的位置偏差。
极小的位置偏差可能不会对换道成功与否带来影响，但是，若换道行驶的当前位置与预设位置点之间的位置偏差太大时，对换道成功会带来影响，因此需要对换道过程中目标交通工具所在位置与目标换道路径中预设位置点的位置偏差进行判断，判断换道过程中目标交通工具所在位置与目标换道路径中预设位置点之间的位置偏差信息是否在换道成功条件中的预设位置偏差范围内。需要说明的是，预设位置点可以是在目标换道路径的一个确切位置点，也可以是以位置点位中心的坐标区域，本申请对此不做限定。
在一种具体的实现方式中，预设位置偏差阈值，若换道过程中目标交通工具所在位置与目标换道路径中预设位置点的偏差的绝对值小于等于预设位置偏差阈值，则表示位置偏差信息在预设位置偏差范围内。上述仅为示例说明，在实际实现中，对位置偏差信息还可以有其他判断方式，本申请对此不做限定。
步骤702：若位置偏差信息不在预设位置偏差范围内，则确定位置偏差信息不满足换道成功条件。
在一种具体的实现方式中，预设位置偏差阈值err_thres，若换道过程中目标交通工具所在位置与目标换道路径中预设位置点的偏差的绝对值大于err_thres，则表示位置偏差信息不在预设位置偏差范围内。上述仅为示例说明，在实际实现中，对速度偏差信息还可以有其他判断方式，本申请对此不做限定。
在一实施例中，在上述图5基础上，本申请还提供一种换道路径规划方法的可能实现方式，图8为本申请再四实施例提供的一种换道路径规划方法的流程图；如图8所示，该方法包括：
步骤801：确定目标换道路径的执行比例以及针对目标车道的历史换道规划次数。
状态偏差信息不满足换道成功条件时，对目标换道路径的执行比例进行判断，目标换道路径的执行比例表示目前已完成的目标换道路径与目标换道路径总路径之间的比值，目标换道路径的执行比例越高，表示目前已完成的目标换道路径越多，可以理解，当目标换道路径的执行比例达到一定的预设比例时，可以认为此次换道已完成，因此不需要再进行重规划。
目标换道路径的执行比例小于预设比例时，对针对目标车道的历史换道规划次数进行判断，可以理解，一般换道情况下经过一定次数的规划即可完成换道，但是多次规划均无法完成时，可以认为当前道路存在其他意外因素，或者目标交通工具可能存在故障等，继续进行重规划不仅难以实现换道，还可能对行车安全造成负面影响，因此需要对针对目标车道的历史换道规划次数进行判断。
步骤802：若执行比例小于预设比例，且重新规划次数小于次数阈值，则重新进行换道路径规划；
若执行比例小于预设比例，且重新规划次数小于次数阈值，则重新进行换道路径规划；
若执行比例大于预设比例，则认为本次换道已完成，无需重新进行换道路径规划；
若执行比例小于预设比例，但重新规划次数大于次数阈值，则认为目前状态异常，不能重新进行换道路径规划。
在重新进行换道路径规划时，可以依据实时DR推算，匹配当前位置与上一周期换道规划目标换道路径中的轨迹点，将其设为当前规划的换道轨迹的起始点约束(即上述步骤中的(l 0,dl 0,ddl 0,dddl 0))，同时依据上一周期换道规划目标换道路径的执行比例，确定目标终点及终点约束，再次生成车道坐标系下的预换道路径，图9为本申请一实施例提供的一种换道轨迹重规划位置示意图；如图9所示，换道在执行比例50％处因速度差较大执行重新规划。图10为本申请一实施例提供的一种换道轨迹重规划航向示意图；图11为本申请一实施例提供的一种换道轨迹重规划曲率示意图；如图10、图11所示，重规划轨迹横向偏差、切向、曲率连续，以有效保障换道过程平稳舒适。
