Track generation method, track display method, equipment, vehicle, medium and chip

Abstract

The embodiment of the invention provides a track generation method, a track display method, equipment, a vehicle, a medium and a chip, and relates to the technical field of automatic driving. The trajectory generation method comprises the steps of determining a target search reference line perpendicular to a trajectory reference line according to a current position of a vehicle and the trajectory reference line; determining two search boundary points on the target search reference line according to the road boundary and the maximum steering wheel angle of the vehicle; determining a plurality of spatial search nodes on a line segment, between the two search boundary points, of the target search reference line; determining a target node for generating a target trajectory of the vehicle according to the plurality of spatial search nodes; and generating a target trajectory of the vehicle according to the target node. The distance between the two search boundary points and the road boundary is larger than a preset safety distance, and the two search boundary points are position points which the vehicle can reach when the vehicle travels from the current position at a steering angle smaller than or equal to the maximum steering angle of the steering wheel.

Description

Technical Field

[0001] The present disclosure relates to the field of autonomous driving technology, and more specifically, to a trajectory generation method, a trajectory display method, a device, a vehicle, a medium, and a chip. Background Art

[0002] With the increasing application of autonomous driving technology, the environment faced by autonomous vehicles during driving is becoming more and more complex. In order to safely and reliably control the operation of autonomous driving vehicles, it is necessary to plan and generate vehicle trajectories and control vehicle driving based on the generated trajectories. However, the trajectory generation methods in related technologies have poor robustness in complex environments, which affects the vehicle's traffic capacity. How to improve the robustness of vehicle trajectory generation has become an urgent problem to be solved. Summary of the invention

[0003] In view of this, the embodiments of the present disclosure propose a new technical solution for trajectory generation.

[0004] According to a first aspect of an embodiment of the present disclosure, a trajectory generation method is provided, the method comprising:

[0005] According to the current position of the vehicle and the trajectory reference line, a target search reference line perpendicular to the trajectory reference line is determined; wherein the trajectory reference line is a reference line for generating a trajectory determined according to a road boundary;

[0006] Determine two search boundary points on the target search reference line according to the road boundary and the maximum steering wheel angle of the vehicle; wherein the distances between the two search boundary points and the road boundary are greater than a preset safety distance, and the two search boundary points are position points that the vehicle can reach when driving from a current position at a steering angle less than or equal to the maximum steering wheel angle;

[0007] Determining a plurality of spatial search nodes on a line segment of the target search reference line located between the two search boundary points;

[0008] Determining a target node for generating a target trajectory of the vehicle according to the plurality of spatial search nodes;

[0009] A target trajectory of the vehicle is generated according to the target node.

[0010] Optionally, determining two search boundary points on the target search reference line according to the road boundary and the maximum steering wheel angle of the vehicle includes:

[0011] Determine a first line segment on the target search reference line according to the road boundary; wherein the distance from two end points of the first line segment to the road boundary closest to the end points is the preset safety distance;

[0012] Determining a second line segment located on the target search reference line according to the maximum steering wheel angle, the current direction angle and the current position of the vehicle;

[0013] Two search boundary points on the target search reference line are determined according to the intersection of the first line segment and the second line segment.

[0014] Optionally, determining a second line segment located on the target search reference line according to the maximum steering wheel angle, the current direction angle and the current position of the vehicle includes:

[0015] Determining a first intersection point when the vehicle travels from a current position to the target search reference line based on a current direction angle of the vehicle and a maximum steering wheel angle of the vehicle in a first direction;

[0016] Determining a second intersection point when the vehicle travels from a current position to the target search reference line based on a current direction angle of the vehicle and a maximum steering wheel angle of the vehicle in a second direction; wherein the first direction and the second direction are opposite directions;

[0017] The second line segment is obtained by taking the first intersection point and the second intersection point as endpoints.

[0018] Optionally, determining a plurality of spatial search nodes on a line segment of the target search reference line between the two search boundary points includes:

[0019] Determine a lateral spacing between two adjacent spatial search nodes according to the distance between the two search boundary points;

[0020] At the position points on the target search reference line that are within the two search boundary points, the multiple spatial search nodes are determined according to the lateral spacing.

[0021] Optionally, determining a target search reference line perpendicular to the trajectory reference line according to the current position of the vehicle and the trajectory reference line includes:

[0022] Generating a plurality of search reference points distributed at intervals on the trajectory reference line; wherein the distance between two adjacent search reference points is a longitudinal spacing determined according to the speed and / or acceleration of the vehicle;

[0023] Based on each of the search reference points, generate a search reference line perpendicular to the trajectory reference line;

[0024] The search reference line closest to the current position of the vehicle among the multiple search reference lines is used as the target search reference line.

[0025] Optionally, the angle between the driving direction of the vehicle when passing through at least one target node and the direction of the trajectory reference line is greater than a preset angle; and / or the target trajectory includes at least an arc curve trajectory between two adjacent target nodes.

[0026] Optionally, determining a target node for generating a target trajectory of the vehicle according to the plurality of spatial search nodes comprises:

[0027] For each of the spatial search nodes, generating a plurality of time attribute information for the spatial search node according to the speed and / or acceleration of the vehicle, and obtaining a plurality of spatiotemporal search nodes corresponding to the spatial search node;

[0028] Based on a preset search target, a target node that meets the search target is searched within the spatiotemporal search node.

[0029] Optionally, the spatiotemporal search node has at least one of the following attribute information: position data, direction angle attribute, curvature attribute, time attribute, speed attribute, acceleration attribute.

[0030] According to a second aspect of an embodiment of the present disclosure, a trajectory display method is provided, the method comprising:

[0031] Generate a target trajectory of the vehicle based on the current position of the vehicle and the trajectory reference line;

[0032] displaying the target trajectory;

[0033] Among them, the target trajectory is generated according to the target node, and the target node is the target node used to generate the target trajectory of the vehicle based on the determination of multiple spatial search nodes; the multiple spatial search nodes are determined on a line segment located between two search boundary points, which is a target search reference line perpendicular to the trajectory reference line determined according to the current position of the vehicle and the trajectory reference line; the two search boundary points are determined according to the road boundary and the maximum steering wheel angle of the vehicle, the trajectory reference line is a reference line for generating the trajectory determined according to the road boundary, the distance between the two search boundary points and the road boundary is greater than a preset safety distance, and the two search boundary points are position points that the vehicle can reach when driving from the current position at a steering angle less than or equal to the maximum steering wheel angle.

[0034] According to a third aspect of an embodiment of the present disclosure, an electronic device is provided, comprising a memory and a processor, wherein the memory is used to store computer instructions, and the processor is used to call the computer instructions from the memory to execute a method as described in any one of the first aspect and / or the second aspect.

