Trajectory generation method, trajectory display method, device, vehicle, medium and chip

Abstract

Embodiments of the present disclosure provide a trajectory generation method, a trajectory display method, an equipment, a vehicle, a medium and a chip, and relate to the technical field of autonomous driving. The trajectory generation method comprises: 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 a road boundary and a maximum steering wheel turning angle of the vehicle; determining a plurality of space search nodes on a line segment between the two search boundary points on the target search reference line; determining a target node for generating a target trajectory of the vehicle according to the plurality of space search nodes; and generating the target trajectory of the vehicle according to the target node. 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 can be reached by the vehicle when the vehicle travels from the current position at a turning angle less than or equal to the maximum steering wheel turning angle.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of automatic driving, and more particularly, to a trajectory generation method, a trajectory display method, an apparatus, a vehicle, a medium and a chip. BACKGROUND

[0002] With the increasing application of automatic driving technology, the environment faced by an automatic driving vehicle in driving is more and more complex. In order to safely and reliably control the operation of the automatic driving vehicle, it is necessary to plan to generate a trajectory of the vehicle and control the vehicle to drive based on the generated trajectory. However, the trajectory generated by the trajectory generation method in the related art has poor robustness in a complex environment, which affects the traffic capacity of the vehicle. How to improve the robustness of the trajectory generation of the vehicle becomes a problem to be solved. SUMMARY

[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 the embodiments of the present disclosure, a trajectory generation method is provided, and the method comprises:

[0005] 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; wherein the trajectory reference line is a reference line for generating a trajectory determined according to a road boundary;

[0006] determining two search boundary points on the target search reference line according to the road boundary and a maximum steering wheel turning angle of the vehicle; wherein 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 can be reached by the vehicle when driving from the current position at a turning angle less than or equal to the maximum steering wheel turning angle;

[0007] determining a plurality of space search nodes on a line segment of the target search reference line between the two search boundary points;

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

[0009] generating the target trajectory of the vehicle according to the target node.

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

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

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

[0013] determining two search boundary points on the target search reference line according to an intersection of the first line segment and the second line segment.

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

[0015] determining a first intersection point of the vehicle from the current position to the target search reference line based on the 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 of the vehicle from the current position to the target search reference line based on the 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] obtaining the second line segment with the first intersection point and the second intersection point as end points.

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

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

[0020] determining the plurality of spatial search nodes among position points on the target search reference line within the two search boundary points according to the lateral distance.

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

[0022] generating a plurality of search reference points on the trajectory reference line in an interval distribution; wherein a distance between two adjacent search reference points is a longitudinal distance determined according to a speed and / or acceleration of the vehicle;

[0023] generating a search reference line perpendicular to the trajectory reference line based on each search reference point;

[0024] taking a search reference line closest to the current position of the vehicle among the plurality of search reference lines as the target search reference line.

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

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

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

[0028] Based on a preset search target, a target node meeting the search target is searched in 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, and acceleration attribute.

[0030] According to a second aspect of the embodiments of the present disclosure, a trajectory display method is provided, and the method includes:

[0031] generating a target trajectory of the vehicle according to a current position of the vehicle and a trajectory reference line;

[0032] displaying the target trajectory;

[0033] The target trajectory is generated according to a target node, and the target node is determined according to a plurality of spatial search nodes used for generating the target trajectory of the vehicle. The plurality of spatial search nodes are determined on a line segment between two search boundary points on a target search reference line perpendicular to the trajectory reference line, which is determined according to a current position of the vehicle and the trajectory reference line. The two search boundary points are determined according to a road boundary and a maximum steering wheel turning angle of the vehicle. The trajectory reference line is a reference line used for generating a trajectory, which is determined according to a 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 can be reached by the vehicle when driving from the current position at a turning angle less than or equal to the maximum steering wheel turning angle.

[0034] According to a third aspect of the embodiments of the present disclosure, an electronic device is provided, including a memory and a processor. The memory is configured to store computer instructions, and the processor is configured to call the computer instructions from the memory to execute the method according to any one of the first aspect and / or the second aspect.

[0035] According to a fourth aspect of the embodiments of the present disclosure, a vehicle is provided, comprising a memory and a processor, the memory is configured to store computer instructions, and the processor is configured to invoke the computer instructions from the memory to execute the method according to any one of the first aspect and / or the second aspect.

[0036] According to a fifth aspect of the embodiments of the present disclosure, a computer readable storage medium is provided, and the computer readable storage medium stores a computer program, and the computer program is configured to implement the method according to any one of the first aspect and / or the second aspect when executed by a processor.

[0037] According to a sixth aspect of the embodiments of the present disclosure, a chip is provided, and the chip comprises a processing unit, and the processing unit is configured to execute the method according to any one of the first aspect and / or the second aspect.

[0038] Based on the trajectory generation method provided by the embodiments of the present disclosure, the maximum steering wheel turning angle of the vehicle and the road boundary in the environmental information are considered when determining the two search boundary points of the search reference line, the search range in the lateral direction can be adaptively determined according to the self-steering capability of the vehicle and the environmental information, in the narrow channel scene such as urban area or underground parking lot, the search range in the lateral direction can be reduced, the search accuracy in the lateral direction can be improved, more accurate trajectory planning can be realized, and the robustness of the trajectory generation of the vehicle can be improved.