采用如上方法，可实现换道轨迹连续、平滑，同时换道轨迹急缓可调；设计换道轨迹重规划机制，在特殊场景下(跟踪偏差较大等)仍能保证换道轨迹连续；保证换道功能的舒适、平缓、安全与时间一致性。
下述对用以执行本申请所提供的换道路径规划装置、电子设备及存储介质等进行说明，其具体的实现过程以及技术效果参见上述，下述不再赘述。
本申请实施例提供一种换道路径规划装置的可能实现示例，能够执行上 述实施例提供的换道路径规划方法。图12为本申请一实施例提供的一种换道路径规划装置的示意图。如图12所示，上述换道路径规划装置100，包括：获取模块121、生成模块123、坐标转换模块125；
获取模块121，用于获取目标交通工具在行驶过程中的道路信息，道路信息包括：当前车道、目标车道以及道路属性；
生成模块123，用于根据道路信息以及目标交通工具的行驶状态信息，生成车道坐标系下的预换道路径；
坐标转换模块125，用于将预换道路径映射到地图坐标系下，得到目标换道路径；目标换道路径用于引导目标交通工具从当前车道切换至目标车道。
在一实施例中，生成模块123，用于根据道路信息以及目标交通工具的行驶状态信息，计算换道时间、换道压缩比以及换道区域边界；根据换道时间、换道区域边界、行驶状态信息中的第一车辆行驶速度，以及换道压缩比，生成车道坐标系下的预换道路径。
在一实施例中，获取模块121，用于获取目标交通工具航向；
生成模块123，用于根据道路信息、行驶状态信息以及航向，计算换道压缩比。
在一实施例中，换道路径规划装置100，还包括：判断模块；
获取模块121，用于获取目标交通工具在基于目标换道路径的行驶过程中的状态偏差信息；
判断模块，用于判断状态偏差信息是否满足预设的换道成功条件；若状态偏差信息不满足换道成功条件，重新获取目标交通工具在行驶过程中的道路信息，以重新生成目标换道路径。
在一实施例中，状态偏差信息包括：速度偏差信息；速度偏差信息为换道过程中的行驶状态信息中的第二车辆行驶速度与标准行驶速度的偏差；
判断模块，用于判断速度偏差信息，是否在换道成功条件中的预设速度偏差范围内；若速度偏差信息不在预设速度偏差范围内，则确定速度偏差信息不满足换道成功条件。
在一实施例中，状态偏差信息还包括：位置偏差信息，位置偏差信息为换道过程中目标交通工具所在位置与目标换道路径中预设位置点之间的位置偏差；
判断模块，用于若速度偏差信息在预设速度偏差范围内，判断位置偏差信息是否在换道成功条件中的预设位置偏差范围内；若位置偏差信息不在预设位置偏差范围内，则确定位置偏差信息不满足换道成功条件。
在一实施例中，判断模块，用于确定目标换道路径的执行比例以及针对目标车道的历史换道规划次数；若执行比例小于预设比例，且重新规划次数小于次数阈值，则重新进行换道路径规划。
上述装置用于执行前述实施例提供的方法，其实现原理和技术效果类似，在此不再赘述。
以上这些模块可以是被配置成实施以上方法的一个或多个集成电路，例如：一个或多个特定集成电路(Application Specific Integrated Circuit，简称ASIC)，或，一个或多个微处理器(digital signal processor，简称DSP)，或，一个或者多个现场可编程门阵列(Field Programmable Gate Array，简称FPGA)等。再如，当以上某个模块通过处理元件调度程序代码的形式实现时，该处理元件可以是通用处理器，例如中央处理器(Central Processing Unit，简称 CPU)或其它可以调用程序代码的处理器。再如，这些模块可以集成在一起，以片上系统(system-on-a-chip，简称SOC)的形式实现。