[0035] According to a fourth aspect of an embodiment of the present disclosure, a vehicle is provided, comprising a memory and a processor, wherein the memory is used to store computer instructions, and the processor is used to call the computer instructions from the memory to execute a method as described in any one of the first aspect and / or the second aspect.

[0036] According to a fifth aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the method described in any one of the first aspect and / or the second aspect is implemented.

[0037] According to a sixth aspect of an embodiment of the present disclosure, a chip is provided, comprising a processing unit, wherein the processing unit is configured to execute a method as described in any one of the first aspect and / or the second aspect.

[0038] Based on the trajectory generation method provided by the embodiment of the present disclosure, the maximum steering wheel angle of the vehicle and the road boundary in the environmental information are taken into account when determining the two search boundary points of the search reference line. The lateral search range can be adaptively determined according to the steering ability of the vehicle and the environmental information. In narrow channel scenarios such as urban areas or underground parking lots, the lateral search range can be reduced and the lateral search accuracy can be improved to achieve more refined trajectory planning and improve the robustness of vehicle trajectory generation.

[0039] Other features and advantages of the present disclosure will become apparent from the following detailed description of exemplary embodiments of the present disclosure with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the present disclosure and, together with the description, serve to explain the principles of the present disclosure.

[0041] Figure 1 The present invention is a schematic diagram of an intelligent network connection system to which the method provided by the embodiment of the present disclosure can be applied.

[0042] Figure 2 is based on Figure 1 The illustrated embodiment provides a schematic diagram of a vehicle.

[0043] Figure 3 It is a flowchart of a trajectory generation method provided by an embodiment of the present disclosure.

[0044] Figure 4 It is a schematic diagram of a search reference line provided by an embodiment of the present disclosure.

[0045] Figure 5 It is a schematic diagram of a spatial search node provided by an embodiment of the present disclosure.

[0046] Figure 6It is a schematic diagram of a node expansion provided by an embodiment of the present disclosure.

[0047] Figure 7 It is a flowchart of a trajectory generation method provided by an embodiment of the present disclosure.

[0048] Fig. 8A It is a trajectory schematic diagram under a typical working condition provided by an embodiment of the present disclosure.

[0049] Figure 8B It is a trajectory schematic diagram under another typical working condition provided by an embodiment of the present disclosure.

[0050] Figure 8C It is a trajectory diagram under another typical working condition provided by an embodiment of the present disclosure.

[0051] Fig. 9 It is a flowchart of a trajectory display method provided by an embodiment of the present disclosure.

[0052] Fig.10 It is a structural schematic diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0053] Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that the relative arrangement of components and steps, numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present disclosure unless otherwise specifically stated.

[0054] The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the present disclosure, its application, or uses.

[0055] Technologies, methods, and equipment known to ordinary technicians in the relevant art may not be discussed in detail, but where appropriate, the above-mentioned technologies, methods, and equipment should be considered as part of the specification.

[0056] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0057] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0058] First, the application scenarios of the embodiments of the present disclosure are described.

[0059] Figure 1 1 is a schematic diagram of an intelligent network system 100 to which the method provided by the embodiment of the present disclosure can be applied. Figure 1 As shown, the intelligent network connection system 100 may include: a vehicle 101, a server 102, and a user terminal 103.

[0060] In some examples, vehicle 101 may be a vehicle with an autonomous driving function. Autonomous driving is also known as unmanned driving or intelligent driving. Vehicles with autonomous driving functions can achieve driving tasks such as environmental perception, decision planning, and control execution. The level of autonomous driving can refer to the automotive intelligence classification standards formulated by the Society of Automotive Engineers (SAE). For example, L0 is manual driving, L1 is assisted driving, L2 is partial autonomous driving, L3 is conditional autonomous driving, L4 is highly autonomous driving, and L5 is fully autonomous driving. The above classification method for autonomous driving levels is only used as an example, and the disclosed embodiments do not limit the classification standards and levels of autonomous driving.

[0061] In some examples, the server 102 may be a single server or a distributed server cluster consisting of multiple servers, and its deployment method may include a local server or a cloud server. The server 102 may communicate with the vehicle 101 and / or the user terminal 103 based on a communication network, and provide various services for the vehicle 101 and / or the user terminal 103. For example, the server may receive the perception data sent by the vehicle, and provide the vehicle with services such as high-precision maps, data analysis, and decision-making planning. For another example, the server may receive query instructions or control instructions sent by the user terminal, and provide corresponding services to the user.

[0062] In some examples, the user terminal 103 may be any form of electronic device that provides services to users, such as a personal computer, a laptop, a smart tablet, a smart phone, a smart wearable device, etc. The user may interact with the vehicle or server through the human-computer interaction terminal configured in the vehicle 101, or may interact with the vehicle or server through the user terminal 103, for example, querying the status and / or parameters of the vehicle through the user terminal, or controlling the vehicle to perform set tasks and / or modify configuration parameters, etc.; wherein the user terminal runs an application based on the intelligent network connection system to achieve interaction with the vehicle or server, and the application may be a local application, a web application, a small program, etc., which is not limited here.

[0063] In some examples, the above-mentioned application running on the user terminal can provide authentication or authorization services for the user. The user who is successfully authenticated and granted the corresponding authority can query and / or control the vehicle within the scope of the granted authority.

[0064] The vehicle 101, the server 102 and the user terminal 103 can communicate with each other through the communication link provided by the communication network 104. The communication network 104 may include one or more networks of any type, for example, the communication network 104 may include the Internet, a local area network (LAN), a wide area network (WAN), a virtual private network (VPN), a public switched telephone network (PSTN), a satellite communication network, Wi-Fi, 2G, 3G, 4G, 5G, 6G, NB-IoT, eMTC, infrared, Bluetooth, NFC and other networks providing communication, or a combination of the above multiple networks. The communication networks between the vehicle 101 and the server 102, between the user terminal 103 and the server 102, and between the user terminal 103 and the vehicle 101 may be the same or different.

[0065] It should be noted that Figure 1 The structure of the intelligent network connection system 100 shown in the figure is only illustrative. The intelligent network connection system in the embodiment of the present disclosure is not limited to the above structure, and may include more or fewer devices as needed, and may also combine or split the devices. For example, the intelligent network connection system may not include a user terminal and / or a server; for another example, the user terminal and the server may be deployed together.

[0066] Figure 2 is based on Figure 1 The illustrated embodiment provides a schematic diagram of a vehicle 101. Figure 2 As shown, the vehicle 101 may include a perception component 1011, a computing platform 1012, an execution component 1013, etc. The perception component 1011, the computing platform 1012, and the execution component 1013 may be connected via a bus or other means.

