Driving trajectory planning method, device, equipment and storage medium

Abstract

The present application provides a driving trajectory planning method, apparatus, device, and storage medium, relating to the field of intelligent driving technology. The driving trajectory planning method includes: determining the expected interaction decision between a target vehicle and an obstacle within a preset future time range; constructing a longitudinal feasible space graph of the target vehicle corresponding to the preset future time range based on the expected interaction decision; obtaining a reference speed of the target vehicle based on the longitudinal feasible space graph and a first preset cost function; and determining a target trajectory of the target vehicle within the preset future time range based on the reference speed. The present application can more accurately obtain an optimal path, improve the rationality and reliability of driving trajectory planning results, enhance driving efficiency, and improve the driving experience.

Description

Technical Field

[0001] The present application relates to the field of intelligent driving technology, and in particular to a driving trajectory planning method, device, equipment and storage medium. Background Art

[0002] With the development of autonomous driving technology, autonomous vehicles are gradually being developed and applied. When an autonomous vehicle is driving, a planned driving trajectory is provided to the autonomous vehicle so that the autonomous vehicle can automatically drive according to the planned driving trajectory.

[0003] Currently, sampling-based path optimization methods are commonly used to plan the driving trajectory of autonomous vehicles. Specifically, this method samples the convergence position of the autonomous vehicle to obtain multiple alternative paths. The optimal path is determined from these multiple alternative paths using the vehicle's current speed as a reference. However, the trajectory obtained by this method may not be optimal. Summary of the Invention

[0004] The present application provides a driving trajectory planning method, apparatus, device and storage medium to solve the problem that the driving trajectory obtained by using a sampling-based path optimization method may not be optimal.

[0005] In a first aspect, the present application provides a driving trajectory planning method, comprising:

[0006] Determine the target vehicle's expected interaction decision with the obstacle within a preset future timeframe. The expected interaction decision is used to characterize the target vehicle's interaction behavior with the obstacle and the corresponding interaction time window. Interaction behaviors include the target vehicle proactively overtaking the obstacle, the target vehicle proactively yielding to the obstacle, and the target vehicle proactively ignoring the obstacle.

[0007] Based on the expected interactive decision, a longitudinal feasible space diagram of the target vehicle corresponding to a preset future time range is constructed. The longitudinal feasible space diagram is used to represent the longitudinal feasible range and longitudinal speed constraint information of the target vehicle corresponding to discrete moments within the preset future time range.

[0008] Obtaining a reference speed of the target vehicle based on the longitudinal feasible space graph and a first preset cost function, wherein the first preset cost function is determined based on the driving efficiency and comfort of the target vehicle;

[0009] Based on the reference speed, the target trajectory of the target vehicle within a preset time range in the future is determined.

[0010] Optionally, based on the expected interactive decision, a longitudinal feasible space diagram of the target vehicle corresponding to a preset future time range is constructed, including: based on the expected interactive decision, determining the target longitudinal feasible minimum distance, target longitudinal feasible maximum distance, minimum target longitudinal speed, and maximum target longitudinal speed corresponding to discrete moments within the preset future time range; and constructing a longitudinal feasible space diagram of the target vehicle corresponding to the preset future time range based on the target longitudinal feasible minimum distance, target longitudinal feasible maximum distance, minimum target longitudinal speed, and maximum target longitudinal speed.

[0011] Optionally, based on the expected interactive decision, the target longitudinal feasible minimum distance, target longitudinal feasible maximum distance, target longitudinal speed minimum value and target longitudinal speed maximum value corresponding to discrete moments in the future preset time range are determined, including: determining the target vehicle's initial longitudinal feasible maximum distance, initial longitudinal feasible minimum distance, initial longitudinal speed minimum value and initial longitudinal speed maximum value; for each obstacle corresponding to the discrete moments in the future preset time range, perform the following operations until each obstacle has been traversed: Based on the expected interactive decision, if it is determined that the target vehicle needs to actively give way to the target obstacle, the target vehicle's target distance is determined based on the longitudinal and lateral space occupied by the target obstacle, the preset safety distance and the initial longitudinal feasible maximum distance. The target longitudinal maximum feasible distance is determined, and the maximum value of the target longitudinal speed is determined according to the speed of the target obstacle, the target longitudinal maximum feasible distance is determined as the new initial longitudinal maximum feasible distance, and the maximum value of the target longitudinal speed is determined as the new maximum value of the initial longitudinal speed; or, based on the expected interactive decision, if it is determined that the target vehicle needs to actively overtake the target obstacle, the target longitudinal minimum feasible distance of the target vehicle is determined according to the longitudinal and lateral space occupied by the target obstacle, the preset overtaking safety distance, and the initial longitudinal minimum feasible distance, and the minimum value of the target longitudinal speed is determined according to the speed of the target obstacle, the target longitudinal minimum feasible distance is determined as the new initial longitudinal minimum feasible distance, and the minimum value of the target longitudinal speed is determined as the new minimum value of the initial longitudinal speed.

[0012] Optionally, determining the initial longitudinal maximum feasible distance of the target vehicle includes: determining the maximum feasible distance of the target vehicle within a future preset time range based on the speed limit of the target vehicle's driving scenario; determining the maximum speed corresponding to the maximum curvature according to the maximum curvature of the path traveled by the target vehicle within the future preset time range and the correspondence between the preset curvature and the speed constraint; obtaining the target maximum feasible distance based on the maximum speed and the maximum feasible distance; and determining the target maximum feasible distance as the initial longitudinal maximum feasible distance.

[0013] Optionally, after constructing the longitudinal feasible space map of the target vehicle corresponding to the future preset time range, the driving trajectory planning method also includes: for the target longitudinal feasible minimum distance and the target longitudinal feasible maximum distance corresponding to discrete moments within the future preset time range, according to the target longitudinal feasible minimum distance corresponding to the previous discrete moment and the target longitudinal feasible minimum distance corresponding to the current discrete moment, obtaining the updated target longitudinal feasible minimum distance corresponding to the current discrete moment, and according to the target maximum feasible distance and the target longitudinal feasible maximum distance corresponding to the current discrete moment, obtaining the updated target longitudinal feasible maximum distance corresponding to the current discrete moment; according to the updated target longitudinal feasible minimum distance and the updated target longitudinal feasible maximum distance, obtaining the updated longitudinal feasible space map.

[0014] Optionally, based on the target longitudinal feasible minimum distance corresponding to the previous discrete moment and the target longitudinal feasible minimum distance corresponding to the current discrete moment, an updated target longitudinal feasible minimum distance corresponding to the current discrete moment is obtained, and based on the target maximum feasible distance and the target longitudinal feasible maximum distance corresponding to the current discrete moment, an updated target longitudinal feasible maximum distance corresponding to the current discrete moment is obtained, including: for discrete moments within a future preset time range, if it is determined that the updated target longitudinal feasible maximum distance is less than the updated target longitudinal feasible minimum distance, then updating the corresponding interaction behavior between the obstacle and the target vehicle, and reconstructing the longitudinal feasible space map of the target vehicle corresponding to the future preset time range.

