Path planning method and device

Abstract

The embodiment of the invention provides a path planning method and device, and the method comprises the steps: obtaining the environment information of a first region where a first vehicle is located, enabling the first region to be within the sensing range of the first vehicle, enabling the first region to comprise at least one lane, enabling the first vehicle to be located on a first lane in the at least one lane, and carrying out the planning of a path according to the environment information, determining transverse partition information of each transverse partition in a plurality of transverse partitions corresponding to each sampling position in the at least one sampling position, each sampling position in the at least one sampling position being located in front of the driving direction of the first vehicle and being different from the first vehicle in position distance, the width of each transverse partition is smaller than the width of the first lane, and finally, planning the driving path of the first vehicle according to the environment information and the transverse partition information of each transverse partition in the plurality of transverse partitions corresponding to each sampling position. As the width of the transverse partition is smaller than that of the lane, the method can improve the accuracy of the driving path of the first vehicle.

Description

Technical Field

[0001] The present invention relates to the field of autonomous driving technology, and in particular to a path planning method and device. Background Art

[0002] In the field of autonomous driving technology, autonomous driving vehicles need to plan their own paths based on the surrounding roads and obstacles on the roads, and drive according to the planned paths.

[0003] At present, the existing technology generally uses lanes as the granularity when performing path planning. The planned path is generally the lane in which the autonomous driving vehicle is to travel. Since the lane has a certain lane width, the accuracy of the path planned by the above method is relatively low. How to improve the accuracy of path planning is an urgent problem to be solved. Summary of the invention

[0004] The present application provides a path planning method and device to improve the accuracy of path planning.

[0005] In a first aspect, an embodiment of the present application provides a path planning method, the method comprising: obtaining environmental information of a first area where a first vehicle is located, the first area being within a perception range of the first vehicle and including at least one lane, the first vehicle being located on a first lane of the at least one lane, and then, based on the environmental information, determining lateral partition information of each of a plurality of lateral partitions corresponding to each sampling position in at least one sampling position, each sampling position in at least one sampling position being located in front of the driving direction of the first vehicle and having a different position distance from the first vehicle, and a width of each lateral partition being smaller than a width of the first lane, and finally, planning a driving path of the first vehicle based on the environmental information and the lateral partition information of each of a plurality of lateral partitions corresponding to each sampling position.

[0006] In this method, since the width of the lateral partition is smaller than the width of the lane, the driving path of the first vehicle planned based on the environment information and the lateral partition information of each of the multiple lateral partitions corresponding to each sampling position has a relatively high accuracy.

[0007] In a possible implementation manner, the lateral partition information includes a first offset distance of the lateral partition relative to the first vehicle in the lateral direction.

[0008] In a possible implementation, planning the driving path of the first vehicle according to the environmental information and the lateral partition information of each of the multiple lateral partitions corresponding to each sampling position may include: determining the partition prediction result of each of the multiple lateral partitions corresponding to each sampling position according to the environmental information and the lateral partition information of each of the multiple lateral partitions corresponding to each sampling position, the partition prediction result including a first predicted probability value and a first predicted deflection angle of each lateral partition relative to the driving direction of the first vehicle, and then planning the driving path of the first vehicle according to the partition prediction result of each of the multiple lateral partitions corresponding to each sampling position. Through this design, a method for planning the driving path of the first vehicle is provided, which determines the lateral partition with a better lateral position among the multiple lateral partitions based on the predicted probability value, and determines the angle at which the first vehicle passes through the lateral partition based on the deflection angle, so that the planned path is smoother.

[0009] In a possible implementation, planning the driving path of the first vehicle according to the partition prediction results of multiple lateral partitions corresponding to each sampling position may include: determining at least one target lateral partition corresponding to each sampling position according to the partition prediction results of multiple lateral partitions corresponding to each sampling position, and then planning the driving path of the first vehicle based on the lateral partition information of at least one target lateral partition corresponding to each sampling position. Through this design, a method for planning the driving path of the first vehicle is provided.

[0010] In a possible implementation, determining at least one target transverse partition corresponding to each sampling position according to the partition prediction result of each transverse partition among the multiple transverse partitions corresponding to each sampling position may include: taking the transverse partition with the largest first prediction probability value among the partition prediction results among the multiple transverse partitions corresponding to the first sampling position as the first target transverse partition corresponding to the first sampling position. In a possible implementation, at least one transverse partition that satisfies a preset partition relationship with the first target transverse partition may also be taken as the second target transverse partition. Through this design, a variety of methods for determining the target transverse partition are provided. By determining the target transverse partition, the path planning of the first vehicle is performed according to the partition prediction result of the target transverse partition, so that the planned path is more accurate.

[0011] In a possible implementation, planning the driving path of the first vehicle based on the lateral partition information of at least one target lateral partition corresponding to each sampling position may include: determining the position information and deflection angle information of the prediction point corresponding to each sampling position based on the lateral partition information of at least one target lateral partition corresponding to each sampling position, and then planning the driving path of the first vehicle based on the position information and deflection angle information of the prediction point corresponding to each sampling position. Through this design, a method for planning the driving path of the first vehicle is provided.

[0012] In a possible implementation, the environmental information includes road topology information and traffic participant information.

[0013] In a possible implementation manner, the multiple lateral partitions corresponding to each sampling position are obtained by segmenting at least one lane of each sampling position in a lateral direction relative to the first vehicle.

[0014] In a second aspect, an embodiment of the present application provides a path planning device, which has the function of implementing the method example of the first aspect. The function can be implemented by hardware, or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above functions. In a possible design, the path planning device includes an acquisition module and a processing module. The acquisition module is used to acquire environmental information of a first area where a first vehicle is located, the first area is within the perception range of the first vehicle, and the first area includes at least one lane, and the first vehicle is located in the first lane of the at least one lane. The processing module is used to determine the lateral partition information of each lateral partition in the multiple lateral partitions corresponding to each sampling position in at least one sampling position according to the acquired environmental information, each sampling position in the at least one sampling position is located in front of the driving direction of the first vehicle and has a different position distance from the first vehicle, and the width of each lateral partition is less than the width of the first lane; the processing module is also used to plan the driving path of the first vehicle according to the environmental information and the lateral partition information of each lateral partition in the multiple lateral partitions corresponding to each sampling position.

[0015] The path planning device can perform the corresponding functions of the method example of the first aspect above. The beneficial effects can be found in the description of the first aspect and will not be repeated here.

[0016] In a third aspect, the present application provides a path planning device, which includes a processor and a memory, wherein the memory is used to store computer program instructions. Optionally, the processor is coupled to the memory. When the processor executes the computer program instructions, the path planning device executes the method in the above-mentioned first aspect or any possible implementation of the first aspect. In one example, the path planning device also includes a transceiver, and the transceiver can be a transceiver device with integrated data transceiver function. In another example, the transceiver can also be composed of a transmitter and a receiver, wherein the transmitter is used to send data and the receiver is used to receive data.

[0017] In a fourth aspect, the present application provides a possible path planning system, the path planning system comprising the path planning device of the second aspect or the third aspect, illustratively, the number of the path planning devices included in the path planning system may be one or more, and the present application does not limit this. The path planning system may be deployed on a vehicle.

[0018] In a fifth aspect, the present application also provides a vehicle including the above-mentioned path planning device or the above-mentioned path planning system.

[0019] In a sixth aspect, the present application provides a computer program product, which includes computer program instructions. When the computer program instructions are executed on a computer, the computer executes the method in the above-mentioned first aspect or any possible implementation manner of the first aspect.

[0020] In the seventh aspect, the present application provides a computer-readable storage medium, which stores computer program instructions. When the computer program instructions are executed by a computer, the computer executes the method in the above-mentioned first aspect or any possible implementation of the first aspect.

[0021] In an eighth aspect, the present application further provides a chip, which is coupled to a memory, and is used to read computer program instructions stored in the memory to execute a method in any possible implementation of the first aspect above.

[0022] In a ninth aspect, the present application further provides a chip system, which includes a processor for supporting a computer device to implement the method in the first aspect or any possible implementation of the first aspect. In a possible implementation, the chip system also includes a memory for storing program instructions and data necessary for the computer device. The chip system may be composed of a chip, or may include a chip and other discrete devices.