[0067] In some examples, the perception component 1011 can be used to collect information about the vehicle itself or outside. The perception component 1011 may include at least one of a visual sensor unit, a radar, a positioning and navigation unit, an inertial measurement unit (IMU) or other sensing units, wherein the visual sensor unit may include one or more cameras, the radar may include at least one of a laser radar, a millimeter wave radar, an ultrasonic radar or other radars, and the positioning and navigation unit may include at least one of a GPS system, a Beidou system or other global positioning systems.

[0068] In some examples, the computing platform 1012 may include a device with computing capabilities, which is used to process the perception information collected by the perception component 1011 to obtain control information, and send corresponding control instructions to the execution component 1013, so that the execution component 1013 performs corresponding actions, thereby realizing control of the vehicle 101. For example, the computing platform 1012 can perform positioning and mapping (Simultaneous Localization and Mapping, SLAM), path planning, behavior decision-making and other behaviors on the vehicle, thereby realizing autonomous control of the vehicle. The computing platform 1012 may include at least one processor and at least one memory, and each processor may execute instructions stored in the memory individually or collectively to implement the method provided in the embodiment of the present disclosure. The processor in the embodiment of the present disclosure may include a central processing unit (CPU), a graphic processing unit (GPU), a neural-network processing unit (NPU), a tensor processing unit (TPU), a data processing unit (DPU), a digital signal processor (DSP), a field programmable gate array (FPGA), a system on chip (SOC), an application specific integrated circuit (ASIC), a microcontroller unit (MCU) or at least one of other processors. The memory can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM), a magnetic memory, a flash memory, a disk or an optical disk. In addition to storing instructions, the memory can also store data, such as high-precision maps, path information, vehicle location, direction, speed and other data. The data stored in the memory can be retrieved and used by the processor.

[0069] In some examples, the vehicle's computing platform can independently perform computing tasks or communicate with a server to complete computing tasks. For example, the vehicle's computing platform can cooperate with the server to complete corresponding computing tasks.

[0070] The computing platform 1012 may be arranged in the vehicle 101, and part or all of the computing platform 1012 may also be arranged in a server corresponding to the vehicle. For example, some functions of the computing platform 1012 with higher real-time requirements may be arranged in the vehicle, and other functions with lower real-time requirements may be arranged in the server corresponding to the vehicle.

[0071] In some examples, the execution component 1013 is used to execute corresponding actions based on the control of the computing platform 1012, so that the vehicle 101 completes the moving task. The execution component 1013 may include, for example, a power component, a brake component, a transmission component, a steering component, and the like.

[0072] It should be noted that Figure 2 The structure of the vehicle 101 shown in the figure is only illustrative. The vehicle in the embodiment of the present disclosure is not limited to the above structure, and may include more or fewer components as needed, and the devices may be combined or split. For example, the vehicle may not include the above computing platform. For another example, the vehicle may also include a communication component, an interface component, a multimedia component, an input component, an output component, etc.

[0073] The disclosed embodiments can be applied to the self-driving vehicle trajectory decision-making and planning scenarios. The environment faced by autonomous vehicles during driving is becoming more and more complex. In order to safely and reliably control the operation of autonomous vehicles, it is necessary to make reasonable trajectory planning decisions for the vehicle to generate the vehicle's own optimal driving route. The result of the generated trajectory will affect the robustness of the vehicle control. For example, a reasonable trajectory decision result can be used as an excellent initial solution for lateral path optimization and improve the lateral path optimization effect; a reasonable trajectory decision result can also enable safer interaction with other vehicles, which is conducive to the speed planning module to perform better overtaking and letting performance. Therefore, how to improve the robustness of vehicle trajectory generation in complex dynamic and static environments has become an urgent problem to be solved.

[0074] In the related art, trajectory planning and decision-making can be performed through multi-layer lattice sampling of dynamic programming (DP) in the Frenet coordinate system to generate the predicted trajectory of the vehicle. However, the lateral sampling resolution in narrow passages based on this method is not enough and cannot adapt to high-resolution search scenarios, resulting in poor trajectory planning in narrow passages in urban areas or underground parking lots, affecting the vehicle's traffic capacity.

[0075] In view of the problems in the related art, an embodiment of the present disclosure provides a trajectory generation method.

[0076] Figure 3 is a flow chart of a trajectory generation method provided by an embodiment of the present disclosure. The trajectory generation method can be Figure 1The vehicle and / or server are executed as shown. Figure 3 As shown, the trajectory generation method of this embodiment may include the following steps S310 to S340.

[0077] Step S310, determining a target search reference line perpendicular to the trajectory reference line according to the current position of the vehicle and the trajectory reference line.

[0078] The track reference line may be a reference line for generating a track determined according to a road boundary. For example, the track reference line may be a road centerline determined according to a road boundary. For example, the track reference line may be a reference line generated by a map module from a current position of the vehicle to a target position. The track reference line may also be referred to as a navigation reference line.

[0079] The target search reference line may be perpendicular to the trajectory reference line. When the trajectory reference line is a curve, the search reference line may be perpendicular to the tangent of the trajectory reference line. It should be noted that in this embodiment, the target search reference line and the trajectory reference line may be perpendicular to each other at an angle of 90 degrees or an angle whose difference from 90 degrees is less than a preset threshold, and the preset threshold may be 5 degrees or 10 degrees.

[0080] In some examples, multiple search reference lines perpendicular to the track reference line may be determined, and the search reference line closest to the current position of the vehicle among the multiple search reference lines may be used as the target search reference line. The multiple search reference lines may be referred to as multi-layer search reference lines. The search reference line may be a vertical line perpendicular to the track reference line.

[0081] For example, multiple search reference points distributed at intervals may be generated on a trajectory reference line; based on each search reference point, a search reference line perpendicular to the trajectory reference line may be generated; and the search reference line closest to the current position of the vehicle among the multiple search reference lines may be used as a target search reference line.

[0082] The distance between two adjacent search reference points may be a longitudinal distance, which may be any preset value, or may be a longitudinal distance determined according to the speed and / or acceleration of the vehicle.

[0083] For example, the greater the speed of the vehicle, the greater the longitudinal spacing; the smaller the speed of the vehicle, the smaller the longitudinal spacing; the greater the acceleration of the vehicle, the greater the longitudinal spacing; the smaller the acceleration of the vehicle, the smaller the longitudinal spacing.