[0015] Optionally, based on the longitudinal feasible space diagram and a first preset cost function, a reference speed of the target vehicle is obtained, including: based on the longitudinal feasible space diagram, according to the maximum acceleration capability, maximum deceleration capability, the curvature corresponding to the reference line of the path within the longitudinal feasible range, and the correspondence between the preset curvature and the speed constraint of the target vehicle, obtaining an updated longitudinal feasible range and longitudinal speed constraint information; based on the updated longitudinal feasible range and longitudinal speed constraint information, obtaining an updated longitudinal feasible space diagram; based on the updated longitudinal feasible space diagram and the first preset cost function, obtaining a first target position of the target vehicle corresponding to a discrete moment; and performing fitting processing on the first target position to obtain a reference speed of the target vehicle.

[0016] Optionally, based on the reference speed, the target trajectory of the target vehicle within a preset time range in the future is determined, including: obtaining a target candidate path corresponding to the preset time range in the future according to at least one preset candidate path corresponding to the target vehicle and the reference speed, the preset candidate path being obtained by longitudinally and transversely sampling the future driving path of the target vehicle respectively; determining the target trajectory of the target vehicle within the preset time range in the future according to the target candidate path and a second preset cost function, the second preset cost function being determined based on the driving safety, comfort and stability of the target vehicle.

[0017] Optionally, based on at least one preset candidate path and a reference speed corresponding to the target vehicle, a target candidate path corresponding to a future preset time range is obtained, including: for each preset candidate path, obtaining a longitudinal forward distance corresponding to the target vehicle at a discrete moment based on the reference speed; obtaining a second target position of the target vehicle at a discrete moment based on the longitudinal forward distance and the preset candidate path; and performing coordinate transformation on the second target position to obtain the target candidate path.

[0018] In a second aspect, the present application provides a driving trajectory planning device, comprising:

[0019] A determination module is used to determine the target vehicle's expected interaction decision with the obstacle within a preset future time range. The expected interaction decision is used to characterize the target vehicle's interaction behavior with the obstacle and the interaction time window corresponding to the interaction behavior. Interaction behaviors include the target vehicle actively overtaking the obstacle, the target vehicle actively yielding to the obstacle, and the target vehicle actively ignoring the obstacle.

[0020] A construction module is used to construct a longitudinal feasible space diagram of the target vehicle corresponding to a preset future time range based on the expected interactive decision. The longitudinal feasible space diagram is used to represent the longitudinal feasible range and longitudinal speed constraint information of the target vehicle corresponding to discrete moments in the preset future time range;

[0021] an acquisition module, configured to obtain a reference speed of the target vehicle based on the longitudinal feasible space map and a first preset cost function, wherein the first preset cost function is determined based on the driving efficiency and comfort of the target vehicle;

[0022] The processing module is used to determine a target trajectory of the target vehicle within a preset time range in the future based on the reference speed.

[0023] Optionally, the construction module is specifically used to: determine the target longitudinal feasible minimum distance, target longitudinal feasible maximum distance, minimum target longitudinal speed and maximum target longitudinal speed corresponding to discrete moments within a preset time range in the future based on the expected interactive decision; and construct a longitudinal feasible space diagram of the target vehicle corresponding to the preset time range in the future based on the target longitudinal feasible minimum distance, target longitudinal feasible maximum distance, minimum target longitudinal speed and maximum target longitudinal speed.

[0024] Optionally, when the construction module is used to determine the target longitudinal feasible minimum distance, target longitudinal feasible maximum distance, minimum target longitudinal speed and maximum target longitudinal speed corresponding to discrete moments within a preset time range in the future based on expected interactive decision-making, it is specifically used to: determine the initial longitudinal feasible maximum distance, initial longitudinal feasible minimum distance, minimum initial longitudinal speed and maximum initial longitudinal speed of the target vehicle; for each obstacle corresponding to the discrete moments within the preset time range in the future, perform the following operations until each obstacle has been traversed: based on the expected interactive decision, if it is determined that the target vehicle needs to actively give way to the target obstacle, determine the target vehicle according to the longitudinal and lateral space occupied by the target obstacle, the preset safety distance and the initial longitudinal feasible maximum distance. The target longitudinal feasible maximum distance of the target vehicle is determined, and the maximum value of the target longitudinal speed is determined based on the speed of the target obstacle, the target longitudinal feasible maximum distance is determined as the new initial longitudinal feasible maximum distance, and the maximum value of the target longitudinal speed is determined as the new maximum value of the initial longitudinal speed; or, based on the expected interactive decision, if it is determined that the target vehicle needs to actively overtake the target obstacle, the target longitudinal feasible minimum distance of the target vehicle is determined based on the longitudinal and lateral spaces occupied by the target obstacle, the preset overtaking safety distance, and the initial longitudinal feasible minimum distance, and the minimum value of the target longitudinal speed is determined based on the speed of the target obstacle, the target longitudinal feasible minimum distance is determined as the new initial longitudinal feasible minimum distance, and the minimum value of the target longitudinal speed is determined as the new minimum value of the initial longitudinal speed.

[0025] Optionally, when the construction module is used to determine the initial longitudinal feasible maximum distance of the target vehicle, it is specifically used to: determine the maximum feasible distance of the target vehicle within a future preset time range based on the driving scenario speed limit of the target vehicle; determine the maximum speed corresponding to the maximum curvature according to the maximum curvature of the path traveled by the target vehicle within the future preset time range and the correspondence between the preset curvature and the speed constraint; obtain the target maximum feasible distance based on the maximum speed and the maximum feasible distance; and determine the target maximum feasible distance as the initial longitudinal feasible maximum distance.

[0026] Optionally, the driving trajectory planning device also includes an updating module, which is used to, after the construction module constructs the longitudinal feasible space map of the target vehicle corresponding to the future preset time range, obtain the updated target longitudinal feasible minimum distance corresponding to the current discrete moment according to the target longitudinal feasible minimum distance corresponding to the previous discrete moment and the target longitudinal feasible minimum distance corresponding to the current discrete moment, and obtain the updated target longitudinal feasible maximum distance corresponding to the current discrete moment according to the target maximum feasible distance and the target longitudinal feasible maximum distance corresponding to the current discrete moment; and obtain the updated longitudinal feasible space map according to the updated target longitudinal feasible minimum distance and the updated target longitudinal feasible maximum distance.

[0027] Optionally, when the update module is used to obtain the updated target longitudinal feasible minimum distance corresponding to the current discrete moment based on the target longitudinal feasible minimum distance corresponding to the previous discrete moment and the target longitudinal feasible minimum distance corresponding to the current discrete moment, and to obtain the updated target longitudinal feasible maximum distance corresponding to the current discrete moment based on the target maximum feasible distance and the target longitudinal feasible maximum distance corresponding to the current discrete moment, it is specifically used to: for discrete moments within a future preset time range, if it is determined that the updated target longitudinal feasible maximum distance is less than the updated target longitudinal feasible minimum distance, update the corresponding interaction behavior between the obstacle and the target vehicle, and reconstruct the longitudinal feasible space map of the target vehicle corresponding to the future preset time range.

[0028] Optionally, the acquisition module is specifically used to: based on the longitudinal feasible space diagram, obtain the updated longitudinal feasible range and longitudinal speed constraint information according to the maximum acceleration capability, maximum deceleration capability, the curvature corresponding to the reference line of the path within the longitudinal feasible range, and the correspondence between the preset curvature and the speed constraint; obtain the updated longitudinal feasible space diagram based on the updated longitudinal feasible range and longitudinal speed constraint information; based on the updated longitudinal feasible space diagram and the first preset cost function, obtain the first target position of the target vehicle corresponding to the discrete moment; perform fitting processing on the first target position to obtain the reference speed of the target vehicle.