[0039] Other features of the present disclosure and advantages thereof will become more apparent from the following detailed description of exemplary embodiments thereof with reference to the drawings. BRIEF DESCRIPTION OF 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 FIG. 1 is a schematic diagram of an intelligent network system to which the method provided by the embodiments of the present disclosure can be applied.

[0042] Figure 2 FIG. 2 is a schematic diagram of a vehicle provided by the embodiments of the present disclosure. Figure 1

[0043] Figure 3 FIG. 3 is a flowchart of a trajectory generation method provided by the embodiments of the present disclosure.

[0044] Figure 4 FIG. 4 is a schematic diagram of a search reference line provided by the embodiments of the present disclosure.

[0045] Figure 5 FIG. 5 is a schematic diagram of a space search node provided by the embodiments of the present disclosure.

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

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

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

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

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

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

[0052] Figure 10 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. Note that the relative arrangement, numerical expressions, and numerical values of components and steps set forth in these embodiments are not limiting to the scope of the present disclosure unless otherwise specifically stated.

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

[0055] Techniques, methods, and devices known to those of ordinary skill in the relevant art can not be discussed in detail herein, but should be considered part of the specification.

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

[0057] Note that similar reference numerals and letters refer to similar items in the following drawings, and thus, once they are defined in one drawing, they do not need to be discussed further in subsequent drawings.

[0058] First, the application scenario of an embodiment of the present disclosure is described.

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

[0060] In some examples, the vehicle 101 can be a vehicle with automatic driving function. The automatic driving, also known as unmanned driving or intelligent driving, is a vehicle with automatic driving function that can realize driving tasks such as environment perception, decision planning, and control execution. The level of automatic driving can refer to the intelligent grading standard of the Society of Automotive Engineers (SAE), for example, L0 level is manual driving, L1 is auxiliary driving, L2 is partial automatic driving, L3 is conditional automatic driving, L4 is high automatic driving, and L5 is complete automatic driving. The above classification method of automatic driving level is only an example, and the classification standard and level of automatic driving are not limited in the embodiments of the present disclosure.

[0061] In some examples, the server 102 can be a single server or a distributed server cluster composed of multiple servers, and the deployment mode can include a local server or a cloud server. The server 102 can 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 can receive the perception data sent by the vehicle, and provide services such as high-precision map, data analysis, decision planning, etc. for the vehicle, and for example, the server can receive the query instruction or control instruction sent by the user terminal, and provide corresponding services for the user.

[0062] In some examples, the user terminal 103 can be any form of electronic device that provides services for users, such as personal computers, notebook computers, smart tablets, smart phones, smart wearable devices, etc. The user can interact with the vehicle or the server through the human-computer interaction terminal configured by the vehicle 101, or interact with the vehicle or the server through the user terminal 103, for example, query the state and / or parameters of the vehicle through the user terminal, or control the vehicle to perform a set task and / or modify a configuration parameter, etc. The user terminal runs an application based on the intelligent network system to realize the interaction between the vehicle or the server, and the application can be a local application, a web application or a mini 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, and the user who successfully authenticates and is granted corresponding permissions can query and / or control the vehicle within the scope of the granted permissions.

[0064] Vehicle 101, server 102, and user terminal 103 can communicate via a communication link provided by communication network 104. This communication network 104 can include one or more networks of any type, such as the Internet, Local Area Network (LAN), Wide Area Network (WAN), Virtual Private Network (VPN), Public Switched Telephone Network (PSTN), satellite communication network, Wi-Fi, 2G, 3G, 4G, 5G, 6G, NB-IoT, eMTC, infrared, Bluetooth, NFC, or a combination of these networks. The communication networks between vehicle 101 and server 102, between user terminal 103 and server 102, and between user terminal 103 and vehicle 101 can be the same or different.

[0065] It should be noted that, Figure 1 The structure of the intelligent connected system 100 shown is merely illustrative. The intelligent connected system in this embodiment is not limited to the above structure and may include more or fewer devices as needed, and the devices may be combined or split. For example, the intelligent connected system may not include user terminals and / or servers; as another example, user terminals and servers may be deployed together.

[0066] Figure 2 It is based on Figure 1 The illustrated embodiment provides a schematic diagram of a vehicle 101. (As shown) Figure 2 As shown, the vehicle 101 may include a sensing component 1011, a computing platform 1012, an execution component 1013, etc. The sensing 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 its external environment. The perception component 1011 may include at least one of a visual sensing unit, radar, a positioning and navigation unit, an inertial measurement unit (IMU), or other sensing units. The visual sensor unit may include one or more cameras, the radar may include at least one of lidar, millimeter-wave radar, 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 can include a device with computing capability for processing the perception information collected by the perception component 1011 to obtain control information, and sending corresponding control instructions to the execution component 1013 to enable the execution component 1013 to perform corresponding actions, thereby achieving control of the vehicle 101. For example, the computing platform 1012 can perform behaviors such as simultaneous localization and mapping (SLAM), path planning, behavior decision making, etc. for the vehicle, thereby achieving autonomous control of the vehicle. The computing platform 1012 can include at least one processor and at least one memory, and each processor can execute instructions stored in the memory individually or jointly to implement the method provided by the embodiments of the present disclosure. The processor in the embodiments of the present disclosure can include at least one of a central processing unit (CPU), a graphic process 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 micro controller unit (MCU), or 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 magnetic disk, or an optical disk. In addition to storing instructions, the memory can also store data, such as high-definition maps, path information, positions, directions, speeds, etc. of the vehicle. The data stored in the memory can be obtained and used by the processor.