本申请实施例提供一种智能驾驶汽车的可能实现示例，能够执行上述实施例提供的换道路径规划方法。图13为本申请实施例提供的一种智能驾驶汽车的示意图，该智能驾驶汽车包括：换道路径规划装置131、智能驾驶汽车132和总线，换道路径规划装置131可执行的程序指令，运行时，换道路径规划装置131与智能驾驶汽车132之间通过总线通信，换道路径规划装置131执行程序指令，以执行时执行上述换道路径规划方法的步骤。具体实现方式和技术效果类似，这里不再赘述。
本申请实施例提供一种计算机可读存储介质的可能实现示例，能够执行上述实施例提供的换道路径规划方法，存储介质上存储有计算机程序，计算机程序被处理器运行时执行上述换道路径规划方法的步骤。
存储在一个存储介质中的计算机程序，可以包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)或处理器(英文：processor)执行本申请各个实施例方法的部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(英文：Read-Only Memory，简称：ROM)、随机存取存储器(英文：Random Access Memory，简称：RAM)、磁碟或者光盘等各种可以存储程序代码的介质。
在本申请所提供的几个实施例中，应该理解到，所揭露的装置和方法，可以通过其它的方式实现。例如，以上所描述的装置实施例仅仅是示意性的，例如，单元的划分，仅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，例如多个单元或组件可以结合或者可以集成到另一个系统，或一些特征可以忽略，或不执行。另一点，所显示或讨论的相互之间的耦合或直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性，机械或其它的形式。
作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的部件可以是或者也可以不是物理单元，即可以位于一个地方，或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例方案的目的。
另外，在本申请各个实施例中的各功能单元可以集成在一个处理单元中，也可以是各个单元单独物理存在，也可以两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，也可以采用硬件加软件功能单元的形式实现。
上述以软件功能单元的形式实现的集成的单元，可以存储在一个计算机可读取存储介质中。上述软件功能单元存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可以是个人计算机，服务器，或者网络设备等)或处理器(英文：processor)执行本申请各个实施例方法的部分步骤。而前述的存储介质包括：U盘、移动硬盘、只读存储器(英文：Read-Only Memory，简称：ROM)、随机存取存储器(英文：Random Access Memory，简称：RAM)、磁碟或者光盘等各种可以存储程序代码的介质。
以上仅为本申请的具体实施方式，但本申请的保护范围并不局限于此，任何熟悉本技术领域的技术人员在本申请揭露的技术范围内，可轻易想到变化或替换，都应涵盖在本申请的保护范围之内。因此，本申请的保护范围应以权利要求的保护范围为准。