[0029] Optionally, the processing module is specifically used to: obtain a target candidate path corresponding to a future preset time range based on at least one preset candidate path and a reference speed corresponding to the target vehicle, the preset candidate path being obtained by longitudinally and transversely sampling the future driving path of the target vehicle respectively; determine a target trajectory of the target vehicle within the future preset time range based on the target candidate path and a second preset cost function, the second preset cost function being determined based on the driving safety, comfort and stability of the target vehicle.

[0030] Optionally, when the processing module is used to obtain a target candidate path corresponding to a future preset time range based on at least one preset candidate path and a reference speed corresponding to the target vehicle, it is specifically used to: for each preset candidate path, obtain the longitudinal forward distance corresponding to the target vehicle at a discrete moment based on the reference speed; obtain the second target position of the target vehicle corresponding to the discrete moment based on the longitudinal forward distance and the preset candidate path; and perform coordinate transformation on the second target position to obtain the target candidate path.

[0031] In a third aspect, the present application provides an electronic device, comprising: a processor, and a memory communicatively connected to the processor;

[0032] Memory stores computer-executable instructions;

[0033] The processor executes the computer-executable instructions stored in the memory to implement the driving trajectory planning method as described in the first aspect of the present application.

[0034] In a fourth aspect, the present application provides a computer-readable storage medium, in which computer program instructions are stored. When the computer program instructions are executed by a processor, the driving trajectory planning method described in the first aspect of the present application is implemented.

[0035] In a fifth aspect, the present application provides a computer program product, including a computer program, which, when executed by a processor, implements the driving trajectory planning method as described in the first aspect of the present application.

[0036] The driving trajectory planning method, device, equipment and storage medium provided by the present application determine the expected interactive decision of the target vehicle with obstacles within a preset time range in the future, construct a longitudinal feasible space graph of the target vehicle corresponding to the preset time range in the future based on the expected interactive decision, obtain a reference speed of the target vehicle based on the longitudinal feasible space graph and a first preset cost function, and determine the target trajectory of the target vehicle within the preset time range in the future based on the reference speed. Because the present application fully considers the information of static obstacles and dynamic obstacles in the driving scene, determines the expected interactive decision, and then constructs the longitudinal feasible space graph of the target vehicle, obtains the reference speed closest to the final execution of the target vehicle based on the longitudinal feasible space graph, and uses the reference speed to evaluate the optimal path, it can more accurately obtain the optimal path, improve the rationality and reliability of the driving trajectory planning results, improve driving efficiency, and improve the driving experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0038] Figure 1 A schematic diagram of an application scenario provided in an embodiment of the present application;

[0039] Figure 2 A flowchart of a driving trajectory planning method provided in one embodiment of the present application;

[0040] Figure 3 A flowchart of a driving trajectory planning method provided in another embodiment of the present application;

[0041] Figure 4 A schematic diagram of the structure of a driving trajectory planning device provided in one embodiment of the present application;

[0042] Figure 5 This is a schematic diagram of the structure of an electronic device provided in this application. DETAILED DESCRIPTION

[0043] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application will be clearly and completely described below in conjunction with the drawings in the embodiments of this application. Obviously, the described embodiments are part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0044] In the technical solution of this application, the collection, storage, use, processing, transmission, provision and disclosure of information such as financial data or user data involved comply with the provisions of relevant laws and regulations and do not violate public order and good morals.

[0045] Currently, autonomous driving trajectory planning approaches often use a decoupled path and speed planning approach. A safe and flexible path planning strategy must fully consider the impact of both static and dynamic obstacles in the driving scenario. Specifically, it must consider whether the ego vehicle (i.e., the current autonomous vehicle) and an obstacle will appear in the same location at some point in the future. Therefore, when evaluating alternative paths, it is necessary to provide corresponding speed and time information for discrete path points along the path to analyze potential collision risks with obstacles. For sampling-based path optimization methods, an unreasonable reference speed can lead to evaluation bias, making it impossible to select the optimal path, impacting driving safety and efficiency.

[0046] Based on the above problems, the present application provides a driving trajectory planning method, device, equipment and storage medium. By fully considering the information of static obstacles and dynamic obstacles in the driving scene, based on the behavior and trajectory prediction results of the obstacles, combined with the execution capability of the target vehicle, and under the premise of reasonable interaction with the obstacles, the reference speed is optimized to obtain the reference speed closest to the final execution of the target vehicle. The reference speed is used for optimal path evaluation, which can more accurately obtain the optimal path and improve the rationality and reliability of the driving trajectory planning results.

[0047] Below, the application scenarios of the solution provided in this application are first illustrated.

[0048] Figure 1 This is a schematic diagram of an application scenario provided by an embodiment of the present application. Figure 1 As shown, in this application scenario, the autonomous driving vehicle 101 drives on the road 102 according to the planned driving trajectory. The specific implementation process of how the autonomous driving vehicle 101 obtains the planned driving trajectory can be referred to the solutions of the following embodiments.

[0049] It should be noted that Figure 1 This is only a schematic diagram of an application scenario provided by the embodiment of the present application. Figure 1 The equipment included in the Figure 1 The positional relationship between the devices is limited.

[0050] Next, the driving trajectory planning method is introduced through a specific embodiment.

[0051] Figure 2 This is a flow chart of a driving trajectory planning method provided by an embodiment of the present application. The method of the embodiment of the present application can be applied to an electronic device, which can be a server or a server cluster. Figure 2 As shown, the method of the embodiment of the present application includes:

[0052] S201: Determine the expected interaction decision between the target vehicle and the obstacle within a preset time range in the future.

[0053] Among them, the expected interaction decision is used to characterize the interaction behavior between the target vehicle and the obstacle and the interaction time window corresponding to the interaction behavior. The interaction behaviors include the target vehicle actively overtaking the obstacle, the target vehicle actively giving way to the obstacle, and the target vehicle actively ignoring the obstacle.

[0054] In the embodiment of the present application, for example, the future preset time range is, for example, the next 10 seconds. Based on the obstacle behavior and trajectory prediction results given by the preset prediction module, combined with consideration of public road driving rules, road rights and speed limits and other information, a preliminary interaction decision can be made for each obstacle that has potential interaction with the target vehicle, that is, the expected interaction decision of the target vehicle with the obstacle within the future preset time range is determined. Specifically, the interaction behavior between the target vehicle and the obstacle can also be referred to as an interaction attribute, which is defined as three types: the target vehicle needs to actively pass the obstacle (expressed as pass_type), the target vehicle needs to actively give way to the obstacle (expressed as yield_type), and the obstacle is actively ignored because it does not pose a potential risk to the target vehicle (expressed as ignore_type). The above three interaction attributes can be understood as decisions at the behavioral level.