[0069] In some examples, the computing platform of the vehicle can independently perform a computing task, or can complete the computing task by communicating with a server. For example, the computing platform of the vehicle can cooperate with the server to complete a corresponding computing task.

[0070] The computing platform 1012 can be arranged in the vehicle 101, and part or all of the computing platform 1012 can also be arranged in the server corresponding to the vehicle, for example, part of the computing platform 1012 with high real-time requirement is arranged in the vehicle, and the other part of the computing platform 1012 with low real-time requirement is arranged in the server corresponding to the vehicle.

[0071] In some examples, the execution component 1013 is configured to perform corresponding actions based on the control of the computing platform 1012, so that the vehicle 101 completes the moving task. The execution component 1013 can 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 FIG. 1 is only schematic, and the vehicle in the embodiments of the present disclosure is not limited to the above structure, and can further include more or fewer components, or the devices can be combined or split. For example, the vehicle can not include the computing platform described above. For another example, the vehicle can further include a communication component, an interface component, a multimedia component, an input component, an output component, and the like.

[0073] The embodiments of the present disclosure can be applied to the ego trajectory decision planning scenario of an autonomous vehicle. The environment faced by the autonomous vehicle in driving is becoming more and more complex, in order to safely and reliably control the operation of the autonomous vehicle, it is necessary to make reasonable trajectory planning and decision for the vehicle to generate the best driving route of the vehicle itself. The result of generating the 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, improving the effect of lateral path optimization; a reasonable trajectory decision result can also interact more safely with other vehicles, which is beneficial to the better performance of the speed planning module in overtaking and yielding. Therefore, how to improve the robustness of the trajectory generation of the vehicle in a complex dynamic and static environment has become a problem to be solved.

[0074] In the related art, trajectory planning and decision can be performed by multi-layer lattice sampling of dynamic programming (DP) in the frenet coordinate system to generate a predicted trajectory of the vehicle. However, the lateral sampling resolution based on this way is not enough in a narrow channel, which cannot adapt to a high-resolution search scene, resulting in poor trajectory planning in a narrow channel in an urban area or an underground parking lot, affecting the traffic capacity of the vehicle.

[0075] To solve the problems in the related art, the embodiments of the present disclosure provide a trajectory generation method.

[0076] Figure 3 FIG. 1 is a flowchart of a trajectory generation method provided by the embodiments of the present disclosure. The trajectory generation method can be performed by a trajectory generation device, such as the computing platform 1012 shown in FIG. 1. Figure 1The vehicle and / or the server shown perform. As Figure 3 As shown, the trajectory generation method of the embodiment can include the following steps S310 to S340.

[0077] In step S310, a target search reference line perpendicular to a trajectory reference line is determined according to a current position of the vehicle and the trajectory reference line.

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

[0079] The target search reference line can be perpendicular to the trajectory reference line. In the case where the trajectory reference line is a curve, the search reference line can be perpendicular to the tangent of the trajectory reference line. It should be noted that in the embodiment, the target search reference line and the trajectory reference line being perpendicular can mean that the two are at an angle of 90 degrees or an angle less than 90 degrees by a preset threshold value, which can be 5 degrees or 10 degrees.

[0080] In some examples, a plurality of search reference lines perpendicular to the trajectory reference line can be determined, and the search reference line closest to the current position of the vehicle among the plurality of search reference lines can be taken as the target search reference line. The plurality of search reference lines can be referred to as a plurality of layers of search reference lines. The search reference line can be a perpendicular line perpendicular to the trajectory reference line.

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

[0082] The distance between two adjacent search reference points can be a longitudinal interval. The longitudinal interval can be any value set in advance, or the longitudinal interval can also be a longitudinal interval 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 interval; the smaller the speed of the vehicle, the smaller the longitudinal interval; the greater the acceleration of the vehicle, the greater the longitudinal interval; and the smaller the acceleration of the vehicle, the smaller the longitudinal interval.

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

[0085]

[0086] wherein, L layer represents a longitudinal distance, v ego represents a speed of the vehicle, t trajectory_length represents a preset track time length, i.e., a total time length of the track currently needed to be planned, for example, 2 seconds or 3 seconds, n layer represents a preset number of layers.

[0087] In an implementation manner, the speed can be a current speed of the vehicle, and the longitudinal distance is determined in a case of assuming that the vehicle moves at a constant speed. In another implementation manner, the speed can be an average speed of the vehicle in the track time length, which is calculated according to the current speed, the current acceleration and the track time length of the vehicle, and the longitudinal distance is determined in a case of assuming that the vehicle moves at a constant acceleration or a constant deceleration.