[0023] These and other aspects of the present application will become more apparent from the description of the following embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 A schematic diagram of a system structure provided for an embodiment of the present application;

[0025] Figure 2 A flowchart of a method for training a partition prediction model provided in an embodiment of the present application;

[0026] Figure 3 A flowchart of a method for obtaining first sample data provided in an embodiment of the present application;

[0027] Figure 4 A schematic diagram of the structure of a sample area provided in an embodiment of the present application;

[0028] Figure 5 A schematic diagram of the structure of a sample area provided in an embodiment of the present application;

[0029] Figure 6 A schematic diagram of a lane segmentation structure provided in an embodiment of the present application;

[0030] Figure 7a A schematic diagram of a lane segmentation structure provided in an embodiment of the present application;

[0031] Figure 7b A schematic diagram of a lane segmentation structure provided in an embodiment of the present application;

[0032] Figure 8 A schematic diagram of the structure of a sample area provided in an embodiment of the present application;

[0033] Fig. 9 A schematic diagram of the structure of a first sampling position provided in an embodiment of the present application;

[0034] Fig.10 A flowchart of a method for determining horizontal partition information provided in an embodiment of the present application;

[0035] Fig.11a A schematic diagram of the structure of a segmentation point provided in an embodiment of the present application;

[0036] Fig.11b A schematic diagram of the structure of a segmentation point provided in an embodiment of the present application;

[0037] Fig.12 A schematic diagram of the structure of a first deflection angle label provided in an embodiment of the present application;

[0038] Fig.13 A schematic diagram of the structure of a partition prediction model provided in an embodiment of the present application;

[0039] Fig.14A schematic diagram of the structure of a partition prediction model provided in an embodiment of the present application;

[0040] Fig.15 A schematic diagram of the structure of a partition prediction model and a loss function calculation model provided in an embodiment of the present application;

[0041] Fig.16 A schematic diagram of a flow chart of a path planning method provided in an embodiment of the present application;

[0042] Fig.17 A schematic diagram of a process flow of a method for planning a driving route provided in an embodiment of the present application;

[0043] Fig.18 A schematic diagram of a process flow of a method for planning a driving route provided in an embodiment of the present application;

[0044] Fig.19 A flowchart of a method for determining location information of a prediction point provided in an embodiment of the present application;

[0045] Fig. 20 A schematic diagram of a prediction point structure provided in an embodiment of the present application;

[0046] Fig.21 A schematic diagram showing a possible path planning device provided by an embodiment of the present application is exemplified;

[0047] Fig. 22 A schematic diagram of the structure of a possible path planning device provided in an embodiment of the present application is exemplified. DETAILED DESCRIPTION

[0048] The technical solutions in the embodiments of the present application will be described below in conjunction with the drawings in the embodiments of the present application.

[0049] Figure 1 This is a schematic diagram of an autonomous driving system that can be applied in an embodiment of the present application, including an environment perception module and a path planning module, wherein the path planning module is a core module for path planning in an embodiment of the present application.

[0050] Environmental perception module: This module is used to perceive the state of the surrounding environment and obtain the current state of the driving environment of the vehicle. Specifically, it integrates the information transmitted by various sensors to judge the state of the vehicle, including the position, speed, deflection angle, etc. of the vehicle; judges the state of the surrounding environment, including the position of static targets (lane boundaries, etc.), the position and speed of dynamic targets (social vehicles and pedestrians, etc.), and transmits this information to the path planning module.

[0051] Path planning module: plans a drivable path for the vehicle based on the status of the vehicle and other vehicles.

[0052] The driving decision-making solution provided in the embodiment of the present application can be applied to applications such as Figure 1 In the subsequent description of the embodiments of the present application, the vehicle terminal, vehicle computing platform, domain controller, and autonomous driving vehicle of the autonomous driving system shown in FIG. Figure 1 The autonomous driving system shown is illustrated by taking the autonomous driving vehicle as an example.

[0053] In an embodiment of the present application, a training method for a partition prediction model is provided, wherein the first vehicle is an autonomous driving vehicle with an autonomous driving system. Figure 2 ,The specific training process of the partition prediction model is as follows:

[0054] Step 201: A first vehicle obtains at least one set of first sample data.

[0055] In the embodiment of the present application, for any set of first sample data, the following can be used: Figure 3 The acquisition method flow shown is obtained, and the acquisition method flow includes the following steps.

[0056] Step 301: The first vehicle obtains environmental information of a sample area where the first vehicle is located. It can be understood that the first vehicle obtains environmental information of the sample area where the first vehicle is located at time i, where time i can be any time before the current time.

[0057] In the embodiment of the present application, the sample area is within the sensing range of the first vehicle, and the sample area is determined based on the sample position information corresponding to the position of the first vehicle at time i and at least one second preset distance. Two possible implementation methods for determining the sample area are described below.

[0058] In a first possible implementation, the sample position information corresponding to the position of the first vehicle at time i is used as the coordinate of the center of the circle, the second preset distance is used as the radius, and the area within the circle is determined as the sample area. The sample area determined by the above method includes at least one lane, and the first vehicle is located in a first lane of the at least one lane.

[0059] like Figure 4The diagram includes lane 1 and lane 2, both of which are north-south straight lanes. Lane 1 is determined by lane hard boundary line 1 and lane soft boundary line, and lane 2 is determined by lane soft boundary line and lane hard boundary line 2, wherein lane hard boundary line 1 and lane hard boundary line 2 are boundary lines that vehicles cannot drive on, and lane soft boundary line is a boundary line that vehicles can drive on. The first vehicle is located on north-south straight lane 1, and the sample position information corresponding to the position of the first vehicle at time i is used as the coordinate of the center of the circle, and the second preset distance D is used as the radius to determine the sample area, wherein the sample area includes lane 1 and lane 2, and lane 1 where the first vehicle is located is the first lane.

[0060] In a second possible implementation, the sample position information corresponding to the position of the first vehicle at time i is used as the coordinates of the center point of the rectangle, the first preset width is the width of the rectangle, the first preset length is the length of the rectangle, and the determined rectangular area is the sample area. The first preset width and the first preset length are both the second preset distance, and the first preset width and the first preset length may be the same or different. The sample area determined by the above method includes at least one lane, and the first vehicle is located in the first lane of the at least one lane.

[0061] like Figure 5 As shown, the first vehicle is located on the north-south straight lane 1, the sample position information corresponding to the position of the first vehicle at time i is used as the coordinates of the center point of the rectangle, the first preset width W1 is the width of the rectangle, and the first preset length L1 is the length of the rectangle. Based on the above, the sample area can be determined, wherein the sample area includes lane 1 and lane 2, and lane 1 where the first vehicle is located is the first lane.

[0062] In an embodiment of the present application, a map application software is installed in the first vehicle, and a high-precision map can be opened through the map application software. After the sample area is determined, environmental information of the sample area can be obtained from the high-precision map, wherein the environmental information includes road topology information and traffic participant information.

[0063] The road topology information includes the road topology structure of the sample area, such as the number of lanes, intersections, changes in the number of lanes (reduction in the number of lanes, increase in the number of lanes, etc.), lane directions (such as one-way streets, two-way lanes, etc.), lane boundaries, lane lines (double yellow lines, dashed and solid lines, etc.), speed limit information, lane types (north-south through lanes, east-west through lanes, left-turn lanes, right-turn lanes), etc.

[0064] In a possible implementation manner, the road topology information may be determined through the following multiple possible implementation manners.

[0065] The position of the first vehicle at time i is taken as the origin O(0,0), the driving direction of the first vehicle at time i is taken as the y-axis, and the x-axis perpendicular to the driving direction of the first vehicle at time i is taken as the x-axis to construct a rectangular coordinate system xOy, which is the body coordinate system corresponding to the first vehicle at time i. The body coordinate system corresponding to the first vehicle at time i can also be determined by other methods, which are not limited here. For the convenience of the following description, the body coordinate system corresponding to the first vehicle at time i can be referred to as the first coordinate system.

[0066] First, each lane in the sample area is segmented longitudinally according to a first preset distance to obtain at least one lane segment. In a possible implementation, when segmenting the lanes in the sample area, the lanes can be segmented along the lanes according to the first preset distance to obtain at least one lane segment. For example, Figure 6 As shown, the first preset distance is 5 meters, the sample area includes lane 1, and lane 1 in the sample area is segmented according to 5 meters to obtain 3 lane segments.

[0067] Determine the coordinates of each lane segment in at least one lane in the sample area in the first coordinate system. Generally speaking, the coordinates of any point of the lane segment in the first coordinate system can be used as the coordinates of the lane segment in the first coordinate system. In the embodiment of the present application, the coordinates of the center point of the lane segment in the first coordinate system can be used as the coordinates of the lane segment in the first coordinate system.