[0055] For obstacles that interact with the target vehicle, in addition to the behavioral decision, the corresponding interaction time window (i.e., the overtaking or yielding time window) must be specified. The range of this interaction time window is [t_min, t_max], where t_min represents the minimum start time of the interaction time window and t_max represents the maximum end time of the interaction time window. For example, if the target vehicle expects to overtake an obstacle with the identity document (id) obs_0 between 3 and 5 seconds, the interaction attribute of this obstacle is marked as pass_type, and the corresponding interaction time window is [3, 5]. For example, if the target vehicle expects to yield to an obstacle with the id obs_1 between 8 and 10 seconds, the interaction attribute of this obstacle is marked as yield_type, and the corresponding interaction time window is [8, 10]. If an obstacle with the id obs_2 does not enter the target vehicle's lane within 10 seconds, or there is no potential for interaction, the interaction attribute of this obstacle is marked as ignore_type, and the corresponding interaction time window is [0, 10]. By performing interaction analysis on all obstacles in the target vehicle's driving scene in turn, the expected interaction decision between the target vehicle and each obstacle can be obtained.

[0056] S202: Based on the expected interactive decision, construct a longitudinal feasible space diagram of the target vehicle corresponding to a preset future time range.

[0057] The longitudinal feasible space diagram is used to represent the longitudinal feasible range and longitudinal speed constraint information of the target vehicle corresponding to discrete moments within a preset time range in the future.

[0058] In this step, after obtaining the expected interactive decision, a longitudinal feasible space diagram for the target vehicle corresponding to a preset future time range can be constructed based on the expected interactive decision to determine the longitudinal feasible range and longitudinal speed constraint information corresponding to discrete moments in the future time range. For example, the horizontal axis of the longitudinal feasible space diagram may represent discrete moments in the future time range, and the vertical axis of the longitudinal feasible space diagram may represent the longitudinal feasible range corresponding to each discrete moment, with corresponding longitudinal speed constraint information within the longitudinal feasible range. For details on how to construct the longitudinal feasible space diagram for the target vehicle corresponding to the preset future time range based on the expected interactive decision, please refer to the subsequent embodiments and will not be further elaborated here.

[0059] S203 : Obtain a reference speed of the target vehicle based on the longitudinal feasible space graph and a first preset cost function.

[0060] The first preset cost function is determined based on the driving efficiency and comfort of the target vehicle.

[0061] For example, a driving speed that is too low will lead to a problem of low driving efficiency, while a speed that is too fast will easily affect the driving experience, and in severe cases may even cause vehicle instability. Therefore, when defining the first preset cost function (expressed as cost_function), it is necessary to weigh the driving efficiency and comfort of the target vehicle, that is: for the sake of driving efficiency, it is hoped that the driving distance tends to be as far as possible; for the sake of comfort, it is hoped that the driving process is smoother, and the acceleration and jerk (i.e., the increment of acceleration) tend to be as small as possible. The specific definition of the first preset cost function can be found in the subsequent embodiments, which will not be repeated here. In this step, after obtaining the longitudinal feasible space diagram, the reference speed of the target vehicle can be obtained based on the longitudinal feasible space diagram and the first preset cost function. For how to obtain the reference speed of the target vehicle based on the longitudinal feasible space diagram and the first preset cost function, please refer to the subsequent embodiments, which will not be repeated here.

[0062] S204: Determine a target trajectory of the target vehicle within a future preset time range based on the reference speed.

[0063] In this step, after obtaining the reference speed, the target trajectory of the target vehicle within a preset future time range can be determined based on the reference speed. For how to determine the target trajectory of the target vehicle within a preset future time range based on the reference speed, please refer to the subsequent embodiments and will not be repeated here.

[0064] After determining the target trajectory of the target vehicle within the future preset time range, that is, determining the optimal trajectory of the target vehicle within the future preset time range, the target vehicle can be controlled to travel according to the planned optimal trajectory.

[0065] The driving trajectory planning method provided in an embodiment of the present application determines the expected interaction decision of the target vehicle with an obstacle within a preset future time range, constructs a longitudinal feasible space graph of the target vehicle corresponding to the preset future time range based on the expected interaction decision, obtains a reference speed of the target vehicle based on the longitudinal feasible space graph and a first preset cost function, and determines the target trajectory of the target vehicle within the preset future time range based on the reference speed. Because the embodiment of the present application fully considers the information of static and dynamic obstacles in the driving scene, determines the expected interaction decision, and then constructs the longitudinal feasible space graph of the target vehicle, obtains the reference speed closest to the target vehicle's final execution based on the longitudinal feasible space graph, and uses this reference speed to evaluate the optimal path, it is possible to more accurately obtain the optimal path, improve the rationality and reliability of the driving trajectory planning results, improve driving efficiency, and enhance the driving experience.

[0066] Figure 3 This is a flow chart of a driving trajectory planning method provided by another embodiment of the present application. Based on the above embodiment, this embodiment of the present application further explains how to plan the driving trajectory. Figure 3 As shown, the method of the embodiment of the present application may include:

[0067] S301: Determine the expected interaction decision between the target vehicle and the obstacle within a preset time range in the future.

[0068] Among them, the expected interaction decision is used to characterize the interaction behavior between the target vehicle and the obstacle and the interaction time window corresponding to the interaction behavior. The interaction behaviors include the target vehicle actively overtaking the obstacle, the target vehicle actively giving way to the obstacle, and the target vehicle actively ignoring the obstacle.

[0069] The detailed description of this step can be found in Figure 2 The relevant description of S201 in the illustrated embodiment will not be repeated here.

[0070] In the embodiment of this application, Figure 2 Step S202 may further include the following two steps S302 and S303:

[0071] S302: Determine, based on the expected interactive decision, a target feasible minimum longitudinal distance, a target feasible maximum longitudinal distance, a minimum target longitudinal speed, and a maximum target longitudinal speed corresponding to discrete moments within a preset future time range.

[0072] In this step, the expected interaction decision provides a reference for constructing the target vehicle's longitudinal feasible space at each discrete moment within a preset future timeframe. After obtaining the target vehicle's expected interaction decision with obstacles within the preset future timeframe, the target minimum longitudinal feasible distance, target maximum longitudinal feasible distance, target minimum longitudinal velocity, and target maximum longitudinal velocity corresponding to the target vehicle at each discrete moment within the preset future timeframe can be determined based on the expected interaction decision.

[0073] Furthermore, optionally, based on the expected interactive decision, determining the target longitudinal feasible minimum distance, target longitudinal feasible maximum distance, target longitudinal speed minimum value and target longitudinal speed maximum value corresponding to discrete moments within a preset time range in the future may include: determining the target vehicle's initial longitudinal feasible maximum distance, initial longitudinal feasible minimum distance, initial longitudinal speed minimum value and initial longitudinal speed maximum value; for each obstacle corresponding to the discrete moments within the preset time range in the future, performing the following operations until each obstacle has been traversed: based on the expected interactive decision, if it is determined that the target vehicle needs to actively give way to the target obstacle, determining the target vehicle's longitudinal and lateral space occupied by the target obstacle, the preset safety distance and the initial longitudinal feasible maximum distance. The target longitudinal feasible maximum distance of the target vehicle is determined based on the longitudinal and lateral spaces occupied by the target obstacle, the preset overtaking safety distance, and the initial longitudinal feasible minimum distance. The target longitudinal feasible maximum distance is determined as the new initial longitudinal feasible maximum distance, and the maximum value of the target longitudinal speed is determined as the new maximum value of the initial longitudinal speed. Alternatively, based on the expected interactive decision, if it is determined that the target vehicle needs to actively overtake the target obstacle, the target longitudinal feasible minimum distance of the target vehicle is determined based on the longitudinal and lateral spaces occupied by the target obstacle, the preset overtaking safety distance, and the initial longitudinal feasible minimum distance. The target longitudinal feasible minimum distance is determined as the new initial longitudinal feasible minimum distance, and the minimum value of the target longitudinal speed is determined as the new minimum value of the initial longitudinal speed.