[0088] In this way, the searched longitudinal distance 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 channel, the speed is low, and a smaller longitudinal distance can be determined to perform more fine search and improve the fineness of track generation.

[0089] In step S320, two search boundary points on the target search reference line are determined according to the road boundary and the maximum steering wheel turning angle of the vehicle.

[0090] wherein, 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 can be reached by the vehicle when the vehicle travels from the current position at a turning angle less than or equal to the maximum steering wheel turning angle. The maximum steering wheel turning angle can be a parameter determined based on the vehicle model, and the maximum steering wheel turning angles of different models of vehicles can be different or the same.

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

[0092] In some examples, the road boundary can include passable road boundaries respectively located on both sides of the track reference line, for example, a left road boundary and a right road boundary. Optionally, the road boundary can be determined according to environmental perception data and / or map data of the vehicle. The environmental perception data can be data obtained by sensing the surrounding environment of the vehicle through sensors during the driving of the vehicle, for example, can include image data based on a visual sensor and / or point cloud data based on a radar. The environmental perception data can 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 as follows: determining a first line segment on the target search reference line according to the road boundary, two end points of the first line segment being at a preset safety distance from the road boundary closest to the end points; determining a second line segment on the target search reference line according to the maximum steering wheel turning angle, the current direction angle and the current position of the vehicle; and determining the two search boundary points on the target search reference line according to the first line segment and the second line segment.

[0094] In some examples, the two end points of the first line segment can be taken as the two search boundary points. In this way, the maximum lateral search range on the target search reference line can be determined based on the road boundary. It should be noted that in this case, the step of determining the second line segment can not be performed.

[0095] For example, a perpendicular line can be drawn at each position on the trajectory reference line in front of the vehicle in the driving direction, the perpendicular line being perpendicular to the reference line, and the perpendicular line being taken as the search reference line. The position where the perpendicular line intersects the road boundary on both sides can be subtracted by a preset safety distance (for example, half of the vehicle body width), to obtain the first line segment, thereby determining the maximum lateral range at each longitudinal position.

[0096] In other examples, the two end points of the second line segment can be taken as the two search boundary points. In this way, the farthest boundary that can be reached by the vehicle can be determined based on the maximum steering wheel turning angle of the vehicle, and invalid lateral search range can be avoided, thereby improving the search efficiency. It should be noted that in this case, the step of determining the first line segment can not be performed.

[0097] In yet other examples, the two search boundary points on the target search reference line can be determined according to the intersection of the first line segment and the second line segment. For example, the two end points of the intersection line segment of the first line segment and the second line segment can be taken as the 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 points can be adjusted according to the maximum steering wheel turning angle (i.e., the steering limit) of the vehicle, so that the lateral search range can be adaptively adjusted, and the search in the invalid range can be avoided on the basis of ensuring that the search range is sufficient, thereby improving the search efficiency.

[0099] In some examples, the second line segment can be determined as follows: determining a 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 turning angle of the vehicle in a first direction; determining a second 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 turning angle of the vehicle in a second direction; and obtaining the second line segment with the first intersection point and the second intersection point as end points; wherein the first direction and the second direction can be opposite directions, for example, the first direction is a left direction of the trajectory reference line, and the second direction is a right direction of the trajectory reference line, or the first direction is a right direction of the trajectory reference line, and the second direction is a left direction of the trajectory reference line.

[0100] In step S330, a plurality of spatial search nodes are determined on the line segment of the target search reference line between the two search boundary points.

[0101] The distance between the two adjacent spatial search nodes can be a lateral distance. The lateral distance can be any value set in advance, or the lateral distance can also be a value determined according to the distance of the two search boundary points.

[0102] In some examples, the lateral distance between the two adjacent spatial search nodes can be determined according to the distance of the two search boundary points; and the plurality of spatial search nodes can be determined according to the lateral distance among the position points on the target search reference line within the two search boundary points.

[0103] For example, the number of spatial search nodes on each target search reference line can be a preset lateral sampling number, and the lateral distance between the two adjacent spatial search nodes can be calculated based on the lateral sampling number and the distance of the two search boundary points, for example, the lateral distance is obtained by dividing the distance of the two search boundary points by the lateral sampling number, and the plurality of spatial search nodes can be uniformly determined according to the lateral distance.

[0104] In this way, since the determination of the two search boundary points takes into account the maximum steering wheel turning angle of the vehicle and the road boundary in the environmental information, the lateral distance can be adaptively determined according to the self-vehicle capability and the environment, the lateral search range can be reduced in narrow channel scenarios such as urban areas or underground parking lots, the lateral search accuracy can be improved, and more accurate trajectory planning can be achieved to improve the robustness of vehicle trajectory generation.

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

[0106] Further, Figure 5 is a schematic diagram of a space search node provided by an embodiment of the present disclosure. As Figure 5 As shown, after the first line segment on the target search reference line is determined, the left turn limit angle and the right turn limit angle are determined according to the current position, the current direction angle and the maximum steering wheel turning angle of the vehicle 101, so as to obtain the leftmost node and the rightmost node on the target search reference, so as to determine the second line segment, Figure 5 As shown in the scenario, the second line segment is completely within the first line segment, so the two endpoints of the second line segment can be used as two search boundary points, and further based on the lateral distance, a plurality of space search nodes are determined on the line segment of the target search reference line between the two search boundary points, so as to generate a high-density lateral discrete point.