[0068] Determine a first deflection angle of each lane segment in at least one lane in the sample area relative to the driving direction of the first vehicle at time i, where the value range of the first deflection angle is -180 ○ ~180 ○ (Among them, -180 ○ and 180 ○ In one possible implementation, the first deflection angle may be determined by the following possible implementation: the angle between the lane prescribed driving direction corresponding to each lane segment and the driving direction of the first vehicle at time i is taken as the first deflection angle. If the lane prescribed driving direction corresponding to each lane segment deviates to the right relative to the driving direction of the first vehicle at time i, the first deflection angle is between 0 and 1. ○ ~180 ○ If the lane specified driving direction corresponding to each lane segment deviates to the left relative to the driving direction of the first vehicle at time i, the first deflection angle is between -180 ○ ~0 ○ between.

[0069] like Figure 7aA lane segment in lane 1 is shown, and the lane prescribed driving direction corresponding to the lane segment is the same as the driving direction of the first vehicle at time i. The angle between the lane prescribed driving direction corresponding to the lane segment and the driving direction of the first vehicle at time i is 0, that is, the first deflection angle is 0.

[0070] like Figure 7b A lane segment in lane 1 is shown, and the angle between the lane prescribed driving direction corresponding to the lane segment and the driving direction of the first vehicle at time i is d1, that is, the first deflection angle is d1.

[0071] The speed limit information of each lane segment in at least one lane in the sample area is determined. For example, the speed limit information corresponding to the lane segment is 60 km / h.

[0072] The lane type of each lane segment in at least one lane in the sample area is determined. The lane types include north-south through lanes, east-west through lanes, left-turn lanes, right-turn lanes, etc., which are not limited here.

[0073] In an embodiment of the present application, the coordinates of the lane segments in the sample area in the first coordinate system, the first deflection angle of the lane segments relative to the driving direction of the first vehicle at time i, the speed limit information of the lane segments, and the lane type of the lane segments are all used as lane features. The number of lane features in the present application is 4.

[0074] If the number of lanes in the sample area is M, the number of lane segments divided by each lane is P, and the number of lane features is F, then the road topology information of the sample area where the first vehicle is located at time i can be a matrix of M*P*F, where the values ​​in the matrix are the values ​​of the lane features corresponding to each lane segment in the sample area, and M, P, and F are all integers greater than 0.

[0075] It should be understood that when dividing a lane, if the number of lane segments obtained by the division is less than P, the preset value -1 can be used as the lane feature.

[0076] In an embodiment of the present application, traffic participants include a first vehicle and obstacles in a sample area, wherein obstacles include static targets in the sample area, such as road debris (such as stones, etc.), water barriers, cones, etc., and dynamic targets in the sample area, such as social vehicles and pedestrians, etc., wherein social vehicles include motor vehicles and non-motor vehicles (such as bicycles, electric vehicles, motorcycles, etc.).

[0077] In the embodiment of the present application, a sensor is installed in the first vehicle, through which speed information and position information of traffic participants in the lane can be obtained.

[0078] The traffic participant information includes information of the first vehicle, including the position, speed, acceleration, steering angle, historical driving trajectory and other information of the first vehicle. The steering angle can be the deflection angle of the first vehicle's driving direction relative to the lane line of the first lane in which it is located.

[0079] Traffic participant information also includes obstacle information, including the type, position, speed, acceleration, steering angle, historical driving trajectory, etc. of the obstacle. The steering angle can be the deflection angle of the obstacle relative to the driving direction of the first vehicle or the deflection angle relative to the lane line of the lane in which it is located.

[0080] Traffic participant information may also include speed information of traffic participants within a third historical time period. Generally speaking, multiple third historical moments are obtained from the third historical time period, and the speed information of each traffic participant at the multiple third historical moments is determined respectively. The multiple third historical moments satisfy the first time preset relationship with moment i. Exemplarily, the first time preset relationship can be understood as the difference between the multiple third historical moments by the first preset time period, and the largest third historical moment among the multiple third historical moments differs from moment i by the first preset time period.

[0081] In one possible implementation, the traffic participant information may be determined through the following multiple possible implementations.

[0082] The coordinates of at least one obstacle in the sample area in the first coordinate system are determined. The first coordinate system has been described above and will not be described again here.

[0083] Determine the second deflection angle of at least one obstacle in the sample area relative to the driving direction of the first vehicle at time i, and the value range of the second deflection angle is -180 ○ ~180 ○ (Among them, -180 ○ and 180 ○ In one possible implementation, the second deflection angle can be determined by the following possible implementations: if the obstacle is a private vehicle or a pedestrian, the angle between the direction of travel of the obstacle at time i and the direction of travel of the first vehicle at time i is taken as the second deflection angle. If the obstacle is road litter, since road litter has no direction of travel, the second deflection angle is 0. ○ .like Figure 8 In the sample area shown, when the obstacle is a motor vehicle, the direction of travel of the obstacle at time i is the same as the direction of travel of the first vehicle at time i, so the angle between the direction of travel of the obstacle at time i and the direction of travel of the first vehicle at time i is 0. ○ , that is, the second deflection angle is 0 ○ .

[0084] Determine the speed information of at least one obstacle in the sample area at time i. If the obstacle is a private vehicle or a pedestrian, the speed of the obstacle at time i is non-zero. If the obstacle is a road spill, the speed of the obstacle at time i is 0.

[0085] Determine the speed information of at least one obstacle in the sample area in the third historical time period. Generally speaking, multiple third historical moments are obtained from the third historical time period, and the speed information of each obstacle in multiple third historical moments is determined respectively. Among them, the third historical time period is the time period before moment i, the third historical moment is the moment before moment i, and the first time preset relationship is satisfied between multiple third historical moments and moment i. In a possible implementation, the first time preset relationship can be understood as the difference between multiple third historical moments by the first preset period, and the largest third historical moment among multiple third historical moments is different from moment i by the first preset period. For example, three third historical moments are obtained from the third historical time period, namely, third historical moment 1, third historical moment 2 and third historical moment 3, third historical moment 1 is less than third historical moment 2, third historical moment 2 is less than third historical moment 3, third historical moment 1 is different from third historical moment 2 by 0.01 seconds, third historical moment 2 is different from third historical moment 3 by 0.01 seconds, and third historical moment 3 is different from moment i by 0.01 seconds.

[0086] The obstacle type of each obstacle in at least one obstacle in the sample area is determined, wherein the obstacle types include motor vehicles, non-motor vehicles, pedestrians, road spills, etc., which are not limited here.

[0087] Determine the coordinates of the position of at least one obstacle in the sample area in the third historical time period in the first coordinate system. Generally speaking, multiple third historical moments are obtained from the third historical time period, and the coordinates of the position of each obstacle in the first coordinate system at the multiple third historical moments are determined respectively.

[0088] For any third historical moment among multiple third historical moments and any obstacle among at least one obstacle, if the position of the obstacle cannot be determined at the third historical moment, the coordinates of the position of the obstacle at the third historical moment in the first coordinate system can be set to preset coordinates, such as (-1,-1).

[0089] The speed information of the first vehicle at time i is determined.

[0090] The vehicle type of the first vehicle is determined. The vehicle type of the first vehicle may include a motor vehicle, a non-motor vehicle, etc., which is not limited here.

[0091] Determine the coordinates of the position of the first vehicle in the third historical time period in the first coordinate system. Generally speaking, multiple third historical moments are obtained from the third historical time period, and the coordinates of the position of the first vehicle in the first coordinate system at the multiple third historical moments are determined respectively.

[0092] Determine the speed information of the first vehicle in the third historical time period. Generally speaking, multiple third historical moments are obtained from the third historical time period, and the speed information of the first vehicle at the multiple third historical moments is determined respectively.

[0093] In an embodiment of the present application, the coordinates of the obstacle in the first coordinate system, the second deflection angle of the obstacle relative to the driving direction of the first vehicle at time i, the speed information of the obstacle at time i, the obstacle type of the obstacle, the speed information of the obstacle in the third historical time period, the coordinates of the position of the obstacle in the third historical time period in the first coordinate system, the speed information of the first vehicle at time i, the type of the first vehicle, the coordinates of the position of the first vehicle in the third historical time period in the first coordinate system, and the speed information of the first vehicle in the third historical time period are all used as traffic participant features. The number of traffic participant features in this application is 10.

[0094] If the number of traffic participants in the sample area is N, the number of reference moments (including multiple third historical moments and moment i) is T, and the number of traffic participant characteristics is H, then the traffic participant information in the sample area can be a matrix of N*T*H, wherein the values ​​in the matrix are the values ​​of the traffic participant characteristics corresponding to each traffic participant at each reference moment, and N, T, and H are all integers greater than 0.

[0095] Step 302: The first vehicle determines, based on environmental information, transverse partition information of each transverse partition in a plurality of transverse partitions corresponding to each sampling position in at least one sampling position.

[0096] In an embodiment of the present application, each of the at least one sampling position is located in front of the first vehicle in the driving direction, and the at least one sampling position has a different distance from the position of the first vehicle at time i.