[0074] Among them, optionally, determining the initial longitudinal feasible maximum distance of the target vehicle may include: determining the maximum feasible distance of the target vehicle within a future preset time range based on the speed limit of the target vehicle's driving scenario; determining the maximum speed corresponding to the maximum curvature according to the maximum curvature of the path traveled by the target vehicle within the future preset time range and the correspondence between the preset curvature and the speed constraint; obtaining the target maximum feasible distance based on the maximum speed and the maximum feasible distance; and determining the target maximum feasible distance as the initial longitudinal feasible maximum distance.

[0075] For example, the preset future time range is, for example, the next 10 seconds, and the correspondence between the preset curvature and speed constraints is, for example, a pre-calibrated offline curvature-speed constraint table. For example, the maximum drivable distance (expressed as max_drivable_length) of the target vehicle in an interference-free state is obtained, i.e., the maximum drivable distance of the target vehicle in the next 10 seconds under physical constraints. This calculation process fully considers the speed limit of the target vehicle's driving scenario and the speed restrictions imposed by the lane shape. First, consider the target vehicle's driving scenario speed limit and initialize max_drivable_length according to the target vehicle's driving scenario speed limit to obtain the initial value of max_drivable_length. Second, consider driving stability. The maximum speed of the target vehicle is affected by the lane shape. For example, if the target vehicle enters a right-angle turn, it needs to decelerate accordingly based on the curvature of the path. Based on the offline calibrated curvature-speed constraint table and the maximum curvature of the future path, the curvature-speed constraint table can be queried to obtain the corresponding maximum speed. Based on the maximum speed and the initial value of max_drivable_length, max_drivable_length is updated to obtain the target maximum feasible distance.

[0076] The target maximum feasible distance is determined to be the initial longitudinal feasible maximum distance, the initial longitudinal feasible minimum distance is, for example, 0, the minimum initial longitudinal speed is, for example, 0, and the maximum initial longitudinal speed is, for example, the target vehicle's driving scenario speed limit. After obtaining the target vehicle's initial longitudinal feasible maximum distance, initial longitudinal feasible minimum distance, initial longitudinal speed minimum, and initial longitudinal speed maximum, the future preset time range is discretized at equal time intervals to obtain the corresponding discrete moments. It is necessary to comprehensively consider the target vehicle's reasonable interaction with each obstacle at each discrete moment to obtain the boundary values of the target longitudinal feasible distance (i.e., the longitudinal feasible range) of the target vehicle. The boundary values of the target longitudinal feasible distance of the target vehicle include the target longitudinal feasible minimum distance (denoted as s_ego_min) and the target longitudinal feasible maximum distance (denoted as s_ego_max). The boundary values of the target longitudinal feasible distance corresponding to each discrete moment must satisfy the basic constraints, namely, s_ego_min>=0 and s_ego_max<=max_drivable_length.

[0077] Specifically, for each obstacle corresponding to each discrete moment in the future preset time range, taking discrete moment t0 as an example, for each obstacle in the target vehicle driving scene (denoted as obs_i), first, query the obstacle trajectory prediction result corresponding to t0 for the longitudinal and lateral space occupied by the obstacle (denoted as sdboundary). sdboundary is a rectangular box used to represent the lane position space occupied by the obstacle at a certain discrete moment. The range of the longitudinal space occupied by the target obstacle is: [obs_i_s_min, obs_i_s_max] , where obs_i_s_min represents the minimum longitudinal space occupied by the target obstacle, and obs_i_s_max represents the maximum longitudinal space occupied by the target obstacle. The range of the transverse space occupied by the target obstacle is: [obs_i_d_min, obs_i_d_max], where obs_i_d_min represents the minimum transverse space occupied by the target obstacle, and obs_i_d_max represents the maximum transverse space occupied by the target obstacle. The target vehicle's no-travel zone is determined based on the sdboundary information. Next, consider the expected interaction decision between the target vehicle and the obstacle:

[0078] If an obstacle needs to be yielded, to ensure that the target vehicle has sufficient reaction time and space in an emergency, the target vehicle's maximum longitudinal feasible distance must be considered based on the obstacle's obs_i_s_min, allowing for a safe yield distance (expressed as lon_buffer). The target vehicle's maximum longitudinal feasible distance is related to the speed of the obstacle, so the target maximum longitudinal feasible distance of the target vehicle corresponding to t0 (expressed as s_ego_max_t0) is updated to the smaller value of s_ego_max_t0 and (obs_i_s_min - lon_buffer). If the obstacle is close to the target vehicle, the maximum target longitudinal speed of the target vehicle (expressed as v_ego_max_t0) needs to be updated to the speed of the obstacle.

[0079] If it is necessary to overtake an obstacle, the longitudinal safety distance reserved for overtaking must be considered based on the obs_i_s_max occupied by the obstacle. This longitudinal safety distance is related to the speed of the obstacle, so the target minimum feasible longitudinal distance of the target vehicle corresponding to the interaction with the obstacle obs_i (expressed as s_ego_min_t0) is updated to the larger value of s_ego_min_t0 and (obs_i_s_max+lon_buffe); if the obstacle is close to the target vehicle, the minimum target longitudinal speed of the target vehicle (expressed as v_ego_min_t0) needs to be updated to the speed of the obstacle.

[0080] By traversing all obstacles that need to be yielded and overtaken, the boundary value of the longitudinal feasible range of the target vehicle corresponding to t0 can be updated. This boundary value includes the target longitudinal minimum feasible distance and the target longitudinal maximum feasible distance, that is, the longitudinal feasible range of the target vehicle is [s_ego_min_t0, s_ego_max_t0], as well as the obstacle ID corresponding to the boundary value and the longitudinal speed constraint information of the target vehicle. The longitudinal speed constraint information includes the minimum target longitudinal speed and the maximum target longitudinal speed, that is, the longitudinal speed constraint range of the target vehicle is [v_ego_min_t0, v_ego_max_t0].

[0081] By referring to the execution steps at discrete time t0, the longitudinal feasible range (corresponding to the target longitudinal minimum distance and the target longitudinal maximum distance) and longitudinal speed constraint information (corresponding to the target longitudinal minimum speed and the target longitudinal maximum speed) of the target vehicle from the 0th to the 10th second in the future can be obtained.

[0082] S303: Construct a longitudinal feasible space diagram of the target vehicle corresponding to a preset future time range based on the target longitudinal feasible minimum distance, the target longitudinal feasible maximum distance, the minimum target longitudinal speed, and the maximum target longitudinal speed.