[0107] Step S340, determining a target node for generating a target trajectory of the vehicle according to the plurality of space 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 loss of part of the solution caused by curvature overrun when a large curvature sampling is performed in the related art, and improve the robustness of the vehicle trajectory generation.

[0110] In other examples, the target trajectory between the current position of the vehicle and the target node can also be generated based on a polynomial algorithm or other algorithm.

[0111] The way of determining the target node for generating the target trajectory of the vehicle according to the plurality of space search nodes in step S340 can be various.

[0112] In some examples, the plurality of space search nodes can be used as search nodes, and a target node meeting a preset search target can be searched in the plurality of space search nodes.

[0113] The search target can be determined based on a plurality of search indexes, which can include one or more of lateral comfort, longitudinal comfort, travel efficiency (e.g., travel time), static obstacle safety cost, dynamic obstacle safety cost, trajectory reference line deviation degree, etc.

[0114] In some examples, a plurality of time attribute information can be generated for each spatial search node according to the speed and / or acceleration of the vehicle, to obtain a plurality of space-time search nodes corresponding to the spatial search node; based on a preset search target, a target node meeting the search target is searched from the plurality of space-time search nodes.

[0115] In some examples, the space-time search nodes can be expanded from the spatial search nodes through different acceleration actions.

[0116] Figure 6 is a schematic diagram of node expansion provided by an embodiment of the present disclosure. As shown in Figure 6 , a target search reference line 105 is determined based on the current position of the vehicle 101, and a plurality of spatial search nodes are determined on the target search reference line. The current node (t1, v1) represents the time t1 and the speed v1 of the vehicle at the current position. For each spatial search node, a space-time search node can be expanded based on the angular velocities a1 and a2, for example, the time attribute of the space-time search node expanded based on the acceleration a1 includes (t2, v2), and the time attribute of the space-time search node expanded based on the acceleration a2 includes (t3, v3). In this way, a plurality of space-time search nodes with spatial attributes and time attributes can be expanded.

[0117] The expansion process for each spatial search stage can be represented as: wherein n i represents a 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 represents the space-time search node obtained by expanding n i through different accelerations.

[0118] In some examples, the space-time search node can 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 space-time search node can be represented as n = (x, y, θ, k, t, v, a), wherein,

[0120] n represents a spatiotemporal search node, x represents a horizontal coordinate, y represents a vertical coordinate, θ represents a direction angle, k represents a curvature, t represents a time, v represents a speed, and a represents an acceleration.

[0121] In some examples, a target node meeting a search target can be searched in the plurality of 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) algorithm, or the like.

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

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

[0124] The first cost function can be used to determine a first travel cost. The second cost function can be used to determine a heuristic cost. The search target can be used to indicate a spatiotemporal search node with a minimum total cost of the first travel cost and the heuristic cost as the target node, that is, the target node can be a node with a minimum cost in the plurality of spatial search nodes of the target search reference line.

[0125] For example, the first travel cost determined by the first cost function can include a second travel cost between nodes, a safety cost, and a trajectory reference line deviation cost. The representative parameters of the second travel cost can include lateral comfort, longitudinal comfort, and travel efficiency (e.g., travel time), the safety cost can be determined according to the distance between the vehicle and the obstacle during the travel 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 collision between the vehicle and the obstacle. For example, the safety cost can include a static obstacle safety cost and a dynamic obstacle safety cost. The trajectory reference line deviation cost can be determined according to the distance between the target trajectory (e.g., 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] The lateral comfort can be used to indicate the stability of the vehicle when turning. For example, the lateral comfort can be determined according to factors such as the lateral acceleration, steering angle, speed, and the like of the vehicle. The 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 according to factors such as the speed, longitudinal acceleration, time, and the like of the vehicle. The driving efficiency can be used to indicate the time required or the energy consumption required to complete a specific journey (for example, from the current position to the target node).

[0127] The heuristic cost can be determined by the distance from the target node to the end point, which can be a longitudinal distance, for example, a longitudinal offset from the end point.

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

[0129] In some examples, the preset path search algorithm does not constrain the driving direction of the vehicle when driving 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 driving through at least one of the plurality of 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, for example, 2 degrees or 5 degrees, and the like.

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

[0132] In some examples, a plurality of search reference lines perpendicular to the trajectory reference line can be determined according to the current position of the vehicle and the trajectory reference line, and the target nodes of the search reference lines can be determined in sequence according to the order from near to far of the plurality of search reference lines from the current position of the vehicle. For example, after the last target node is searched, the target trajectory can be generated by backtracking the trajectory of the target node.

[0133] For example, the plurality of search reference lines include a first search reference line, a second search reference line, …, and an Nth search reference line. 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, i.e., the first search reference line is the search reference line closest to the current position of the vehicle, and the Nth search reference line is the search reference line farthest from the current position of the vehicle. The first search reference line can be determined based on the following method. Figure 3 The method shown in steps S310 to S340 determines the first target node on the first search reference line closest to the current position of the vehicle. The first target node can be the node with the minimum cost among the plurality of spatiotemporal search nodes on the first search reference line. Then, the first target node is taken as the current position, the second search reference line is taken as the target search reference line, and the second target node on the second search reference line is determined. The same is true for the Nth target node on the Nth search reference line.