[0097] For the first sampling position, the first sampling position is any sampling position of the at least one sampling position, and the first sampling position may be determined by the following possible implementation: taking a position at a third preset distance from the position of the first vehicle at time i as the first sampling position.

[0098] In the embodiment of the present application, different sampling positions may be determined by different third preset distances from the position of the first vehicle at time i.

[0099] It should be understood that the third preset distance may be a preset value, such as 100 meters; the third preset distance may be determined based on the speed information of the first vehicle at time i and the second preset period, that is, the speed of the first vehicle at time i multiplied by the second preset period is the third preset distance. For example, if the speed of the first vehicle at time i is 40 km / h and the second preset period is 3s, then the third preset distance is 40 km / h*3s=(100 / 3) meters.

[0100] In the embodiment of the present application, the location at a third preset distance from the location of the first vehicle at time i is used as the first sampling location, which may include the following possible implementations: Fig. 9 As shown, first determine the third reference line, wherein the third reference line includes the position of the first vehicle at time i and is parallel to the lane center line of the first lane, and then, along the third reference line, a position 300 meters away from the position of the first vehicle at time i is used as the first sampling position.

[0101] In the embodiment of the present application, the third reference line may also be other reference lines including the position of the first vehicle at time i, which is not limited here.

[0102] In an embodiment of the present application, after the sampling position is determined, multiple lateral partitions corresponding to each sampling position are determined, wherein the multiple lateral partitions corresponding to each sampling position are obtained by segmenting at least one lane of each sampling position in a lateral direction relative to the first vehicle, and each sampling position is within the multiple lateral partitions corresponding to each sampling position.

[0103] Determining the multiple horizontal partitions and horizontal partition information corresponding to each sampling position may include: Fig.10 The following steps are shown:

[0104] Step 1001: A first vehicle determines a first reference line corresponding to at least one sampling position, wherein the first reference line is perpendicular to a lane centerline of a first lane.

[0105] In the embodiment of the present application, a second reference line is first determined, wherein the second reference line may be the center line of the first lane, or may be a line parallel to the center line of the first lane, such as a third reference line. Then, at least one first reference line perpendicular to the second reference line and each including at least one sampling position is determined, and each sampling position corresponds to a first reference line.

[0106] Among them, the second reference line is a line parallel to the lane center line of the first lane, which can be understood as the angle between the second reference line and the lane center line of the first lane is less than the preset angle.

[0107] Step 1002: The first vehicle segments the first reference line corresponding to each sampling position to obtain a plurality of segmentation points, and uses the area between two adjacent segmentation points as a transverse partition to obtain a plurality of transverse partitions corresponding to each sampling position. The width of each transverse partition is less than the width of the first lane. The width of each transverse partition is the second preset width W2.

[0108] In the embodiment of the present application, for the first sampling position, the first sampling position is any sampling position of the at least one sampling position, and determining the segmentation point corresponding to the first sampling position may include the following possible implementations:

[0109] In a first possible implementation, on the first reference line, a point at a distance n*W2 from the first sampling position is a segmentation point, and the first sampling position can also be used as a segmentation point, where n is an integer greater than 0. Fig.11a As shown, the first sampling position is point o, on the first reference line, the points with a distance of W2 from point o are point a1 and point a2, the points with a distance of 2*W2 from point o are point b1 and point b2, and the points with a distance of 3*W2 from point o are point c1 and point c2. Among them, point o, point a1, point a2, point b1, point b2, point c1, and point c2 are all segmentation points.

[0110] In a second possible implementation, a point (W2) / 2 away from the first sampling position is first determined on the first reference line as the starting point, and a point on the first reference line that is n*W2 away from the starting point is a segmentation point. The starting point can also be used as a segmentation point, where n is an integer greater than 0. Fig.11b As shown, the first sampling position is point o, and the points a1 and a2 are determined on the first reference line to be (W2) / 2 away from the first sampling position, b1 is the point W2 away from point a1, c1 is the point 2*W2 away from point a1, b2 is the point W2 away from point a2, and c2 is the point 2*W2 away from point a2. Point a1, point a2, b1, b2, c1, and c2 are all segmentation points.

[0111] The widths between the segmentation points determined in the above manner are the same. The widths between the segmentation points determined in other manners may be different, which is not specifically limited here.

[0112] Step 1003: The first vehicle obtains transverse partition information of a plurality of transverse partitions corresponding to each sampling position in at least one sampling position.

[0113] In the embodiment of the present application, the horizontal partition information can be determined in the following manner:

[0114] Determine the width information of multiple horizontal partitions corresponding to each sampling position.

[0115] The lane types of the lanes where the multiple lateral partitions corresponding to each sampling position are located are determined. If the lateral partition includes partial areas of two lanes, the lane type of any lane is selected as the lane type of the lane where the lateral partition is located.

[0116] Determine the first offset distances of the plurality of transverse partitions corresponding to each sampling position relative to the first vehicle in the transverse direction (ie, the first offset distance relative to the third reference line). Generally speaking, the vertical distance of the center point of the transverse partition relative to the third reference line can be used as the first offset distance.

[0117] Determine the second offset distances of the plurality of lateral partitions corresponding to each sampling position relative to the lane centerline of the first lane. Generally speaking, the vertical distance of the center point of the lateral partition relative to the lane centerline of the first lane can be used as the second offset distance.

[0118] Determine the third deflection angles of the multiple lateral partitions corresponding to each sampling position relative to the driving direction of the first vehicle at time i, where the value range of the third deflection angle is -180 ○ ~180 ○ (Among them, -180 ○ and 180 ○ The third deflection angle may be determined by the following possible implementations: the angle between the lane specified driving direction of the lane corresponding to each lateral partition and the driving direction of the first vehicle at time i is taken as the third deflection angle. If the lane specified driving direction of the lane corresponding to each lateral partition deviates to the right relative to the driving direction of the first vehicle at time i, the third deflection angle is 0. ○ ~180 ○ If the lane specified driving direction of the lane corresponding to each lateral partition is deflected to the left relative to the driving direction of the first vehicle at time i, the third deflection angle is -180 ○ ~0 ○ between.

[0119] In an embodiment of the present application, the lane type of the lane where the lateral partition is located, the second offset distance of the lateral partition relative to the lane centerline of the first lane, the first offset distance of the lateral partition relative to the third reference line, and the third deflection angle of the lateral partition relative to the driving direction of the first vehicle at time i are all used as partition features. The number of partition features in this application is 4.

[0120] If the number of horizontal partitions corresponding to at least one sampling position is K, and the number of partition features is G, then the horizontal partition information can be a K*G matrix, where the values ​​in the matrix are the values ​​of the partition features corresponding to each horizontal partition, and K and G are both integers greater than 0.

[0121] In the embodiment of the present application, each group of first sample data has a corresponding first label. For any group of first sample data, the first label corresponding to the first sample data includes a first partition label and a first deflection angle label corresponding to at least one sampling position.

[0122] For the first sampling position, which is any sampling position of at least one sampling position, determine the intersection of the actual driving trajectory of the first vehicle and the first reference line, record it as the first reference point, and the number of the lateral partition corresponding to the first sampling position where the first reference point falls is the first partition label. The deflection angle of the driving direction of the first vehicle at the first reference point relative to the driving direction of the first vehicle at time i is the first deflection angle label, and the value range of the first deflection angle label is -180 ○ ~180 ○ (Among them, -180 ○ and 180 ○ The labels of the first deflection angles represented are the same).

[0123] The explanation is given by taking the multiple horizontal partitions corresponding to the first sampling position determined in the first possible implementation manner as an example. Fig.12 As shown, the lateral partition between segmentation point o and segmentation point a1 is marked as 1, the lateral partition between segmentation point a1 and segmentation point b1 is marked as 2, the lateral partition between segmentation point b1 and segmentation point c1 is marked as 3, the lateral partition between segmentation point o and segmentation point a2 is marked as -1, the lateral partition between segmentation point a2 and segmentation point b2 is marked as -2, and the lateral partition between segmentation point b2 and segmentation point c2 is marked as -3. The intersection of the actual driving trajectory of the first vehicle and the first reference line is the first reference point. The first reference point falls into the lateral partition numbered 1, so the first partition label is 1. The deflection angle of the driving direction of the first vehicle at the first reference point relative to the driving direction of the first vehicle at time i is d2, so the first deflection angle label is d2.

[0124] In step 202 , the first vehicle inputs at least one set of first sample data into a partition prediction model to obtain a first prediction result for each of a plurality of transverse partitions corresponding to at least one sampling position of each set of first sample data.