[0083] In this step, illustratively, the future preset time range is, for example, the next 10 seconds. After obtaining the target longitudinal feasible minimum distance, the target longitudinal feasible maximum distance, the minimum value of the target longitudinal speed, and the maximum value of the target longitudinal speed, a longitudinal feasible space diagram of the target vehicle corresponding to the next 10 seconds can be constructed based on the target longitudinal feasible minimum distance, the target longitudinal feasible maximum distance, the minimum value of the target longitudinal speed, and the maximum value of the target longitudinal speed. The diagram is used to describe the longitudinal feasible minimum distance and the longitudinal feasible maximum distance corresponding to the target vehicle at each discrete moment in the next 10 seconds, that is, the longitudinal feasible range corresponding to the target vehicle at each discrete moment in the next 10 seconds, and within the longitudinal feasible range there is a corresponding minimum value of the target longitudinal speed and a maximum value of the target longitudinal speed, that is, the longitudinal speed constraint information.

[0084] S304. For the target longitudinal feasible minimum distance and the target longitudinal feasible maximum distance corresponding to discrete moments within a preset future time range, obtain an updated target longitudinal feasible minimum distance corresponding to the current discrete moment based on the target longitudinal feasible minimum distance corresponding to the previous discrete moment and the target longitudinal feasible minimum distance corresponding to the current discrete moment, and obtain an updated target longitudinal feasible maximum distance corresponding to the current discrete moment based on the target maximum feasible distance and the target longitudinal feasible maximum distance corresponding to the current discrete moment.

[0085] This step can be understood as a rationality check on the boundary values of the longitudinal feasible range corresponding to discrete moments within a future preset time range. Exemplarily, since it is not a reverse trajectory planning, it is necessary to ensure that the target longitudinal feasible minimum distance corresponding to the current discrete moment is not less than the target longitudinal feasible minimum distance corresponding to the adjacent previous discrete moment, and the driving process is constrained by the target maximum feasible distance.

[0086] In this step, further optionally, based on the target longitudinal feasible minimum distance corresponding to the previous discrete moment and the target longitudinal feasible minimum distance corresponding to the current discrete moment, obtain the updated target longitudinal feasible minimum distance corresponding to the current discrete moment, and based on the target maximum feasible distance and the target longitudinal feasible maximum distance corresponding to the current discrete moment, obtain the updated target longitudinal feasible maximum distance corresponding to the current discrete moment, which may include: for discrete moments within the future preset time range, if it is determined that the updated target longitudinal feasible maximum distance is less than the updated target longitudinal feasible minimum distance, then update the interaction behavior between the corresponding obstacle and the target vehicle, and reconstruct the longitudinal feasible space graph of the target vehicle corresponding to the future preset time range.

[0087] In the above process of rationality check on the boundary values of the longitudinal feasible range, if at a certain discrete moment s_ego_max < s_ego_min, it means that if the expected interaction decision needs to be satisfied, that is, passing a certain obstacle will cause it impossible to yield to the obstacles encountered next, taking safety as the highest consideration, then it is necessary to modify the expected interaction decision of the obstacle of this pass_type to yield_type, and repeat the above steps S302, S303, and S304 until the boundary values of the longitudinal feasible range corresponding to all discrete moments are reasonable values, and reconstruct the longitudinal feasible space graph of the target vehicle corresponding to the future preset time range.

[0088] S305. Obtain the updated longitudinal feasible space graph according to the updated target longitudinal feasible minimum distance and the updated target longitudinal feasible maximum distance.

[0089] In this step, after obtaining the updated target longitudinal feasible minimum distance and the updated target longitudinal feasible maximum distance corresponding to each discrete moment within the future preset time range, the updated longitudinal feasible space graph can be obtained according to the updated target longitudinal feasible minimum distance and the updated target longitudinal feasible maximum distance.

[0090] In the embodiments of the present application, Figure 2 Step S203 may further include the following four steps S306 to S309:

[0091] S306. Based on the longitudinal feasible space graph, according to the maximum acceleration capability and maximum deceleration capability of the target vehicle, the curvature corresponding to the reference line of the path within the longitudinal feasible range, and the correspondence between the preset curvature and the speed constraint, obtain updated longitudinal feasible range and longitudinal speed constraint information.

[0092] In this step, the reference line of the path within the longitudinal feasible range is, for example, the center line of the path within the longitudinal feasible range. For example, the following three constraints are first determined: (1) Considering the driving experience and the continuity of the controller control signal of the target vehicle (such as the control signal corresponding to the opening of the accelerator pedal), it is necessary to ensure the continuity of the initial state of the target vehicle (i.e., speed and acceleration), that is, the planned initial speed and initial acceleration at the starting point are initialized according to the current execution state of the target vehicle; (2) Considering the execution capability of the target vehicle, such as the maximum acceleration capability and maximum deceleration capability of the target vehicle, determine the constraint information of the executable speed at the next discrete moment and the constraint information of the executable distance at the next discrete moment; (3) Considering the lane shape, such as excessive speed during turning, it is easy to cause the target vehicle to become unstable or reduce the riding experience. Therefore, it is necessary to add constraints to the speed of the target vehicle according to the curvature of the lane line; specifically, for example, for a discrete time t, any sampling point that meets the boundary value constraint of the longitudinal feasible range is represented as (t_i, s_i), where s_i represents the longitudinal feasible distance of any sampling point, and the corresponding curvature information on the reference line is queried, and the maximum speed constraint at the position is obtained by querying the correspondence between the preset curvature and the speed constraint.

[0093] Based on the longitudinal feasible range and longitudinal speed constraint information corresponding to discrete moments of the target vehicle within a preset future time range in the longitudinal feasible space diagram, the updated longitudinal feasible range and longitudinal speed constraint information can be obtained according to the above three constraints.

[0094] S307 : Obtain an updated longitudinal feasible space diagram according to the updated longitudinal feasible range and longitudinal speed constraint information.

[0095] In this step, after obtaining the updated longitudinal feasible range and longitudinal speed constraint information corresponding to each discrete moment in the future preset time range, an updated longitudinal feasible space diagram can be obtained based on the updated longitudinal feasible range and longitudinal speed constraint information.

[0096] S308 : Based on the updated longitudinal feasible space graph and the first preset cost function, obtain a first target position of the target vehicle corresponding to a discrete moment.

[0097] Exemplarily, the first preset cost function cost_function is defined as:

[0098] cost_function=coefficient_vel*(max_vel_i–vel_i)+coefficient_acc*acc_i+coefficient_jerk*jerk_i

[0099] Among them, coefficient_vel, coefficient_acc and coefficient_jerk respectively represent the weight factors of velocity, acceleration and jerk of the first preset cost function, and all are positive values; vel_i represents the velocity of the sampling point at the i-th discrete moment, max_vel_i represents the maximum velocity determined according to the maximum curvature of the path at the i-th discrete moment; acc_i represents the acceleration of the sampling point at the i-th discrete moment; jerk_i represents the jerk of the sampling point at the i-th discrete moment.

[0100] In this step, after obtaining the updated longitudinal feasible space graph, the first target position of the target vehicle corresponding to each discrete moment can be obtained based on the updated longitudinal feasible space graph and the first preset cost function. This process can be understood as a dynamic programming solution process within a finite space. Ultimately, a discrete sequence that increases in time and satisfies the boundary values of the longitudinal feasible range can be searched, which is expressed as the st discrete sequence, where s represents the first target position of the target vehicle corresponding to the discrete moment t.

[0101] S309: Perform fitting processing on the first target position to obtain a reference speed of the target vehicle.