[0084] The longitudinal spacing may be determined according to the vehicle speed, the preset track time length, and the preset number of layers. For example, the longitudinal spacing may be calculated based on the following formula (1):

[0085]

[0086] Among them, L layer Indicates the vertical spacing, v ego represents the speed of the vehicle, t trajectory_length Indicates the preset trajectory time length, that is, the total time length of the trajectory that needs to be planned, such as 2 seconds or 3 seconds, n layer Indicates the number of layers of the preset setting.

[0087] In one implementation, the speed may be the current speed of the vehicle, in which case the longitudinal spacing is determined under the assumption that the vehicle moves at a constant speed. In another implementation, the speed may be the average speed of the vehicle within the time length of the trajectory calculated based on the current speed, current acceleration, and the time length of the trajectory, in which case the longitudinal spacing is determined under the assumption that the vehicle moves at a constant acceleration or constant deceleration.

[0088] In this way, the longitudinal spacing of the search can be flexibly determined according to the speed and / or acceleration of the vehicle. When the vehicle passes through a complex environment such as a narrow passage, the speed is low, and a smaller longitudinal spacing can be determined to perform a more detailed search and improve the precision of trajectory generation.

[0089] Step S320, determining two search boundary points on the target search reference line according to the road boundary and the maximum steering wheel angle of the vehicle.

[0090] The distances between the two search boundary points and the road boundary are greater than a preset safety distance, and the two search boundary points are positions that the vehicle can reach when driving from the current position at a steering angle less than or equal to the maximum steering wheel angle. The maximum steering wheel angle may be a parameter determined based on the vehicle model, and the maximum steering wheel angles may be different or the same for vehicles of different models.

[0091] The preset safety distance may be any preset distance, or the preset safety distance may be determined according to the body width of the vehicle, for example, may be half of the body width.

[0092] In some examples, the road boundary may include passable road boundaries located on both sides of the trajectory reference line, such as a left road boundary and a right road boundary. Optionally, the road boundary may be determined based on the vehicle's environmental perception data and / or map data. The environmental perception data may be data obtained by sensing the vehicle's surrounding environment through sensors during the vehicle's driving process, for example, it may include image data collected by a visual sensor and / or point cloud data collected by a radar. The environmental perception data may be input into a pre-generated neural network model to obtain the road boundary output by the neural network model.

[0093] In some examples, the two search boundary points on the target search reference line can be determined in the following manner: a first line segment on the target search reference line is determined based on the road boundary, and the distances from the two endpoints of the first line segment to the road boundary closest to the endpoints are preset safety distances; a second line segment located on the target search reference line is determined based on the vehicle's maximum steering wheel angle, current direction angle, and current position; and two search boundary points on the target search reference line are determined based on the first line segment and the second line segment.

[0094] In some examples, the two endpoints of the first line segment may be used as two search boundary points. In this way, the maximum lateral search range on the target search reference line may be determined based on the road boundary. It should be noted that the above step of determining the second line segment may not be performed at this time.

[0095] For example, a vertical line perpendicular to the reference line can be drawn at each longitudinal spacing on the track reference line in front of the vehicle in the driving direction, and the vertical line is used as the search reference line. The first line segment is obtained by subtracting a preset safety distance (for example, half of the vehicle body width) from the intersection of the vertical line and the road boundaries on both sides, thereby determining the maximum lateral range at each longitudinal position.

[0096] In other examples, the two endpoints of the second line segment can be used as two search boundary points. In this way, the farthest boundary that the vehicle can reach can be determined based on the maximum steering wheel angle of the vehicle, avoiding invalid lateral search ranges and improving search efficiency. It should be noted that the above step of determining the first line segment may not be performed at this time.

[0097] In some other examples, two search boundary points on the target search reference line may be determined according to the intersection of the first line segment and the second line segment. For example, two endpoints of the intersection of the first line segment and the second line segment may be used as two search boundary points.

[0098] In this way, the maximum lateral search range can be determined based on the road boundary, and the lateral search boundary point can be adjusted according to the vehicle's maximum steering wheel angle (i.e., steering limit), so that the lateral search range can be adaptively adjusted, avoiding searches within invalid ranges while ensuring that the search range is sufficient, thereby improving search efficiency.

[0099] In some examples, the second line segment can be determined in the following manner: based on the current direction angle of the vehicle and the maximum steering wheel angle of the vehicle in the first direction, determine the first intersection point when the vehicle travels from the current position to the target search reference line; based on the current direction angle of the vehicle and the maximum steering wheel angle of the vehicle in the second direction, determine the second intersection point when the vehicle travels from the current position to the target search reference line; with the first intersection point and the second intersection point as endpoints, obtain the second line segment; wherein the first direction and the second direction can be opposite directions, for example, the first direction is the left direction of the trajectory reference line, and the second direction is the right direction of the trajectory reference line, or, the first direction is the right direction of the trajectory reference line, and the second direction is the left direction of the trajectory reference line.

[0100] Step S330, determining a plurality of spatial search nodes on a line segment of the target search reference line between two search boundary points.

[0101] The distance between the two adjacent spatial search nodes may be a horizontal spacing, which may be any preset value, or may be a value determined according to the distance between two search boundary points.

[0102] In some examples, the lateral spacing between two adjacent spatial search nodes can be determined based on the distance between two search boundary points; and multiple spatial search nodes are determined at position points within the two search boundary points on the target search reference line based on the lateral spacing.

[0103] For example, the number of spatial search nodes on each target search reference line can be a pre-set horizontal sampling number. The horizontal spacing between two adjacent spatial search nodes can be calculated based on the horizontal sampling number and the distance between two search boundary points. For example, the horizontal spacing is obtained by dividing the distance between the two search boundary points by the horizontal sampling number. Based on the horizontal spacing, multiple spatial search nodes can be evenly determined.

[0104] In this way, since the maximum steering wheel angle of the vehicle and the road boundaries in the environmental information are taken into account when determining the two search boundary points, the lateral spacing can be adaptively determined according to the vehicle's capabilities and the environment. In narrow channel scenarios such as urban areas or underground parking lots, the lateral search range can be reduced and the lateral search accuracy can be improved to achieve more refined trajectory planning and improve the robustness of vehicle trajectory generation.

[0105] Figure 4 Schematic diagram of a search reference line provided by an embodiment of the present disclosure. Figure 4As shown, based on the current position of the vehicle 101 and the trajectory reference line and based on the longitudinal spacing, multiple search reference lines perpendicular to the trajectory reference line are determined, and a first line segment can be determined on each search reference line. The distances from the two endpoints of the first line segment to the road boundary closest to the endpoints are preset safety distances, and the preset safety distance can be half the width of the vehicle body.