[0125] In the embodiments of the present application, Fig.13 The partition prediction model shown includes a feature extraction module, a classification module and a regression module, wherein the feature extraction module includes a first fusion feature extraction module, a second fusion feature extraction module and a third fusion feature extraction module.

[0126] The traffic participant information and road topology information in at least one group of first sample data are input into the first fusion feature extraction module to obtain the first fusion feature vector and the second fusion feature vector respectively. The first fusion feature vector and the lateral partition information of each lateral partition in the multiple lateral partitions corresponding to each sampling position in at least one group of first sample data are input into the second fusion feature extraction module to obtain the third fusion feature vector. The second fusion feature vector and the third fusion feature vector are input into the third fusion feature extraction module to obtain the target fusion feature vector. The target fusion feature vector is input into the classification module and the regression module respectively to obtain the first prediction result of each lateral partition in the multiple lateral partitions corresponding to at least one sampling position of each group of first sample data. The first prediction result of each lateral partition in the multiple lateral partitions corresponding to one sampling position of one group of first sample data is used for illustration. The first prediction result of each lateral partition in the multiple lateral partitions corresponding to one sampling position of the group of first sample data may include the first prediction probability value of each lateral partition corresponding to the sampling position and the first predicted deflection angle of each lateral partition relative to the driving direction of the first vehicle at time i. The classification module outputs the first prediction probability value of each lateral partition, and the regression module outputs the first predicted deflection angle of each lateral partition relative to the driving direction of the first vehicle at time i.

[0127] It should be understood that the first fusion feature extraction module, the second fusion feature extraction module and the third fusion feature extraction module can be a cross-attention mechanism, which is not limited here.

[0128] like Fig.14 The partition prediction model shown may also include a first graph neural network and a second graph neural network. First, the traffic participant information in at least one group of first sample data is input into the first graph neural network to obtain a first feature vector, and the road topology information in at least one group of first sample data is input into the second graph neural network to obtain a second feature vector. The first feature vector and the second feature vector are input into the first fusion feature extraction module to obtain a first fusion feature vector and a second fusion feature vector respectively, and the first fusion feature vector and the lateral partition information of each lateral partition in the multiple lateral partitions corresponding to each sampling position in at least one group of first sample data are input into the second fusion feature extraction module to obtain a third fusion feature vector, and the second fusion feature vector and the third fusion feature vector are input into the third fusion feature extraction module to obtain a target fusion feature vector, and the target fusion feature vector is input into the classification module and the regression module respectively to obtain the first prediction result of each lateral partition in the multiple lateral partitions corresponding to at least one sampling position of each group of first sample data.

[0129] It should be understood that the first graph neural network and the second graph neural network can be graph neural networks (GNN), which is not limited here.

[0130] In step 203, the first vehicle inputs the first prediction result and the first label of at least one group of first sample data into the loss function calculation model to obtain the loss value between the first prediction result and the first label of each group of first sample data, and updates the parameters in the partition prediction model according to the loss value between the first prediction result and the first label of each group of first sample data until the training is completed to obtain the trained partition prediction model.

[0131] like Fig.15 The model structure shown includes a partition prediction model and a loss function calculation model, wherein the loss function calculation model includes a cross entropy loss function module and a mean square error (mse) loss function module.

[0132] The following calculation is performed for the first sampling position corresponding to each group of first sample data in the at least one group of first sample data.

[0133] The cross entropy loss function module receives the first predicted probability value of each horizontal partition corresponding to the first sampling position output by the classification module, and the first partition label corresponding to the first sampling position. The cross entropy loss function is sampled to calculate the loss value of the first predicted probability value of each horizontal partition corresponding to the first sampling position corresponding to the first sample data and the first partition label, and the parameters in the partition prediction model are updated according to the loss value.

[0134] The mean square error loss function module receives the first predicted deflection angle of each lateral partition corresponding to the first sampling position relative to the driving direction of the first vehicle at time i and the first deflection angle label corresponding to the first sampling position output by the regression module. The mean square error loss function is used to calculate the loss value of the first predicted deflection angle of each lateral partition corresponding to the first sampling position corresponding to the first sample data relative to the driving direction of the first vehicle at time i and the first deflection angle label, and the parameters in the partition prediction model are updated according to the loss value.

[0135] In an embodiment of the present application, the parameters in the partition prediction model are updated based on the first predicted probability value of each lateral partition corresponding to at least one sampling position and the loss value of the first partition label, and the first predicted deflection angle of each lateral partition corresponding to at least one sampling position relative to the driving direction of the first vehicle at time i and the loss value of the first deflection angle label, so that the partition prediction model can be trained faster.

[0136] The present application embodiment provides a process of a path planning method, such as Fig.16 As shown, the following steps may be included:

[0137] Step 1601: A first vehicle obtains environmental information of a first area where the first vehicle is located. The first area is within a perception range of the first vehicle and the first area includes at least one lane. The first vehicle is located in a first lane of the at least one lane.

[0138] In the embodiment of the present application, the first area is within the perception range of the first vehicle. The following examples introduce possible implementation methods for determining the first area.

[0139] Exemplarily, the coordinates of the circle with the current position of the first vehicle as the center and the second preset distance as the radius are determined to be the first area.

[0140] Exemplarily, the current position of the first vehicle is the coordinate of the center point of the rectangle, the first preset width is the width of the rectangle, the first preset length is the length of the rectangle, and the determined rectangular area is the first area. The first preset width and the first preset length are both the second preset distance, and the first preset width and the first preset height can be the same or different.

[0141] Exemplarily, the first area is an area that can be sensed by the first vehicle through a vehicle-mounted sensor or a vehicle-mounted sensing system, and can also be an area sensed based on vehicle to everything (V2X), or can be an area sensed by a combination of the two.

[0142] In an embodiment of the present application, after the first area is determined, environmental information of the first area may be acquired, wherein the environmental information includes road topology information and traffic participant information.

[0143] The road topology information includes the road topology structure of the first area, such as the number of lanes, intersections, changes in the number of lanes (reduction in the number of lanes, increase in the number of lanes, etc.), lane directions (such as one-way streets, two-way lanes, etc.), lane boundaries, lane lines (double yellow lines, dashed and solid lines, etc.), speed limit information, lane types (north-south through lanes, east-west through lanes, left-turn lanes, right-turn lanes), etc.

[0144] The traffic participant information includes information of the first vehicle, including the position, speed, acceleration, steering angle, historical driving trajectory and other information of the first vehicle. The steering angle can be the deflection angle of the first vehicle's driving direction relative to the lane line of the first lane in which it is located.

[0145] Traffic participant information also includes obstacle information, including the type, position, speed, acceleration, steering angle, historical driving trajectory, etc. of the obstacle. The steering angle can be the deflection angle of the obstacle relative to the driving direction of the first vehicle or the deflection angle relative to the lane line of the lane in which it is located.

[0146] Traffic participant information may also include speed information of traffic participants within a first historical time period. Generally speaking, multiple first historical moments are obtained from the first historical time period, and the speed information of each traffic participant at the multiple first historical moments is determined respectively. The multiple first historical moments satisfy the first time preset relationship with the current moment. Exemplarily, the first time preset relationship can be understood as the difference between the multiple first historical moments by the first preset time period, and the largest first historical moment among the multiple first historical moments differs from the current moment by the first preset time period.

[0147] Step 1602: The first vehicle determines, based on environmental information, transverse partition information of each transverse partition in a plurality of transverse partitions corresponding to each sampling position in at least one sampling position.

[0148] In an embodiment of the present application, each of the at least one sampling position is located in front of the driving direction of the first vehicle, and each of the at least one sampling position has a different distance from the position of the first vehicle at the current moment.

[0149] For the first sampling position, the first sampling position is any one of the at least one sampling position, and the first sampling position can be determined by the following possible implementation methods: a position located at a first preset sampling distance in front of the first vehicle in the driving direction is taken as the first sampling position, and the first preset sampling distance can be 5 meters or 7 meters, etc., without specific limitation. Similarly, the second sampling position, the third sampling position, etc. can be determined, and each sampling position is located on the road in front of the first vehicle in the driving direction, and has a different distance from the first vehicle, for example, the first sampling position is 5 meters away from the position of the first vehicle, the second sampling position is 8 meters away from the position of the first vehicle, and the third sampling position is 10 meters away from the position of the first vehicle, etc.

[0150] Exemplarily, the method for determining the first sampling position in step 1602 may also be the same as the method for determining the first sampling position in step 302, which is not described in detail here. Similarly, the method for determining the horizontal partition corresponding to each sampling position may be the same as the method for determining the horizontal partition corresponding to each sampling position in step 1002, which is not described in detail here.