[0134] In some examples, each target node can be recorded in a priority queue. When the node exploration reaches the end longitudinal position (e.g., the Nth target node), the entire expansion ends. At this time, the plurality of target nodes recorded in the priority queue are selected for backtracking, and the target trajectory of the vehicle is obtained 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 loss of part of the solution caused by the curvature over-limit when the trajectory is generated by the polynomial algorithm in the related art, and improve the robustness of the trajectory generation of the vehicle.

[0137] According to the current position of the vehicle and the trajectory reference line, the target search reference line perpendicular to the trajectory reference line is determined; according to the road boundary and the maximum steering wheel turning angle of the vehicle, two search boundary points on the target search reference line are determined; on the line segment of the target search reference line between the two search boundary points, a plurality of spatial search nodes are determined; according to the plurality of spatial search nodes, a target node for generating a target trajectory of the vehicle is determined; and according to the target node, the target trajectory of the vehicle is generated. The trajectory reference line is a reference line for generating a 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 position points that can be reached by the vehicle when the vehicle travels from the current position at a turning angle less than or equal to the maximum steering wheel turning angle. In this way, when determining the two search boundary points of the search reference line, the maximum steering wheel turning angle of the vehicle and the road boundary in the environmental information are considered, and the lateral search range can be adaptively determined according to the steering ability of the ego vehicle and the environmental information. In a narrow channel scene such as an urban area or an underground parking lot, the lateral search range can be reduced, the lateral search accuracy can be improved, more accurate trajectory planning can be achieved, and the robustness of the vehicle trajectory generation can be improved.

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

[0139] Step S710, determining a longitudinal distance according to the state information of the vehicle and the trajectory reference line, and determining a plurality of search reference lines perpendicular to the trajectory reference line.

[0140] The state information of the vehicle can include at least one of the current position, speed and acceleration of the vehicle.

[0141] In this step, the longitudinal distance can be determined according to the speed of the vehicle, and then a plurality of search reference lines perpendicular to the trajectory reference line are determined based on the current position of the vehicle and the longitudinal distance, each search reference line being a layer.

[0142] In some examples, the determination method of the longitudinal distance and the determination method of the plurality of search reference lines can refer to the description in the foregoing embodiments of the present disclosure, which will not be described here.

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

[0144] ​For example, the lateral constraint range can be determined according to the road boundary, for example, the intersection position of each search reference line and the road boundary on both sides can be reduced by a preset safety distance (for example, half of the vehicle body width) to obtain the above-mentioned lateral constraint range. As shown in Figure 4 A schematic diagram for producing the lateral constraint range of each layer search reference line based on the trajectory reference line and the road boundary is shown.

[0145] In some examples, the lateral constraint range can be the first line segment in the foregoing embodiments of the present disclosure.

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

[0147] According to the current vehicle position and the maximum steering wheel turning angle of the left and right turning of the vehicle, two search boundary points of the next layer search reference line, i.e., the leftmost node and the rightmost spatial node, are determined, a high-density lateral discrete point is generated through the connection line between the discrete leftmost and rightmost spatial nodes, and all search tree paths reaching the next layer are determined. As shown in Figure 5 The process of determining the leftmost and rightmost nodes and the middle node in the lateral direction and the search tree reaching the next layer through the left limit and right limit turning angles is shown.

[0148] Step S740, determining the time attribute of the spatial search nodes of the next layer to obtain the space-time search nodes.

[0149] When the spatial search nodes of the next layer are determined, the space-time search nodes can be expanded to the next layer nodes through different acceleration actions.

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

[0151] For example, the total cost of the space-time search nodes can be calculated based on the first cost function and / or the second cost function of the preset path search algorithm in the foregoing embodiments 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 space-time 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 longitudinal position of the terminal point, the entire expansion ends. At this time, the optimal node in the priority queue is selected for backtracking to obtain the optimal space-time search trajectory as the target trajectory.

[0154] It should be noted that the specific implementation manner of each step in this embodiment can refer to the description in the foregoing embodiments of the present disclosure, which will not be described here again.

[0155] By adopting the technical solution, state space sampling is performed under the trajectory reference line, which can reduce the sampling space of the algorithm and improve the running efficiency compared with action space sampling in the related art, and can perform high-density discretization in the road transverse direction, thereby generating more turning angle trees and having stronger passing capacity for narrow channels. Further, the trajectory generation method avoids strong dependence on the frenet coordinate system, and sampling of the state space-based road guidance generates various turning angles, which can ensure flexible movement of the vehicle and has better obstacle avoidance capability.