Claims (10)

  1. 一种换道路径规划方法，其中，包括：
    获取目标交通工具在行驶过程中的道路信息，所述道路信息包括：当前车道、目标车道，以及道路属性；
    根据所述道路信息以及所述目标交通工具的行驶状态信息，生成车道坐标系下的预换道路径；
    将所述预换道路径映射到地图坐标系下，得到目标换道路径；所述目标换道路径用于引导所述目标交通工具从所述当前车道切换至所述目标车道。
  2. 如权利要求1所述的方法，其中，所述根据所述道路信息以及所述目标交通工具的行驶状态信息，生成车道坐标系下的预换道路径，包括：
    根据所述道路信息以及所述目标交通工具的行驶状态信息，计算换道时间、换道压缩比以及换道区域边界；
    根据所述换道时间、所述换道区域边界、所述行驶状态信息中的第一车辆行驶速度，以及所述换道压缩比，生成车道坐标系下的所述预换道路径。
  3. 根据权利要求2所述的方法，其中，所述根据所述道路信息以及所述目标交通工具的行驶状态信息，计算换道压缩比之前，所述方法还包括：
    获取所述目标交通工具航向；
    所述根据所述道路信息以及所述目标交通工具的行驶状态信息，计算换道压缩比，包括：
    根据所述道路信息、所述行驶状态信息以及所述航向，计算所述换道压缩比。
  4. 根据权利要求1所述的方法，其中，所述将所述预换道路径映射到地图坐标系下，得到目标换道路径之后，所述方法还包括：
    获取所述目标交通工具在基于所述目标换道路径的行驶过程中的状态偏差信息；
    判断所述状态偏差信息是否满足预设的换道成功条件；
    若所述状态偏差信息不满足所述换道成功条件，重新获取所述目标交通工具在行驶过程中的道路信息，以重新生成所述目标换道路径。
  5. 如权利要求4所述的方法，其中，所述状态偏差信息包括：速度偏差信息；所述速度偏差信息为换道过程中的所述行驶状态信息中的第二车辆行驶速度与标准行驶速度的偏差；
    所述判断所述状态偏差信息是否满足预设的换道成功条件，包括：
    判断所述速度偏差信息是否在所述换道成功条件中的预设速度偏差范围内；
    若所述速度偏差信息不在所述预设速度偏差范围内，则确定所述速度偏差信息不满足所述换道成功条件。
  6. 如权利要求5所述的方法，其中，所述状态偏差信息还包括：位置偏差信息，所述位置偏差信息为换道过程中目标交通工具所在位置与所述目标换道路径中预设位置点之间的位置偏差；
    所述判断所述状态偏差信息是否满足预设的换道成功条件，还包括：
    若所述速度偏差信息在所述预设速度偏差范围内，判断所述位置偏差 信息是否在所述换道成功条件中的预设位置偏差范围内；
    若所述位置偏差信息不在所述预设位置偏差范围内，则确定所述位置偏差信息不满足所述换道成功条件。
  7. 如权利要求4所述的方法，其中，所述方法还包括：
    确定所述目标换道路径的执行比例以及针对所述目标车道的历史换道规划次数；
    若所述执行比例小于预设比例，且重新规划次数小于次数阈值，则重新进行换道路径规划。
  8. 一种换道路径规划装置，其中，包括：获取模块、生成模块、坐标转换模块；
    所述获取模块，用于获取目标交通工具在行驶过程中的道路信息，所述道路信息包括：当前车道、目标车道以及道路属性；
    所述生成模块，用于根据所述道路信息以及所述目标交通工具的行驶状态信息，生成车道坐标系下的预换道路径；
    所述坐标转换模块，用于将所述预换道路径映射到地图坐标系下，得到目标换道路径；所述目标换道路径用于引导所述目标交通工具从所述当前车道切换至所述目标车道。
  9. 一种智能驾驶汽车，其中，包括：换道路径规划装置，智能驾驶汽车和总线，所述换道路径规划装置存储有换道路径规划方法，当智能驾驶汽车运行时，所述智能驾驶汽车与所述换道路径规划装置之间通过总线通信，所述换道路径规划装置执行程序指令，以执行时执行如权利要求1至7任一所述的换道路径规划方法的步骤。
  10. 一种计算机可读存储介质，其中，所述存储介质上存储有计算机程序，所述计算机程序被处理器运行时执行如权利要求1至7任一所述的换道路径规划方法的步骤。
PCT/CN2022/102379 2022-02-22 2022-06-29 换道路径规划方法、装置、智能驾驶汽车及存储介质 WO2023159839A1 (zh)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN202210159774.3 2022-02-22
CN202210159774.3A CN114407898B (zh) 2022-02-22 2022-02-22 换道路径规划方法、装置、智能驾驶汽车及存储介质