[0106] Further, Figure 5 is a schematic diagram of a spatial search node provided by an embodiment of the present disclosure. Figure 5 As shown, after determining the first line segment on the target search reference line, the left turn limit angle and the right turn limit angle are determined according to the current position of the vehicle 101, the current direction angle and the maximum steering wheel angle of the left turn and the right turn, thereby obtaining the leftmost node and the rightmost node on the target search reference, thereby determining the second line segment, Figure 5 In the scenario shown, the second line segment is completely within the first line segment. Therefore, the two endpoints of the second line segment can be used as two search boundary points. Further, on the line segment of the target search reference line located between the two search boundary points, multiple spatial search nodes are determined based on the lateral spacing, thereby generating high-density lateral discrete points.

[0107] Step S340, determining a target node for generating a target trajectory of the vehicle according to the plurality of spatial search nodes.

[0108] Step S350, generating a target trajectory of the vehicle according to the target node.

[0109] In some examples, a circular arc curve can be generated between the current position of the vehicle and the target node as the target trajectory. In this way, the trajectory is generated based on the circular arc curve without relying on the polynomial algorithm in the Frenet coordinate system, which can avoid the problem of curvature exceeding the limit and causing partial solution loss when performing large curvature sampling in the method of generating trajectories using polynomial algorithms in related technologies, thereby improving the robustness of vehicle trajectory generation.

[0110] In other examples, a target trajectory from the current position of the vehicle to the target node may also be generated based on a polynomial algorithm or other algorithms.

[0111] There may be multiple ways to determine the target node for generating the target trajectory of the vehicle based on the multiple spatial search nodes in the above step S340.

[0112] In some examples, multiple spatial search nodes may be used as nodes to be searched, and based on a preset search target, a target node that meets the search target may be searched within the multiple spatial search nodes.

[0113] The search target can be determined based on multiple search indicators, which may include one or more of lateral comfort, longitudinal comfort, driving efficiency (such as driving time), static obstacle safety cost, dynamic obstacle safety cost, trajectory reference line deviation degree and the like.

[0114] In other examples, for each spatial search node, multiple time attribute information can be generated for the spatial search node based on the speed and / or acceleration of the vehicle, so as to obtain multiple space-time search nodes corresponding to the spatial search node; based on a preset search target, a target node that meets the search target is searched within the multiple space-time search nodes.

[0115] In some examples, the spatial search node can be expanded to obtain the spatiotemporal search node through different acceleration actions.

[0116] Figure 6 FIG. 1 is a schematic diagram of a node expansion provided by an embodiment of the present disclosure. Figure 6 As shown, a target search reference line 105 is determined based on the current position of the vehicle 101, and multiple space search nodes are determined on the target search reference line. The current node (t1, v1) indicates that the time when the vehicle is at its current position is t1 and the speed is v1. For each space search node, a space-time search node can be expanded based on the angular velocities a1 and a2, respectively. For example, the time attributes of the space-time search node obtained after expansion based on the acceleration a1 include (t2, v2), and the time attributes of the space-time search node obtained after expansion based on the acceleration a2 include (t3, v3). In this way, multiple space-time search nodes with space attributes and time attributes can be expanded.

[0117] The expansion process for each space search stage can be expressed as: Among them, n i represents the spatial search node, τ ij represents the trajectory of the vehicle from the current node to the spatial search node based on different accelerations, n j Indicates the effect of different accelerations on n i The spatiotemporal search node obtained after expansion.

[0118] In some examples, the spatiotemporal search node may have at least one of the following attribute information: position data, direction angle attribute, curvature attribute, time attribute, speed attribute, acceleration attribute.

[0119] For example, each spatiotemporal search node can be represented as n=(x, y, θ, k, t, v, a), where

[0120] n represents the space-time search node, x represents the horizontal coordinate, y represents the vertical coordinate, θ represents the direction angle, k represents the curvature, t represents the time, v represents the velocity, and a represents the acceleration.

[0121] In some examples, a target node that meets the search target can be searched in multiple spatiotemporal search nodes based on a preset path search algorithm. The preset path search algorithm can be a heuristic path search algorithm, such as an A-star search algorithm, a Dijkstra algorithm, a BFS (Breadth-First Search) search algorithm, and the like.

[0122] In some examples, the preset path search algorithm may set a search target based on a cost function set in the algorithm, and search for a target node that meets the search target within the plurality of spatiotemporal search nodes to generate a target trajectory for the vehicle.

[0123] The cost function in the preset path search algorithm may include a first cost function and / or a second cost function, and the search target is achieved through the first cost function and / or the second cost function.

[0124] The first cost function may be used to determine the first travel cost. The second cost function may be used to determine the heuristic cost. The search target may be used to indicate the space-time search node with the smallest total cost of searching the first travel cost and the heuristic cost as the target node, that is, the target node may be the node with the smallest cost among the multiple space search nodes of the target search reference line.

[0125] For example, the first driving cost determined by the first cost function may include the second driving cost between nodes, the safety cost, and the trajectory reference line deviation cost. Among them, the characterization parameters of the second driving cost may include lateral comfort, longitudinal comfort and driving efficiency (such as driving time). The safety cost can be determined based on the distance between the vehicle and the obstacle during the process of driving from the current position to the target node. The smaller the distance, the greater the cost, which can be used to indicate the probability of the vehicle colliding with the obstacle. For example, the safety cost may include the static obstacle safety cost and the dynamic obstacle safety cost. The trajectory reference line deviation cost can be determined based on the distance between the target trajectory (such as the trajectory of the vehicle from the current position to the target node) and the trajectory reference line. The greater the distance, the greater the cost.

[0126] Among them, the above-mentioned lateral comfort can be used to indicate the stability of the vehicle when turning. For example, the lateral comfort can be determined based on factors such as the lateral acceleration, steering angle, speed, etc. of the vehicle. The above-mentioned longitudinal comfort can be used to indicate the stability of the vehicle when driving in a straight line or changing speed. For example, the longitudinal comfort can be determined based on factors such as the speed of the vehicle, longitudinal acceleration, time, etc. The above-mentioned driving efficiency can be used to indicate the time required to complete a specific journey (for example, driving from the current position to the target node) or the required energy consumption.

[0127] The above heuristic cost may be determined by the distance from the target node to the end point, and the distance may be a longitudinal distance, such as a longitudinal offset from the end point.

[0128] In this way, based on the preset path search algorithm, a target node that meets the search target is searched in multiple spatiotemporal search nodes, which can improve the accuracy of vehicle trajectory planning.

[0129] In some examples, the preset path search algorithm does not restrict the driving direction of the vehicle passing through the target node, which can be any direction, for example, any direction including forward.