[0151] In the embodiment of the present application, after determining the horizontal partition corresponding to each sampling position, the horizontal partition information of each horizontal partition in the multiple horizontal partitions corresponding to each sampling position in at least one sampling position may be determined:

[0152] Determine the width information of each lateral partition in the multiple lateral partitions corresponding to each sampling position, the lane type of the lane where each lateral partition is located (if the lateral partition is located between two lanes, select the lane type of any one of the two lanes as the lane type of the lane where the lateral partition is located, or select the lane type of the lane with more occupied lanes as the lane type of the lateral partition), the first offset distance of each lateral partition in the lateral direction relative to the first vehicle, the second offset distance of each lateral partition relative to the lane centerline of the first lane, and the third deflection angle of each lateral partition relative to the driving direction of the first vehicle. Exemplarily, when calculating the offset distance of each lateral partition, the center point position of the lateral partition can be used for calculation. The value range of the third deflection angle is -180 to 180 ○ (Among them, -180 ○ and 180 ○ For example, the third deflection angle may be determined in the following manner: the angle between the lane specified driving direction of the lane corresponding to each lateral partition and the driving direction of the first vehicle is taken as the third deflection angle. If the lane specified driving direction of the lane corresponding to each lateral partition deviates to the right relative to the driving direction of the first vehicle at the current moment, the third deflection angle is 0. ○ ~180 ○ If the lane specified driving direction of the lane corresponding to each lateral partition is deflected to the left relative to the driving direction of the first vehicle at the current moment, the third deflection angle is -180 ○ ~0 ○ between.

[0153] Step 1603: The first vehicle plans a driving path of the first vehicle according to the environmental information and the transverse partition information of each transverse partition in the plurality of transverse partitions corresponding to each sampling position.

[0154] Specifically, when planning the driving path of the first vehicle, the first vehicle may include: Fig.17 The following steps are shown:

[0155] Step 1701: The first vehicle determines a partition prediction result for each of the multiple lateral partitions corresponding to each sampling position based on environmental information and lateral partition information of each of the multiple lateral partitions corresponding to each sampling position, wherein the partition prediction result includes a first predicted probability value indicating a probability that the first vehicle may pass through the lateral partition, and a first predicted deflection angle of each lateral partition relative to the driving direction of the first vehicle.

[0156] Exemplarily, environmental information (road topology information, traffic participant information) and lateral partition information of each lateral partition in multiple lateral partitions corresponding to each sampling position can be input into the partition prediction model to obtain the partition prediction results of each lateral partition corresponding to each sampling position that the first vehicle will pass through in the future.

[0157] In the embodiment of the present application, the partition prediction model is obtained by training according to steps 201 to 203, which will not be described in detail here.

[0158] Step 1702: The first vehicle plans a driving path of the first vehicle according to a partition prediction result of each transverse partition in a plurality of transverse partitions corresponding to each sampling position.

[0159] Exemplarily, for the first sampling position, the first sampling position is any one of the at least one sampling position. If, among the transverse partitions corresponding to the first sampling position, there is a transverse partition that intersects with the hard boundary line of the lane, the first predicted probability value of the transverse partition that intersects with the hard boundary line of the lane is set to -1, that is, the transverse partition that intersects with the hard boundary line of the lane is not considered subsequently. If, among the transverse partitions corresponding to the first sampling position, there is a transverse partition whose lane type conflicts with the driving direction of the first vehicle at the current moment, the first predicted probability value of the transverse partition that conflicts with the driving direction of the first vehicle at the current moment is set to -1, that is, the transverse partition that conflicts with the driving direction of the first vehicle at the current moment is not considered subsequently. To illustrate the conflict between the lane type of the lane where the transverse partition is located and the driving direction of the first vehicle at the current moment, if the lane type of the lane where the transverse partition is located is a straight lane from south to north, and the driving direction of the first vehicle at the current moment is from north to south, it means that the lane type of the lane where the transverse partition is located conflicts with the driving direction of the first vehicle at the current moment.

[0160] In the above step 1702, the first vehicle plans the driving path of the first vehicle according to the partition prediction result of each of the multiple transverse partitions corresponding to each sampling position, which may include: Fig.18 The following steps are shown:

[0161] Step 1801: The first vehicle determines at least one target lateral partition corresponding to each sampling position according to a partition prediction result of each lateral partition among a plurality of lateral partitions corresponding to each sampling position.

[0162] Exemplarily, for the first sampling position, which is any one of the at least one sampling position, at least one target horizontal partition corresponding to the first sampling position may be determined in the following two possible ways.

[0163] In a first manner, a horizontal partition having a maximum first prediction probability value among partition prediction results of multiple horizontal partitions corresponding to the first sampling position is used as a target horizontal partition corresponding to the first sampling position.

[0164] In a second possible implementation, the horizontal partition with the largest first prediction probability value among the partition prediction results of the multiple horizontal partitions corresponding to the first sampling position is used as the first target horizontal partition corresponding to the first sampling position, and at least one horizontal partition that satisfies the preset partition relationship with the first target horizontal partition is used as the second target horizontal partition. The first target horizontal partition and the second target horizontal partition are both target horizontal partitions.

[0165] In an embodiment of the present application, at least one horizontal partition that satisfies a preset partition relationship with the first target horizontal partition can be understood as at least one horizontal partition that is closest to the first target horizontal partition. For example, the two horizontal partitions that are closest to the first target horizontal partition are both the second target horizontal partitions.

[0166] Step 1802: The first vehicle plans a driving path of the first vehicle based on the lateral partition information of at least one target lateral partition corresponding to each sampling position.

[0167] In an embodiment of the present application, based on the lateral partition information of at least one target lateral partition corresponding to each sampling position, the position information and deflection angle information of the prediction point corresponding to each sampling position are determined, and based on the position information and deflection angle information of the prediction point corresponding to each sampling position, the driving path of the first vehicle is planned.

[0168] Exemplarily, for the first sampling position, the first sampling position is any one of the at least one sampling position, and the position information of the prediction point corresponding to the first sampling position can be obtained by: Fig.19 The following steps are shown to determine.

[0169] Step 1901: The first vehicle determines second position information corresponding to an intersection of a third reference line and a first reference line corresponding to a first sampling position.

[0170] Step 1902: The first vehicle determines a second lateral offset distance according to a first lateral offset distance of at least one target lateral partition relative to a third reference line. The first lateral offset distance of at least one target lateral partition relative to the third reference line may be understood as a vertical distance of a center point of at least one target lateral partition relative to the third reference line.

[0171] In the embodiment of the present application, if there is a target horizontal partition, the first horizontal offset distance of the target horizontal partition relative to the third reference line is used as the second horizontal offset distance.

[0172] If there are multiple target horizontal partitions (i.e., including a first target horizontal partition and at least one second target horizontal partition), a different weight is set for each target horizontal partition, and the second horizontal offset distance is determined according to the first horizontal offset distance of each target horizontal partition relative to the third reference line and the weight of each target horizontal partition.

[0173] For example, multiple target horizontal partitions include a first target horizontal partition, a second target horizontal partition 1, and a second target horizontal partition 2. The first horizontal offset distance of the first target horizontal partition relative to the third reference line is e1, the first horizontal offset distance of the second target horizontal partition 1 relative to the third reference line is e2, and the first horizontal offset distance of the second target horizontal partition 2 relative to the third reference line is e3. The corresponding weight of the first target horizontal partition is 0.8, the corresponding weight of the second target horizontal partition 1 is 0.1, and the corresponding weight of the second target horizontal partition 2 is 0.1. The second horizontal offset distance e4=e1*0.8+e2*0.1+e3*0.1.

[0174] In the embodiment of the present application, since the first lateral offset distances of multiple target lateral partitions relative to the third reference line are referred to when determining the second lateral offset distance, sudden changes in the determined second lateral offset distance can be effectively avoided and errors can be reduced.

[0175] Optionally, after determining the second lateral offset distance based on the first lateral offset distance of at least one target lateral partition relative to the third reference line, at least one third lateral offset distance corresponding to the first vehicle in the second historical time period can also be determined, and the second lateral offset distance can be adjusted using the at least one third lateral offset distance, wherein the second historical time period is a time period of a preset historical length before the current moment of the first vehicle, for example, the preset historical length can be 5 seconds or 3 seconds, etc.

[0176] In one possible implementation, at least one second historical moment is obtained from a second historical time period, and a third lateral offset distance corresponding to the first vehicle at the at least one second historical moment is determined respectively, and different weights are set for each third lateral offset distance and the second lateral offset distance, and a new second lateral offset distance is determined based on each third lateral offset distance and the second lateral offset distance, and their respective corresponding weights.