[0102] In this step, after obtaining the first target position, a fitting process can be performed on the first target position to obtain a reference speed of the target vehicle. For example, the ST discrete sequence points obtained in step S308 are fitted to obtain the reference speed of the target vehicle. The reference speed is specifically described in the form of an ST curve.

[0103] In the embodiment of this application, Figure 2 Step S204 may further include the following two steps S310 and S311:

[0104] S310: Obtain a target candidate path corresponding to a future preset time range according to at least one preset candidate path corresponding to the target vehicle and a reference speed.

[0105] Among them, the preset candidate path is obtained by sampling the future driving path of the target vehicle longitudinally and transversely respectively.

[0106] For example, the current position of the target vehicle is used as the longitudinal starting position, and the longitudinal position is sampled at equal intervals along the reference line direction. For each sampling point (denoted as ss), the corresponding lane width is queried as the transverse sampling space, and the transverse sampling space is divided at equal intervals to obtain discrete points (ss, dd), where dd represents the transverse position of the target vehicle relative to the reference line. Using a spline curve to smoothly connect the starting point and each sampling point, multiple initial candidate paths can be generated, which are described in the form of an ss-dd curve, that is, the preset candidate path corresponding to the target vehicle is obtained. After obtaining the preset candidate path corresponding to the target vehicle, the target candidate path corresponding to the preset time range in the future can be obtained based on the preset candidate path corresponding to the target vehicle and the reference speed.

[0107] Furthermore, optionally, based on at least one preset candidate path and a reference speed corresponding to the target vehicle, a target candidate path corresponding to a preset time range in the future is obtained, including: for each preset candidate path, obtaining a longitudinal forward distance corresponding to the target vehicle at a discrete moment based on the reference speed; obtaining a second target position of the target vehicle at a discrete moment based on the longitudinal forward distance and the preset candidate path; and performing coordinate transformation on the second target position to obtain the target candidate path.

[0108] For example, for each preset candidate path, at equal time intervals, that is, for each discrete moment, the longitudinal advance distance of the target vehicle corresponding to that discrete moment is queried on the reference speed curve ST curve. This longitudinal advance distance is represented by, for example, s1. The lateral position of the target vehicle relative to the reference line is obtained by interpolation of s1 on the SS-DD curve. This lateral position is represented by, for example, dd1. The position information (s1, dd1) at that discrete moment is transformed to obtain the spatial coordinates (x, y) of the target vehicle. In this way, the target candidate path (also called the target candidate trajectory) for a preset time range in the future (e.g., 10 seconds) can be deduced.

[0109] S311 : Determine a target trajectory of the target vehicle within a future preset time range based on the candidate target path and a second preset cost function.

[0110] The second preset cost function is determined based on the driving safety, comfort and stability of the target vehicle.

[0111] For example, after obtaining the target candidate path (i.e., the target candidate trajectory), for each target candidate trajectory, when defining the second preset cost function (denoted as cost_function_2), it is necessary to comprehensively consider the potential collision risk of the target candidate trajectory, that is, the probability that the target vehicle and the obstacle appear in the same position at the same time, which is used to determine the driving safety of the target vehicle; the comfort of the trajectory, that is, the acceleration of the trajectory and its rate of change; and the stability of the trajectory, that is, the maximum yaw angular velocity of the target candidate trajectory. Therefore, the second preset cost function is defined as:

[0112] cost_function_2=coefficient_risk*trajectory_risk_value+coefficient_comfort*(trajectory_max_jerk+trajectory_max_acc)+coefficient_stability*trajectory_max_yaw_rate;

[0113] Among them, trajectory_risk_value, trajectory_max_jerk, trajectory_max_acc, and trajectory_max_yaw_rate represent the collision risk, maximum jerk, maximum acceleration, and maximum yaw rate of the entire target candidate trajectory, respectively; coefficient_risk, coefficient_comfort, and coefficient_stability represent the weight factor of the potential collision risk, comfort, and stability of the target candidate trajectory, respectively, and all are positive values.

[0114] In this step, after obtaining the target candidate path, the target trajectory of the target vehicle within the future preset time range can be determined based on the target candidate path and the second preset cost function, that is, an optimal trajectory is finally selected and published to the control module of the target vehicle to control the target vehicle to travel according to the planned optimal trajectory.

[0115] The driving trajectory planning method provided by the embodiment of the present application determines the expected interaction decision of the target vehicle with the obstacle within a preset time range in the future; based on the expected interaction decision, determines the target longitudinal feasible minimum distance, target longitudinal feasible maximum distance, minimum target longitudinal speed and maximum target longitudinal speed corresponding to discrete moments in the preset time range in the future, and constructs a longitudinal feasible space diagram of the target vehicle corresponding to the preset time range in the future according to the target longitudinal feasible minimum distance, target longitudinal feasible maximum distance, minimum target longitudinal speed and maximum target longitudinal speed; for the target longitudinal feasible minimum distance and target longitudinal feasible maximum distance corresponding to discrete moments in the preset time range in the future, obtains the updated target longitudinal feasible minimum distance corresponding to the current discrete moment according to the target longitudinal feasible minimum distance corresponding to the previous discrete moment and the target longitudinal feasible minimum distance corresponding to the current discrete moment, and obtains the target longitudinal feasible maximum distance corresponding to the current discrete moment according to the target maximum feasible distance and the target longitudinal feasible maximum distance corresponding to the current discrete moment. Corresponding updated target longitudinal feasible maximum distance; based on the updated target longitudinal feasible minimum distance and the updated target longitudinal feasible maximum distance, an updated longitudinal feasible space diagram is obtained; based on the longitudinal feasible space diagram, according to the maximum acceleration capability, maximum deceleration capability of the target vehicle, the curvature corresponding to the reference line of the path within the longitudinal feasible range, and the correspondence between the preset curvature and the speed constraint, the updated longitudinal feasible range and longitudinal speed constraint information are obtained; based on the updated longitudinal feasible range and longitudinal speed constraint information, an updated longitudinal feasible space diagram is obtained; based on the updated longitudinal feasible space diagram and the first preset cost function, the first target position of the target vehicle corresponding to the discrete moment is obtained; the first target position is fitted to obtain the reference speed of the target vehicle; based on at least one preset candidate path and the reference speed corresponding to the target vehicle, a target candidate path corresponding to the future preset time range is obtained; based on the target candidate path and the second preset cost function, the target trajectory of the target vehicle within the future preset time range is determined. Since the embodiment of the present application fully considers the information of static and dynamic obstacles in the driving scene, determines the expected interaction decision, and then constructs the longitudinal feasible space map of the target vehicle, based on the longitudinal feasible space map, combined with the execution capability of the target vehicle, and under the premise of reasonable interaction with obstacles, a reference speed closest to the final execution of the target vehicle is obtained, and this reference speed is used to evaluate the optimal path. Therefore, the optimal path can be obtained more accurately, the rationality and reliability of the driving trajectory planning results are improved, driving efficiency is improved, and the driving experience is improved.

[0116] The following are device embodiments of the present application, which can be used to implement the method embodiments of the present application. For details not disclosed in the device embodiments of the present application, please refer to the method embodiments of the present application.