[0130] For example, based on the planned target trajectory, the angle between the driving direction of the vehicle when passing through at least one of the multiple target nodes and the direction of the trajectory reference line is greater than a preset angle. The preset angle can be 0 degrees, or any angle close to 0 degrees, such as 2 degrees or 5 degrees.

[0131] In this way, compared with the sampling method in the related art based on the Frenet coordinate system, in which the sampling of nodes is based on the reference line and the sampling of nodes is constrained to be parallel to the reference line direction, the method provided by the embodiment of the present disclosure, a sampling method based on road guidance in the state space, does not need to constrain the direction angle of the vehicle at the target node to be parallel to the trajectory reference line, and can generate a variety of direction angles at the target node, thereby improving the flexibility of vehicle movement, having better obstacle avoidance capability, and improving the vehicle's traffic capacity.

[0132] In some examples, multiple search reference lines perpendicular to the track reference line can be determined based on the current position of the vehicle and the track reference line, and the target node of the next search reference line can be determined based on the target node of the previous search reference line in order from near to far from the current position of the vehicle. In this way, a target track can be generated based on multiple target nodes. For example, after searching for the last target node, the track of the target node can be traced back to generate the target track.

[0133] Taking the multiple search reference lines including the first search reference line, the second search reference line, ..., and the Nth search reference line as an example, the first search reference line to the Nth search reference line are arranged in order from near to far from the current position of the vehicle, that is, the first search reference line is the search reference line closest to the current position of the vehicle among the multiple search reference lines, and the Nth search reference line is the search reference line farthest from the current position of the vehicle among the multiple search reference lines. Figure 3 The method of steps S310 to S340 shown in the figure determines the first target node on the first search reference line that is closest to the current position of the vehicle. The first target node can be the node with the smallest cost among multiple spatiotemporal search nodes on the first search reference line; then the first target node is taken as the current position and the second search reference line as the target search reference line, and the second target node on the second search reference line is determined; and so on, until the Nth target node on the Nth search reference line is determined.

[0134] In some examples, each target node can be recorded in a priority queue, and when the node exploration reaches the end longitudinal position (for example, the Nth target node is explored), the entire expansion ends. At this time, multiple target nodes recorded in the priority queue are selected for backtracking to obtain the target trajectory of the vehicle as the optimal spatiotemporal search trajectory.

[0135] In some examples, the target trajectory includes at least a circular arc curve trajectory between two adjacent target nodes.

[0136] In this way, the trajectory is generated based on the circular arc curve without relying on the polynomial algorithm in the Frenet coordinate system. This can avoid the problem of curvature exceeding the limit and causing partial solution loss when performing large curvature sampling in the related technology using the polynomial algorithm to generate the trajectory, thereby improving the robustness of vehicle trajectory generation.

[0137] Adopting the technical solution of the above steps S310 to S340, according to the current position of the vehicle and the trajectory reference line, determine a target search reference line perpendicular to the trajectory reference line; according to the road boundary and the maximum steering wheel angle of the vehicle, determine two search boundary points on the target search reference line; on the line segment of the target search reference line located between the two search boundary points, determine multiple spatial search nodes; according to the multiple spatial search nodes, determine the target node for generating the target trajectory of the vehicle; generate the target trajectory of the vehicle according to the target node. Among them, the trajectory reference line is a reference line for generating the trajectory determined according to the road boundary, the distance between the two search boundary points and the road boundary is greater than the preset safety distance, and the two search boundary points are the position points that the vehicle can reach when driving from the current position at a steering angle less than or equal to the maximum steering wheel angle. In this way, the maximum steering wheel angle of the vehicle and the road boundary in the environmental information are taken into account when determining the two search boundary points of the search reference line. The lateral search range can be adaptively determined according to the steering ability of the vehicle and the environmental information. In narrow channel scenes such as urban areas or underground parking lots, the lateral search range can be reduced and the lateral search accuracy can be improved to achieve more refined trajectory planning and improve the robustness of vehicle trajectory generation.

[0138] Figure 7 is a flow chart of a trajectory generation method provided by an embodiment of the present disclosure. The trajectory generation method can be Figure 1 The vehicle and / or server are executed as shown. Figure 7 As shown, the trajectory generation method of this embodiment may include the following steps S710 to S750.

[0139] Step S710, determining the longitudinal spacing according to the vehicle status information and the track reference line, and determining a multi-layer search reference line perpendicular to the track reference line.

[0140] The vehicle status information may include at least one of the vehicle's current position, speed, and acceleration.

[0141] In this step, the longitudinal distance may be determined first according to the speed of the vehicle, and then multiple search reference lines perpendicular to the track reference line may be determined based on the current position and longitudinal distance of the vehicle, with each search reference line serving as a layer.

[0142] In some examples, the method for determining the longitudinal spacing and the method for determining multiple search reference lines can refer to the description in the aforementioned embodiments of the present disclosure, and will not be repeated here.

[0143] Step S720, determining the lateral constraint range of each layer of search reference line.

[0144] For example, the lateral constraint range can be determined according to the road boundary. For example, the lateral constraint range can be obtained by subtracting a preset safety distance (such as half the width of the vehicle body) from the intersection position of each search reference line and the road boundary on both sides. Figure 4 , which shows a schematic diagram of the lateral constraint range of each layer of search reference line produced based on the trajectory reference line and the road boundary.

[0145] In some examples, the lateral constraint range may be the first line segment in the aforementioned embodiment of the present disclosure.

[0146] Step S730, determining the spatial search node of the next layer of search reference line of the vehicle position.

[0147] According to the current vehicle position and the maximum steering wheel angle of the vehicle turning left or right, the two search boundary points of the next layer of search reference line, namely the leftmost node and the rightmost spatial node, are determined. By discretely connecting the leftmost and rightmost spatial nodes, high-density horizontal discrete points are generated, thereby determining all search tree paths to the next layer. Figure 5 It shows the process of determining the leftmost and rightmost nodes and the middle nodes horizontally and reaching the search tree of the next layer through the left and right limit corners.

[0148] Step S740, determining the time attribute of the space search node of the next layer to obtain the spatiotemporal search node.

[0149] After the spatial search nodes of the next layer are determined, they can be expanded to the nodes of the next layer through different acceleration action spaces to obtain spatiotemporal search nodes.

[0150] Step S750, calculating the cost of the spatiotemporal search node, and selecting the target node with the lowest cost from multiple spatiotemporal search nodes for the next round of expansion.