Publications (1)

Publication Number Publication Date
WO2023159839A1 true WO2023159839A1 (zh) 2023-08-31

Family

ID=81262591

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2022/102379 WO2023159839A1 (zh) 2022-02-22 2022-06-29 换道路径规划方法、装置、智能驾驶汽车及存储介质

Country Status (2)

Country Link
CN (1) CN114407898B (zh)
WO (1) WO2023159839A1 (zh)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117622147A (zh) * 2023-12-29 2024-03-01 上海保隆汽车科技股份有限公司 智能驾驶变道轨迹的生成方法、系统、电子设备及介质

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114407898B (zh) * 2022-02-22 2024-04-19 爱驰汽车（上海）有限公司 换道路径规划方法、装置、智能驾驶汽车及存储介质

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102017222563A1 (de) * 2017-12-13 2019-06-13 Robert Bosch Gmbh Verfahren und Vorrichtung zur Kommunikation zwischen mehreren Kraftfahrzeugen
CN111653113A (zh) * 2020-04-20 2020-09-11 浙江吉利汽车研究院有限公司 车辆的局部路径确定方法、装置、终端和存储介质
CN112673234A (zh) * 2020-01-17 2021-04-16 华为技术有限公司 路径规划方法和路径规划装置
CN114407898A (zh) * 2022-02-22 2022-04-29 爱驰汽车（上海）有限公司 换道路径规划方法、装置、智能驾驶汽车及存储介质

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR20200075915A (ko) * 2018-12-07 2020-06-29 현대자동차주식회사 차량의 주행 제어 장치 및 그 방법

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102017222563A1 (de) * 2017-12-13 2019-06-13 Robert Bosch Gmbh Verfahren und Vorrichtung zur Kommunikation zwischen mehreren Kraftfahrzeugen
CN112673234A (zh) * 2020-01-17 2021-04-16 华为技术有限公司 路径规划方法和路径规划装置
CN111653113A (zh) * 2020-04-20 2020-09-11 浙江吉利汽车研究院有限公司 车辆的局部路径确定方法、装置、终端和存储介质
CN114407898A (zh) * 2022-02-22 2022-04-29 爱驰汽车（上海）有限公司 换道路径规划方法、装置、智能驾驶汽车及存储介质

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117622147A (zh) * 2023-12-29 2024-03-01 上海保隆汽车科技股份有限公司 智能驾驶变道轨迹的生成方法、系统、电子设备及介质

Also Published As

Publication number Publication date
CN114407898A (zh) 2022-04-29
CN114407898B (zh) 2024-04-19

Similar Documents

Publication Publication Date Title
WO2023159839A1 (zh) 换道路径规划方法、装置、智能驾驶汽车及存储介质
CN112068545B (zh) 一种无人驾驶车辆在十字路口的行驶轨迹规划方法、系统及存储介质
EP3517893B1 (en) Path and speed optimization fallback mechanism for autonomous vehicles
US10571921B2 (en) Path optimization based on constrained smoothing spline for autonomous driving vehicles
US10591926B2 (en) Smooth road reference for autonomous driving vehicles based on 2D constrained smoothing spline
US10606277B2 (en) Speed optimization based on constrained smoothing spline for autonomous driving vehicles
WO2018176593A1 (zh) 一种面向无人自行车的局部避障路径规划方法
US10429849B2 (en) Non-linear reference line optimization method using piecewise quintic polynomial spiral paths for operating autonomous driving vehicles
CN113916246A (zh) 一种无人驾驶避障路径规划方法和系统
JP6997211B2 (ja) ポリゴンにおける中間点を低減する方法および装置
WO2019042295A1 (zh) 一种无人驾驶路径规划方法、系统和装置
WO2020125686A1 (zh) 实时相对地图的生成方法，智能驾驶设备以及计算机存储介质
WO2022257283A1 (zh) 车辆避障方法、装置、电子设备及存储介质
US11614740B2 (en) Determining speeds for an autonomous vehicle
US11449058B2 (en) Traveling track determination processing and automated drive device
WO2023232119A1 (zh) 一种车辆控制方法、装置、设备、介质及车辆
WO2023114221A1 (en) Autonomous vehicle trajectory generation using velocity-based steering limits
CN114527761A (zh) 一种基于融合算法的智能汽车局部路径规划方法
CN115140096A (zh) 一种基于样条曲线与多项式曲线的自动驾驶轨迹规划方法
CN117885764B (zh) 车辆的轨迹规划方法、装置、车辆及存储介质
WO2022001708A1 (zh) 车辆控制方法、装置、车辆及存储介质
Weisswange et al. Intelligent traffic flow assist: Optimized highway driving using conditional behavior prediction
CN114620071A (zh) 绕行轨迹规划方法、装置、设备及存储介质
CN116653963B (zh) 车辆变道控制方法、系统和智能驾驶域控制器
CN111649751A (zh) 一种用于参考线平滑的超自由缝合方法

Legal Events

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

Ref document number: 22928109

Country of ref document: EP

Kind code of ref document: A1