[0156] Figures 8A to 8C is a trajectory diagram in three typical working conditions provided by the present embodiment. Among them, Figure 8A is a schematic diagram of generating a trajectory by performing space-time search based on the method of the present embodiment 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 is a schematic diagram of generating a trajectory by performing space-time search based on the method of the present embodiment in a static obstacle avoidance scene, which can generate a trajectory that avoids static obstacles, such as the static other vehicle in the figure. Figure 8C is a schematic diagram of generating a trajectory by performing space-time search based on the method of the present embodiment in a dynamic overtaking scene, which can generate a trajectory that avoids dynamic obstacles, such as the moving other vehicle.

[0157] Figure 9 is a flowchart of a trajectory display method provided by the present embodiment. The trajectory display method can be executed by Figure 1 the vehicle and / or the server as shown. As shown in Figure 9 the trajectory generation method of the present embodiment can include:

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

[0159] Step S920, displaying the target trajectory.

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

[0161] The target trajectory can be generated according to a target node, the target node being determined according to a plurality of spatial search nodes for generating a target trajectory of the vehicle; the plurality of spatial search nodes being determined on a line segment between two search boundary points according to a current position of the vehicle and a trajectory reference line, the target search reference line being determined perpendicular to the trajectory reference line; the two search boundary points being determined according to a road boundary and a maximum steering wheel turning angle of the vehicle, the trajectory reference line being a reference line for generating a trajectory according to the road boundary, the two search boundary points being greater than a preset safety distance from the road boundary, and the two search boundary points being position points that can be reached by the vehicle from the current position at a steering angle less than or equal to the maximum steering wheel turning angle.

[0162] In some examples, the specific manner of determining the target node can refer to the description in the foregoing embodiments of the present disclosure, which will not be described here.

[0163] By using this method, the maximum steering wheel turning angle of the vehicle and the road boundary in the environmental information are considered when determining the two search boundary points of the search reference line, the lateral search range can be adaptively determined according to the self-vehicle steering capability and the environmental information, in a narrow channel scene such as an urban area or an underground parking lot, the lateral search range can be reduced, the lateral search accuracy can be improved, more accurate trajectory planning can be achieved, and the robustness of vehicle trajectory generation can be improved.

[0164] Figure 10 FIG. 1 is a structural schematic diagram of an electronic device provided by an embodiment of the present disclosure. As shown in FIG. 1, the electronic device 1000 can include a memory 1010 and a processor 1020, the memory 1010 can be used to store computer instructions, and the processor 1020 can be used to call the computer instructions from the memory 1010 to execute all or part of the steps of any method in the foregoing embodiments of the present disclosure. Wherein the processor can be one or more, the one or more processors can execute instructions individually or collectively, and the memory can also be one or more, the one or more memories can store the above computer instructions individually or collectively. Figure 10 Optionally, the electronic device can be a server and / or a vehicle in Figure 1

[0165] An embodiment of the present disclosure further provides a vehicle, which can include a memory and a processor, the memory can be used to store computer instructions, and the processor can be used to call the computer instructions from the memory to execute all or part of the steps of any method in the foregoing embodiments of the present disclosure. Wherein the processor can be one or more, the one or more processors can execute instructions individually or collectively, and the memory can also be one or more, the one or more memories can store the above computer instructions individually or collectively.

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

[0167] The embodiments of the present disclosure further provide a computer readable storage medium having stored thereon a computer program, the computer program, when executed by a processor, implementing any of the methods in the foregoing embodiments of the present disclosure. Alternatively, the computer readable storage medium can be a non-transitory computer readable storage medium, but is not limited to this. It can also be a transitory computer readable storage medium.

[0168] The embodiments of the present disclosure further provide a chip, which can include a processing unit, the processing unit being configured to execute all or part of the steps of any of the methods in the foregoing embodiments of the present disclosure. The chip can be in the form of an application specific integrated chip (ASIC), a system on chip (SOC), a field programmable gate array (FPGA), or the like, and the embodiments of the present disclosure are not limited thereto. Alternatively, the chip can further include a storage unit, the storage unit being configured to store computer instructions, and the processing unit being configured to invoke the computer instructions from the storage unit to execute all or part of the steps of any of the methods in the foregoing embodiments of the present disclosure.

[0169] The embodiments of the present disclosure further provide a computer program product, which can include a computer program, the computer program, when executed by a processor, implementing any of the methods in the foregoing embodiments of the present disclosure.

[0170] The present disclosure can be a system, a method, and / or a computer program product. The computer program product can include a computer readable storage medium having computer readable program instructions stored thereon, the computer readable program instructions being configured to cause a processor to implement any of the methods in the foregoing embodiments of the present disclosure.

[0171] Computer readable storage media can be tangible storage media which can retain and store instructions for use by an instruction execution device. Computer readable storage media can be, for example, but is not limited to, an electronic 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 (a non-exhaustive list) of computer readable storage media include the following: a portable computer diskette, 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 disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.

[0172] Computer readable program instructions described herein can be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and / or a wireless network. The network can comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0173] Computer readable program instructions for carrying out operations of the present disclosure can be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions can execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection can be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate array (FPGA), or programmable logic array (PLA) can execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present disclosure.