[0151] For example, the total cost of the spatiotemporal search node can be calculated based on the first cost function and / or the second cost function of the preset path search algorithm in the aforementioned embodiment of the present disclosure. The new node can be added to the priority queue, and the node with the lowest cost can be selected as the target node for the next round of spatiotemporal search node expansion.

[0152] Step S760, backtracking the trajectory of the target node to generate a target trajectory.

[0153] The target trajectory can be used as the optimal decision trajectory. For example, when the node exploration reaches the end longitudinal position, the entire expansion ends. At this time, the optimal node in the priority queue is selected for backtracking to obtain the optimal spatiotemporal search trajectory as the target trajectory.

[0154] It should be noted that the specific implementation method of each step in this embodiment can refer to the description in the previous embodiments of the present disclosure and will not be repeated here.

[0155] By adopting the above technical solution, state space sampling is performed under the trajectory reference line. Compared with the action space sampling in related technologies, the sampling space of the algorithm can be reduced and the operation efficiency can be improved; and high-density discretization can be performed in the horizontal direction of the road, which can generate more corner trees and have stronger traffic capacity for narrow channels. Furthermore, this trajectory generation method avoids the strong dependence on the Frenet coordinate system. The sampling of the road guidance based on the state space will generate a variety of corners, which can ensure the flexible movement of the vehicle and have better obstacle avoidance capabilities.

[0156] FIG. 8A to FIG. 8C Schematic diagrams of trajectories under three typical working conditions provided by the embodiments of the present disclosure. Fig. 8A It is a schematic diagram of performing spatiotemporal search and generating a trajectory based on the method of the embodiment of the present disclosure in a large curvature curve scene, which can avoid other vehicles and cones in a large curvature curve scene and generate a safe and reliable large curvature trajectory; Figure 8B In a static obstacle avoidance scenario, a schematic diagram of performing spatiotemporal search and generating a trajectory is generated based on the method of an embodiment of the present disclosure. The generated trajectory can avoid static obstacles, such as the stationary other car in the figure. Figure 8C In a dynamic overtaking scenario, a schematic diagram of performing spatiotemporal search and generating a trajectory is generated based on the method of an embodiment of the present disclosure, and the generated trajectory can avoid dynamic obstacles, such as other moving vehicles.

[0157] Fig. 9 is a flow chart of a trajectory display method provided by an embodiment of the present disclosure. The trajectory display method can be Figure 1 The vehicle and / or server are executed as shown. Fig. 9 As shown, the trajectory generation method of this embodiment may include:

[0158] Step S910, generating a target trajectory of the vehicle according to the current position of the vehicle and the trajectory reference line.

[0159] Step S920, display the target trajectory.

[0160] For example, the target track can be displayed by a display device of the vehicle and / or server. For example, the target track can be displayed on a vehicle-mounted display device at the vehicle end, or the target track can be displayed by a device such as a client (such as a mobile phone).

[0161] Among them, the target trajectory can be generated according to a target node, which is a target node used to generate the target trajectory of the vehicle based on multiple spatial search nodes; the multiple spatial search nodes are determined on a line segment located between two search boundary points, which is a target search reference line perpendicular to the trajectory reference line determined based on the current position of the vehicle and the trajectory reference line; the two search boundary points are determined based on the road boundary and the maximum steering wheel angle of the vehicle, the trajectory reference line is a reference line for generating the trajectory determined based on the road boundary, the distance between the two search boundary points and the road boundary is greater than a preset safety distance, and the two search boundary points are position points that the vehicle can reach when driving from the current position with a steering angle less than or equal to the maximum steering wheel angle.

[0162] In some examples, the specific method of determining the target node can refer to the description in the aforementioned embodiments of the present disclosure and will not be repeated here.

[0163] Using this method, the vehicle's maximum steering wheel angle and the road boundary in the environmental information are taken into account when determining the two search boundary points of the search reference line. The lateral search range can be adaptively determined according to the vehicle's steering ability and environmental information. In narrow channel scenarios such as urban areas or underground parking lots, the lateral search range can be reduced, the lateral search accuracy can be improved, more precise trajectory planning can be achieved, and the robustness of vehicle trajectory generation can be improved.

[0164] Fig.10 Schematic diagram of the structure of an electronic device provided by an embodiment of the present disclosure. Fig.10 As shown, the electronic device 1000 may include a memory 1010 and a processor 1020. The memory 1010 may be used to store computer instructions, and the processor 1020 may be used to call computer instructions from the memory 1010 to execute all or part of the steps of any method in the aforementioned embodiments of the present disclosure. The processor may be one or more, and the one or more processors may execute instructions individually or together. The memory may also be one or more, and the one or more memories may store the above-mentioned computer instructions individually or together. Optionally, the electronic device may be Figure 1 Servers and / or vehicles in.

[0165] The embodiment of the present disclosure also provides a vehicle, which may include a memory and a processor, wherein the memory may be used to store computer instructions, and the processor may be used to call computer instructions from the memory to execute all or part of the steps of any method in the aforementioned embodiment of the present disclosure. The processor may be one or more, and the one or more processors may execute instructions individually or together, and the memory may also be one or more, and the one or more memories may store the above-mentioned computer instructions individually or together.

[0166] The vehicle in the foregoing embodiments of the present disclosure may be an electric vehicle, a hybrid vehicle, a fuel cell vehicle, or other types of vehicles. The vehicle may be an autonomous vehicle or a non-autonomous vehicle. For example, the vehicle provided in this embodiment may be Figure 1 or Figure 2 Vehicle shown.

[0167] The embodiments of the present disclosure also provide a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, any method in the aforementioned embodiments of the present disclosure is implemented. Optionally, the computer-readable storage medium may be a non-transitory storage medium, but is not limited thereto, and may also be a transient storage medium.

[0168] The present disclosure also provides a chip, which may include a processing unit, which may be used to execute all or part of the steps of any method in the foregoing embodiments of the present disclosure. The chip may be a chip in the form of an application-specific integrated chip ASIC, a system on chip SOC, a field programmable gate array FPGA, etc., which is not limited in the present embodiment. Optionally, the chip may also include a storage unit, which may be used to store computer instructions, and the processing unit may be used to call computer instructions from the storage unit to execute all or part of the steps of any method in the foregoing embodiments of the present disclosure.

[0169] The embodiments of the present disclosure further provide a computer program product, which may include a computer program. When the computer program is executed by a processor, any method in the aforementioned embodiments of the present disclosure may be implemented.

[0170] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement any method in the foregoing embodiments of the present disclosure.

[0171] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0172] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0173] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, which may include object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed entirely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be personalized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0174] Various aspects of the present disclosure are described herein with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present disclosure. It should be understood that each box in the flowchart and / or block diagram and the combination of each box in the flowchart and / or block diagram can be implemented by computer-readable program instructions.