[0177] For example, the second lateral offset distance is e4, and the second historical moment 1 and the second historical moment 2 are obtained from the second historical time period. The third lateral offset distance corresponding to the first vehicle at the second historical moment 1 is e5, and the third lateral offset distance corresponding to the first vehicle at the second historical moment 2 is e6. The weight corresponding to the third lateral offset distance e5 is 0.1, the weight corresponding to the third lateral offset distance e6 is 0.1, and the weight corresponding to the second lateral offset distance e4 is 0.8. The updated second lateral offset distance e7 = e4*0.8+e5*0.1+e6*0.1.

[0178] In the embodiment of the present application, the second lateral offset distance is adjusted by using the third lateral offset distance corresponding to the first vehicle at at least one second historical moment, which can effectively avoid a sudden change in the determined second lateral offset distance and reduce errors.

[0179] Step 1903: The first vehicle determines the position information of the prediction point corresponding to the first sampling position according to the second position information corresponding to the first sampling position and the second lateral offset distance.

[0180] like Fig. 20 As shown, the first position of the first vehicle is point B, the second position information corresponding to the first sampling position is point o, and the second lateral offset distance is the distance between point o and point A. Then, the position information of the predicted point corresponding to the first sampling position, that is, point A, can be determined.

[0181] For the first sampling position, the first sampling position is any one of the at least one sampling position, the deflection angle information of the predicted point corresponding to the first sampling position can include the following steps: the first vehicle determines the deflection angle information according to the first predicted deflection angle corresponding to at least one target lateral partition corresponding to the first sampling position.

[0182] In the embodiment of the present application, if there is a target horizontal partition, the first predicted deflection angle corresponding to the target horizontal partition is used as the deflection angle information.

[0183] If there are multiple target horizontal partitions (ie, including a first target horizontal partition and at least one second target horizontal partition), the deflection angle information is determined according to a first preset deflection angle corresponding to each target horizontal partition and a weight of each target horizontal partition.

[0184] For example, multiple target horizontal partitions include a first target horizontal partition, a second target horizontal partition 1, and a second target horizontal partition 2. The first preset deflection angle corresponding to the first target horizontal partition is d1, the first preset deflection angle corresponding to the second target horizontal partition 1 is d2, and the first preset deflection angle corresponding to the second target horizontal partition 2 is d3. The weight corresponding to the first target horizontal partition is 0.8, the weight corresponding to the second target horizontal partition 1 is 0.1, and the weight corresponding to the second target horizontal partition 2 is 0.1. The deflection angle information = d1*0.8+d2*0.1+d3*0.1.

[0185] In an embodiment of the present application, planning the driving path of the first vehicle based on the position information of the prediction point corresponding to each sampling position and the deflection angle information can include the following steps: the first vehicle uses the Dubins curve to perform curve fitting on the first position information of the first vehicle at the current moment, the position information of the prediction point corresponding to each sampling position, and the deflection angle information to obtain the driving path of the first vehicle.

[0186] In the above method, since the width of the lateral partition is smaller than the width of the lane, the accuracy of the planned driving path of the first vehicle can be improved based on the environment information and the lateral partition information of the multiple lateral partitions corresponding to each sampling position.

[0187] The present application also provides a path planning device, which is suitable for Figure 1 Optionally, the path planning device may be a computing device with a path planning function or a device (such as a plug-in or component or functional component) that can support the computing device to implement the functions required by the path planning method. Optionally, in one example, the path planning device is used to implement the above Figure 2 or Fig.16 The technical solution involved in the path planning device in the method embodiment shown, or the module (such as a chip) of the path planning device is used to implement the above Figure 2 or Fig.16 The technical solution involved in the path planning device in the method embodiment shown can also achieve the above Figure 2 or Fig.16 The beneficial effects of the path planning device in the method embodiment are shown.

[0188] See also Fig.21 The path planning device 2100 includes an acquisition module 2101 and a processing module 2102. The path planning device 2100 is used to implement the above Figure 2 or Fig.16 The functions of the path planning device in the method embodiment are shown.

[0189] When the path planning device 2100 is used to implement Figure 2 or Fig.16 The functions of the method embodiment shown are: an acquisition module 2101, used to acquire environmental information of a first area where a first vehicle is located, wherein the first area is within the perception range of the first vehicle and the first area includes at least one lane, and the first vehicle is located on a first lane of the at least one lane. A processing module 2102, used to determine, based on the environmental information, lateral partition information of each of a plurality of lateral partitions corresponding to each sampling position in at least one sampling position, wherein each sampling position in at least one sampling position is located in front of the driving direction of the first vehicle and has a different distance from the position of the first vehicle, and the width of each lateral partition is less than the width of the first lane. The processing module 2102 is also used to plan the driving path of the first vehicle based on the environmental information and the lateral partition information of each of a plurality of lateral partitions corresponding to each sampling position.

[0190] Wherein, when the path planning device 2100 is used to implement Figure 2 or Fig.16 For a more detailed description of the acquisition module 2101 and the processing module 2102, please refer to the above Figure 2 or Fig.16 The relevant description in the method embodiment shown will not be repeated here.

[0191] Optional, above Fig.21 The acquisition module and the processing module shown can be implemented by software or hardware. Exemplarily, the implementation of the processing module is introduced below by taking the processing module as an example. Similarly, the implementation of the acquisition module can refer to the implementation of the processing module, which will not be repeated here.

[0192] When implemented by software, the processing module is used as an example of a software functional unit, and the processing module may be an application or a code block running on a computer device. Among them, the computer device may be at least one of a physical host, a virtual machine, a container and other computing devices. Furthermore, the above-mentioned computer device may be one or more. For example, the processing module may be an application running on multiple hosts / virtual machines / containers. It should be noted that the multiple hosts / virtual machines / containers used to run the application may be distributed in the same availability zone (AZ) or in different AZs. The multiple hosts / virtual machines / containers used to run the application may be distributed in the same region or in different regions. Among them, usually a region may include multiple AZs.

[0193] Similarly, multiple hosts / virtual machines / containers for running the application can be distributed in the same virtual private cloud (VPC) or in multiple VPCs. Typically, a region can include multiple VPCs, and a VPC can include multiple AZs.

[0194] When implemented by hardware, the processing module is used as an example of a hardware functional unit, and the processing module may include at least one computing device, such as a server, etc. Alternatively, the processing module may also be a device implemented by an application-specific integrated circuit (ASIC) or a programmable logic device (PLD). The PLD may be a complex programmable logical device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL), or any combination thereof.

[0195] The multiple computing devices included in the processing module can be distributed in the same AZ or in different AZs. The multiple computing devices included in the processing module can be distributed in the same region or in different regions. Similarly, the multiple computing devices included in the processing module can be distributed in the same VPC or in multiple VPCs. The multiple computing devices can be any combination of computing devices such as servers, ASICs, PLDs, CPLDs, FPGAs, and GALs.

[0196] It should be noted that the steps implemented by the acquisition module and the processing module can be specified as needed, and the full functions of the path planning device can be realized by implementing different execution steps of the path planning method by the acquisition module and the processing module respectively.

[0197] In addition, it should be noted that the division of modules in the embodiments of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation. The functional modules in the embodiments of the present application can be integrated into one module, or each module can exist physically separately, or two or more modules can be integrated into one module. For example, the acquisition module and the processing module can be integrated into one module, or the acquisition module and the processing module are the same module. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units.

[0198] Based on the same concept, the present application also provides a path planning device, which is suitable for Figure 1 In the application scenario shown in FIG. 1 , the path planning device is used to implement the above Figure 2 or Fig.16 The technical solution of the path planning device in the method embodiment shown can also achieve the above Figure 2 or Fig.16 The beneficial effects of the path planning device in the method embodiment shown in FIG. Fig. 22 , the path planning device 2200 includes: a processor 2202. Optionally, the path planning device further includes a memory 2203. Optionally, the path planning device 2200 also includes a transceiver 2201. The transceiver 2201, the processor 2202 and the memory 2203 are interconnected. When the path planning device 2200 is used to implement the technical solution involved in the path planning device provided in the above embodiment, the transceiver 2201 can be used to implement the function of the above acquisition module 2101, and the processor 2202 is used to implement the function of the above processing module 2102.

[0199] Optionally, the transceiver 2201, the processor 2202, and the memory 2203 are interconnected via a bus 2204. The bus 2204 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 5 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0200] The transceiver 2201 is used to receive and send data. In one example, the transceiver may be a transceiver device integrated with a data transceiver function. In another example, the transceiver may also be composed of a transmitter and a receiver, wherein the transmitter is used to send data and the receiver is used to receive data.