[0174] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0175] These computer readable program instructions can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks. These computer readable program instructions can also be stored in a computer readable storage medium that can include random access memory (RAM), read only memory (ROM), electrically erasable programmable read only memory (EEPROM), flash memory or other data storage device. When the computer readable program instructions are loaded into the computer and other programmable data processing apparatus, a series of operational steps are implemented that provide processes such that the computer or other programmable apparatus provide processes for implementing the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0176] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer, other programmable data processing apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0177] The computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus or other device to produce a computer implemented process such that the instructions which execute on the computer, other programmable data processing apparatus, or other device implement the functions / acts specified in the flowchart and / or block diagram block or blocks.

[0178] Embodiments of the present disclosure have been described above, and the description is intended to be illustrative, and not restrictive, of the disclosed embodiments. Many modifications and variations of the disclosed embodiments are possible in light of the above teachings. It is therefore to be understood that within the scope of the disclosed embodiments, modifications and variations of the disclosed embodiments can be practiced. It is also to be understood that the specific order or hierarchy of steps in the processes disclosed is an illustration of exemplary processes. Based upon the description and illustrations provided herein, those skilled in the art will understand that changes can be made to the order of steps in the processes and that many of the individual steps can be modified or eliminated. Additionally, the description and illustrations provided herein are not meant to limit the scope of the disclosed embodiments. The scope of the disclosed embodiments is limited only by the claims.

Claims

1. A trajectory generation method characterized by, The method comprises: determining a target search reference line perpendicular to a trajectory reference line according to a current position and the trajectory reference line of a vehicle; wherein the trajectory reference line is a reference line for generating a trajectory determined according to a road boundary; determining two search boundary points on the target search reference line according to the road boundary and a maximum steering wheel turning angle of the vehicle; wherein 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 the vehicle travels from the current position at a turning angle less than or equal to the maximum steering wheel turning angle; determining a plurality of spatial search nodes on a line segment of the target search reference line between the two search boundary points; determining a target node for generating a target trajectory of the vehicle according to a plurality of the spatial search nodes; generating a target trajectory of the vehicle according to the target node.

2. The method of claim 1, wherein, The determining of the two search boundary points on the target search reference line according to the road boundary and the maximum steering wheel turning angle of the vehicle comprises: determining a first line segment on the target search reference line according to the road boundary; wherein the distance between the two end points of the first line segment and the road boundary closest to the end points is the preset safety distance; determining a second line segment on the target search reference line according to the maximum steering wheel turning angle, the current direction angle and the current position of the vehicle; determining the two search boundary points on the target search reference line according to the intersection of the first line segment and the second line segment.

3. The method of claim 2, wherein, The determining of the second line segment on the target search reference line according to the maximum steering wheel turning angle, the current direction angle and the current position of the vehicle comprises: determining a 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 turning angle of the vehicle in a first direction; determining a second 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 turning angle of the vehicle in a second direction; wherein the first direction and the second direction are opposite directions; obtaining the second line segment with the first intersection point and the second intersection point as end points.

4. The method of claim 1, wherein, The determining of the plurality of spatial search nodes on the line segment of the target search reference line between the two search boundary points comprises: determining a lateral interval between two adjacent spatial search nodes according to the distance between the two search boundary points; determining the plurality of spatial search nodes according to the lateral interval among the position points on the target search reference line within the two search boundary points.

5. The method of claim 1, wherein, The determining of the target search reference line perpendicular to the trajectory reference line according to the current position and the trajectory reference line of the vehicle 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 interval determined according to the speed and / or acceleration of the vehicle; generating a search reference line perpendicular to the trajectory reference line based on each search reference point; The search reference line closest to the current position of the vehicle among the plurality of search reference lines is taken as the target search reference line.

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

7. The method according to any one of claims 1 to 6, characterized in that, The target node for generating the target trajectory of the vehicle is determined according to the plurality of spatial search nodes, including: For each spatial search node, a plurality of time attribute information is generated for the spatial search node according to the speed and / or acceleration of the vehicle, to obtain a plurality of spatio-temporal search nodes corresponding to the spatial search node; Based on a preset search target, a target node that meets the search target is searched in the spatio-temporal search node.

8. The method of claim 7, wherein, The spatio-temporal 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 by comprising: The method includes: generating a target trajectory of the vehicle according to a current position of the vehicle and a trajectory reference line; displaying the target trajectory; The target trajectory is generated according to a target node, and the target node is determined according to a plurality of spatial search nodes for generating the target trajectory of the vehicle. The plurality of spatial search nodes are determined on a line segment between two search boundary points on a target search reference line perpendicular to the trajectory reference line, which is determined according to a current position of the vehicle and the trajectory reference line. The two search boundary points are determined according to a road boundary and a maximum steering angle of the vehicle. The trajectory reference line is a reference line for generating a trajectory, which is determined according to a 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 can be reached by the vehicle when driving from the current position with a steering angle less than or equal to the maximum steering angle.

10. An electronic device, comprising: A memory and a processor, 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 of any one of claims 1-9.

11. A vehicle characterized by comprising: A memory and a processor, 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 of any one of claims 1-9.

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

13. A chip, characterized by The chip includes: the chip includes a processing unit, and the processing unit is used to execute the method of any one of claims 1-9. The chip includes: the chip includes a processing unit, and the processing unit is used to execute the method of any one of claims 1-9.

Images