[0175] These computer-readable program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so as to produce a machine, so that when these instructions are executed by the processor of the computer or other programmable data processing device, a device for implementing the functions / actions specified in one or more boxes in the flowchart and / or block diagram is generated. These computer-readable program instructions can also be stored in a computer-readable storage medium, and these instructions make the computer, programmable data processing device, and / or other equipment work in a specific manner, so that the computer-readable medium storing the instructions includes a manufactured product, which includes instructions for implementing various aspects of the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0176] Computer-readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device so that a series of operating steps are performed on the computer, other programmable data processing apparatus, or other device to produce a computer-implemented process, thereby causing the instructions executed on the computer, other programmable data processing apparatus, or other device to implement the functions / actions specified in one or more boxes in the flowchart and / or block diagram.

[0177] The flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the systems, methods and computer program products according to multiple embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of an instruction, and the module, a program segment or a part of an instruction contains one or more executable instructions for implementing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions. It should be noted that implementation by hardware, implementation by software, and implementation by a combination of software and hardware are all equivalent.

[0178] The embodiments of the present disclosure have been described above, and the above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and changes will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The choice of terms used herein is intended to best explain the principles of the embodiments, practical applications, or technical improvements to the technology in the market, or to enable other persons of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the present disclosure is defined by the appended claims.

Claims

1. A trajectory generation method, characterized in that: The method comprises: According to the current position of the vehicle and the trajectory reference line, a target search reference line perpendicular to the trajectory reference line is determined; wherein the trajectory reference line is a reference line for generating a trajectory determined according to a road boundary; Determine two search boundary points on the target search reference line according to the road boundary and the maximum steering wheel angle of the vehicle; wherein the distances between the two search boundary points and the road boundary are greater than a preset safety distance, and the two search boundary points are position points that the vehicle can reach when driving from a current position at a steering angle less than or equal to the maximum steering wheel angle; Determining a plurality of spatial search nodes on a line segment of the target search reference line located between the two search boundary points; Determining a target node for generating a target trajectory of the vehicle according to the plurality of spatial search nodes; A target trajectory of the vehicle is generated according to the target node.

2. The method according to claim 1, characterized in that: Determining two search boundary points on the target search reference line according to the road boundary and the maximum steering wheel angle of the vehicle includes: Determine a first line segment on the target search reference line according to the road boundary; wherein the distance from two end points of the first line segment to the road boundary closest to the end points is the preset safety distance; Determining a second line segment located on the target search reference line according to the maximum steering wheel angle, the current direction angle and the current position of the vehicle; Two search boundary points on the target search reference line are determined according to the intersection of the first line segment and the second line segment.

3. The method according to claim 2, characterized in that Determining a second line segment located on the target search reference line according to the maximum steering wheel angle, the current direction angle and the current position of the vehicle includes: Determining a first intersection point when the vehicle travels from a current position to the target search reference line based on a current direction angle of the vehicle and a maximum steering wheel angle of the vehicle in a first direction; Determining a second intersection point when the vehicle travels from a current position to the target search reference line based on a current direction angle of the vehicle and a maximum steering wheel angle of the vehicle in a second direction; wherein the first direction and the second direction are opposite directions; The second line segment is obtained by taking the first intersection point and the second intersection point as endpoints.

4. The method according to claim 1, characterized in that Determining a plurality of spatial search nodes on a line segment of the target search reference line between the two search boundary points comprises: Determine a lateral spacing between two adjacent spatial search nodes according to the distance between the two search boundary points; At the position points on the target search reference line that are within the two search boundary points, the multiple spatial search nodes are determined according to the lateral spacing.

5. The method according to claim 1, characterized in that The step of determining a target search reference line perpendicular to the trajectory reference line according to the current position of the vehicle and the trajectory reference line comprises: Generating a plurality of search reference points distributed at intervals on the trajectory reference line; wherein the distance between two adjacent search reference points is a longitudinal spacing determined according to the speed and / or acceleration of the vehicle; Based on each of the search reference points, generate a search reference line perpendicular to the trajectory reference line; The search reference line closest to the current position of the vehicle among the multiple search reference lines is used as the target search reference line.

6. The method according to claim 5, characterized in that The angle between the driving direction of the vehicle when passing through at least one target node and the direction of the trajectory reference line is greater than a preset angle; and / or, The target trajectory at least includes an arc curve trajectory between two adjacent target nodes.

7. The method according to any one of claims 1 to 6, characterized in that The step of determining a target node for generating a target trajectory of the vehicle according to the plurality of spatial search nodes comprises: For each of the spatial search nodes, generating a plurality of time attribute information for the spatial search node according to the speed and / or acceleration of the vehicle, and obtaining a plurality of spatiotemporal search nodes corresponding to the spatial search node; Based on a preset search target, a target node that meets the search target is searched within the spatiotemporal search node.

8. The method according to claim 7, characterized in that The spatiotemporal search node has at least one of the following attribute information: position data, direction angle attribute, curvature attribute, time attribute, speed attribute, and acceleration attribute.

9. A trajectory display method, characterized in that: The method comprises: Generate a target trajectory of the vehicle based on the current position of the vehicle and the trajectory reference line; displaying the target trajectory; Among them, the target trajectory is generated according to the target node, and the target node is the target node used to generate the target trajectory of the vehicle based on the determination of multiple spatial search nodes; the multiple spatial search nodes are determined on a line segment located between two search boundary points, which is a target search reference line perpendicular to the trajectory reference line determined according to the current position of the vehicle and the trajectory reference line; the two search boundary points are determined according to the road boundary and the maximum steering wheel angle of the vehicle, the trajectory reference line is a reference line for generating the trajectory determined according to the road boundary, the distance between the two search boundary points and the road boundary is greater than a preset safety distance, and the two search boundary points are position points that the vehicle can reach when driving from the current position at a steering angle less than or equal to the maximum steering wheel angle.

10. An electronic device, characterized in that: The invention comprises a memory and a processor, wherein the memory is used to store computer instructions, and the processor is used to call the computer instructions from the memory to execute: the method according to any one of claims 1 to 9.

11. A vehicle, characterized in that: The invention comprises a memory and a processor, wherein the memory is used to store computer instructions, and the processor is used to call the computer instructions from the memory to execute: the method according to any one of claims 1 to 9.

12. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the computer program implements: the method according to any one of claims 1 to 9.

13. A chip, characterized in that: The chip comprises: the chip comprises a processing unit, and the processing unit is used to execute the method as claimed in any one of claims 1 to 9.

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