[0201] Optionally, the transceiver 2201 may include a transmitter and / or a receiver. The transmitter is used to send signals, messages, information, or data, etc. The receiver is used to receive signals, messages, information, or data, etc. Exemplarily, the transmitter sends signals, messages, information, or data, etc. under the control of the processor 2202. The receiver receives signals, messages, information, or data, etc. under the control of the processor 2202.

[0202] The function of the processor 2202 can refer to the above Figure 2 or Fig.16The description of the corresponding functions involved in the path planning device in the method embodiment shown is not repeated here. Among them, the processor 2202 can be a central processing unit (CPU), a network processor (NP) or a combination of CPU and NP, etc. The processor 2202 can further include a hardware chip. The above-mentioned hardware chip can be an application-specific integrated circuit (ASIC), a programmable logic device (PLD) or a combination thereof. The above-mentioned PLD can be a complex programmable logic device (CPLD), a field-programmable gate array (FPGA), a generic array logic (GAL) or any combination thereof. When implementing the above-mentioned functions, the processor 2202 can be implemented by hardware, and of course, it can also execute the corresponding software implementation through hardware.

[0203] The memory 2203 may include a volatile memory (volatile memory), such as a random access memory (RAM), and may also include a non-volatile memory (non-volatile memory), such as at least one disk storage.

[0204] The memory 2203 stores executable program instructions, and the processor 2202 executes the executable program instructions to respectively implement the functions of the aforementioned path planning device (such as the acquisition module and the processing module), thereby implementing the path planning method provided in the embodiment of the present application. That is, the memory 2203 stores computer program instructions for executing the path planning method.

[0205] Alternatively, the memory 2203 stores executable code, and the processor 2202 executes the executable code to implement the functions of the aforementioned path planning device (such as the acquisition module and the processing module), thereby implementing the path planning method provided in the embodiment of the present application. That is, the memory 2203 stores a computer program code for the path planning device to execute the path planning method provided in the embodiment of the present application.

[0206] The present application also provides a path planning system, which includes the above-mentioned path planning device, wherein the path planning device can be used to implement the above-mentioned Figure 2 or Fig.16The technical solution involved in the path planning device in the method embodiment shown, exemplarily, the number of the path planning devices can be one or more, and the embodiment of the present application does not limit this.

[0207] An embodiment of the present application also provides a vehicle, which includes the above-mentioned path planning device or the above-mentioned path planning system.

[0208] An embodiment of the present application also provides a computer program product, which includes computer program instructions. When the computer program or instructions are executed on a computer, the computer executes the path planning method provided in the above embodiment.

[0209] An embodiment of the present application also provides a computer-readable storage medium, in which a computer program or computer program instructions are stored. When the computer program or computer program instructions are executed by a computer, the computer executes the path planning method provided in the above embodiment.

[0210] The storage medium may be any available medium that can be accessed by a computer. For example, but not limited to, a computer-readable medium may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer.

[0211] An embodiment of the present application also provides a chip, which is coupled to a memory and is used to read computer program instructions stored in the memory to implement the path planning method provided in the above embodiment.

[0212] The embodiment of the present application also provides a chip system, which includes a processor for supporting a computer device to implement the functions involved in the path planning device in the above embodiment. In one design, the chip system also includes a memory, which is used to store program instructions and / or data necessary for the computer device. The chip system can be composed of a chip, or it can include a chip and other discrete devices.

[0213] In the method provided in the embodiment of the present application, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from a website site, computer, server or data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (digital subscriber line, DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, server or data center. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrated. The available medium may be a magnetic medium (eg, a floppy disk, a hard disk, a magnetic tape), an optical medium (eg, a digital video disc (DVD)), or a semiconductor medium (eg, a solid state drive (SSD)).

[0214] The steps of the method described in the embodiments of the present application can be directly embedded in the hardware, the software unit executed by the processor, or the combination of the two. The software unit can be stored in RAM, ROM, EEPROM, register, hard disk, removable disk, CD-ROM or other storage media of any form in the art. Exemplarily, the storage medium can be connected to the processor so that the processor can read information from the storage medium and can write information to the storage medium. Optionally, the storage medium can also be integrated into the processor. The processor and the storage medium can be arranged in an ASIC.

[0215] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0216] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0217] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A path planning method, characterized in that: The method comprises: Acquire environmental information of a first area where a first vehicle is located, where the first area is within a sensing range of the first vehicle and the first area includes at least one lane, and the first vehicle is located in a first lane of the at least one lane; Determine, according to the environmental information, transverse partition information of each of a plurality of transverse partitions corresponding to each sampling position in at least one sampling position, wherein each sampling position in the at least one sampling position is located in front of the first vehicle in a driving direction and has a different distance from the position of the first vehicle, and a width of each transverse partition is less than a width of the first lane; The driving path of the first vehicle is planned according to the environmental information and the transverse partition information of each transverse partition corresponding to each sampling position.

2. The method according to claim 1, characterized in that The lateral partition information includes a first offset distance of the lateral partition relative to the first vehicle in a lateral direction.

3. The method according to claim 1 or 2, characterized in that: The planning of the driving path of the first vehicle according to the environmental information and the lateral partition information of each lateral partition corresponding to each sampling position includes: Determine, according to the environmental information and the lateral partition information of each lateral partition corresponding to each sampling position, a partition prediction result of each lateral partition corresponding to each sampling position, wherein the partition prediction result includes a first prediction probability value and a first predicted deflection angle of each lateral partition relative to the driving direction of the first vehicle; The driving path of the first vehicle is planned according to the partition prediction result of each lateral partition corresponding to each sampling position.

4. The method according to claim 3, characterized in that The planning of the driving path of the first vehicle according to the partition prediction result of each lateral partition corresponding to each sampling position includes: Determine at least one target horizontal partition corresponding to each sampling position according to the partition prediction result of each horizontal partition corresponding to each sampling position; Based on the lateral partition information of at least one target lateral partition corresponding to each sampling position, a driving path of the first vehicle is planned.

5. The method according to claim 4, characterized in that The determining, according to the partition prediction result of each horizontal partition corresponding to each sampling position, at least one target horizontal partition corresponding to each sampling position comprises: A first horizontal partition among the multiple horizontal partitions corresponding to the first sampling position is used as a first target horizontal partition corresponding to the first sampling position, the first sampling position is any sampling position among the at least one sampling position, and the first target horizontal partition is a horizontal partition with the largest first prediction probability in the partition prediction results among the multiple horizontal partitions corresponding to the first sampling position.

6. The method according to claim 5, characterized in that The method further includes taking at least one horizontal partition that satisfies a preset partition relationship with the first target horizontal partition as a second target horizontal partition.

7. The method according to any one of claims 4 to 6, characterized in that: The planning of the driving path of the first vehicle based on the lateral partition information of at least one target lateral partition corresponding to each sampling position includes: Determine the position information and the deflection angle information of the prediction point corresponding to each sampling position based on the horizontal partition information of at least one target horizontal partition corresponding to each sampling position; Based on the position information and the deflection angle information of the prediction point corresponding to each sampling position, the driving path of the first vehicle is planned.

8. The method according to any one of claims 1 to 7, characterized in that: The environmental information includes road topology information and traffic participant information.

9. The method according to any one of claims 1 to 8, characterized in that: The multiple lateral partitions corresponding to each sampling position are obtained by segmenting at least one lane of each sampling position in a lateral direction relative to the first vehicle.

10. A path planning device, characterized in that: include: an acquisition module, configured to acquire environmental information of a first area where a first vehicle is located, the first area being within a sensing range of the first vehicle and including at least one lane, and the first vehicle being located in a first lane of the at least one lane; a processing module, configured to determine, according to the environmental information, transverse partition information of each of a plurality of transverse partitions corresponding to each sampling position in at least one sampling position, wherein each sampling position in the at least one sampling position is located ahead of the first vehicle in a driving direction and has a different distance from the position of the first vehicle, and a width of each transverse partition is smaller than a width of the first lane; The processing module is further used to plan the driving path of the first vehicle according to the environmental information and the transverse partition information of each transverse partition in the multiple transverse partitions corresponding to each sampling position.

11. A path planning device, characterized in that: It comprises a memory and a processor, wherein the memory is used to store computer program instructions and data, and the processor is used to execute and call the computer program instructions in the memory so that the path planning device executes the method as described in any one of claims 1-9.

12. A path planning system, characterized in that: Comprising a path planning device as claimed in claim 10 or 11.

13. A vehicle, characterized in that: The vehicle includes the path planning device according to claim 10 or 11, or the vehicle includes the path planning system according to claim 12.

14. A chip, characterized in that: The chip is coupled to a memory, and the chip reads computer program instructions stored in the memory to execute the method according to any one of claims 1 to 9.

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