[0117] Figure 4This is a structural diagram of a driving trajectory planning device provided in one embodiment of the present application, as shown in FIG. Figure 4 As shown, the driving trajectory planning device 400 of the embodiment of the present application includes: a determination module 401, a construction module 402, an acquisition module 403 and a processing module 404. Among them:

[0118] Determination module 401 is used to determine the expected interaction decision between the target vehicle and the obstacle within a preset time range in the future. The expected interaction decision is used to characterize the interaction behavior between the target vehicle and the obstacle and the interaction time window corresponding to the interaction behavior. The interaction behavior includes the target vehicle actively overtaking the obstacle, the target vehicle actively giving way to the obstacle, and the target vehicle actively ignoring the obstacle.

[0119] Construction module 402 is used to construct a longitudinal feasible space diagram of the target vehicle corresponding to a preset future time range based on the expected interactive decision. The longitudinal feasible space diagram is used to represent the longitudinal feasible range and longitudinal speed constraint information corresponding to discrete moments in the preset future time range of the target vehicle.

[0120] The acquisition module 403 is configured to obtain a reference speed of the target vehicle based on the longitudinal feasible space diagram and a first preset cost function, where the first preset cost function is determined based on the driving efficiency and comfort of the target vehicle.

[0121] The processing module 404 is configured to determine a target trajectory of the target vehicle within a preset future time range based on the reference speed.

[0122] In some embodiments, the construction module 402 can be specifically used to: determine the target longitudinal feasible minimum distance, target longitudinal feasible maximum distance, minimum target longitudinal speed, and maximum target longitudinal speed corresponding to discrete moments within a preset future time range based on the expected interactive decision; and construct a longitudinal feasible space diagram of the target vehicle corresponding to the preset future time range based on the target longitudinal feasible minimum distance, target longitudinal feasible maximum distance, minimum target longitudinal speed, and maximum target longitudinal speed.

[0123] Optionally, when the construction module 402 is used to determine the target longitudinal feasible minimum distance, target longitudinal feasible maximum distance, minimum value of target longitudinal speed and maximum value of target longitudinal speed corresponding to discrete moments within a preset time range in the future based on expected interactive decision-making, it can be specifically used to: determine the initial longitudinal feasible maximum distance, initial longitudinal feasible minimum distance, minimum value of initial longitudinal speed and maximum value of initial longitudinal speed of the target vehicle; for each obstacle corresponding to the discrete moments within the preset time range in the future, perform the following operations until each obstacle is traversed: based on the expected interactive decision, if it is determined that the target vehicle needs to actively give way to the target obstacle, then determine the target vehicle's longitudinal and lateral space occupied by the target obstacle, the preset safe distance for giving way and the initial longitudinal feasible maximum distance. A target longitudinal feasible maximum distance for the target vehicle is determined, and based on the speed of the target obstacle, the maximum value of the target longitudinal speed is determined, the target longitudinal feasible maximum distance is determined as the new initial longitudinal feasible maximum distance, and the maximum value of the target longitudinal speed is determined as the new maximum value of the initial longitudinal speed; or, based on the expected interactive decision, if it is determined that the target vehicle needs to actively overtake the target obstacle, the target longitudinal feasible minimum distance for the target vehicle is determined based on the longitudinal and lateral space occupied by the target obstacle, the preset overtaking safety distance, and the initial longitudinal feasible minimum distance, and the minimum value of the target longitudinal speed is determined based on the speed of the target obstacle, the target longitudinal feasible minimum distance is determined as the new initial longitudinal feasible minimum distance, and the minimum value of the target longitudinal speed is determined as the new minimum value of the initial longitudinal speed.

[0124] Optionally, when the construction module 402 is used to determine the initial longitudinal feasible maximum distance of the target vehicle, it can be specifically used to: determine the maximum feasible distance of the target vehicle within a future preset time range based on the driving scenario speed limit of the target vehicle; determine the maximum speed corresponding to the maximum curvature according to the maximum curvature of the path traveled by the target vehicle within the future preset time range and the correspondence between the preset curvature and the speed constraint; obtain the target maximum feasible distance based on the maximum speed and the maximum feasible distance; and determine the target maximum feasible distance as the initial longitudinal feasible maximum distance.

[0125] In some embodiments, the driving trajectory planning device also includes an updating module 405, which is used to, after the construction module 402 constructs the longitudinal feasible space diagram of the target vehicle corresponding to the future preset time range, obtain the updated target longitudinal feasible minimum distance corresponding to the current discrete moment according to the target longitudinal feasible minimum distance corresponding to the previous discrete moment and the target longitudinal feasible minimum distance corresponding to the current discrete moment, and obtain the updated target longitudinal feasible maximum distance corresponding to the current discrete moment according to the target maximum feasible distance and the target longitudinal feasible maximum distance corresponding to the current discrete moment; and obtain the updated longitudinal feasible space diagram according to the updated target longitudinal feasible minimum distance and the updated target longitudinal feasible maximum distance.

[0126] Optionally, when the update module 405 is used to obtain the updated target longitudinal feasible minimum distance corresponding to the current discrete moment based on the target longitudinal feasible minimum distance corresponding to the previous discrete moment and the target longitudinal feasible minimum distance corresponding to the current discrete moment, and to obtain the updated target longitudinal feasible maximum distance corresponding to the current discrete moment based on the target maximum feasible distance and the target longitudinal feasible maximum distance corresponding to the current discrete moment, it can be specifically used to: for discrete moments within a future preset time range, if it is determined that the updated target longitudinal feasible maximum distance is less than the updated target longitudinal feasible minimum distance, then update the corresponding interaction behavior between the obstacle and the target vehicle, and reconstruct the longitudinal feasible space map of the target vehicle corresponding to the future preset time range.

[0127] In some embodiments, the acquisition module 403 can be specifically used to: based on the longitudinal feasible space diagram, according to the maximum acceleration capability, maximum deceleration capability, the curvature corresponding to the reference line of the path within the longitudinal feasible range, and the correspondence between the preset curvature and the speed constraint, obtain the updated longitudinal feasible range and longitudinal speed constraint information; based on the updated longitudinal feasible range and longitudinal speed constraint information, obtain the updated longitudinal feasible space diagram; based on the updated longitudinal feasible space diagram and the first preset cost function, obtain the first target position of the target vehicle corresponding to the discrete moment; perform fitting processing on the first target position to obtain the reference speed of the target vehicle.

[0128] In some embodiments, the processing module 404 can be specifically used to: obtain a target candidate path corresponding to a future preset time range based on at least one preset candidate path and a reference speed corresponding to the target vehicle, where the preset candidate path is obtained by longitudinally and transversely sampling the future driving path of the target vehicle; determine the target trajectory of the target vehicle within the future preset time range based on the target candidate path and a second preset cost function, where the second preset cost function is determined based on the driving safety, comfort, and stability of the target vehicle.

[0129] Optionally, when the processing module 404 is used to obtain a target candidate path corresponding to a future preset time range based on at least one preset candidate path and a reference speed corresponding to the target vehicle, it can be specifically used to: for each preset candidate path, obtain the longitudinal forward distance corresponding to the target vehicle at a discrete moment based on the reference speed; obtain the second target position of the target vehicle corresponding to the discrete moment based on the longitudinal forward distance and the preset candidate path; and perform coordinate transformation on the second target position to obtain the target candidate path.

[0130] The device of this embodiment can be used to execute the technical solution of any of the above-mentioned method embodiments. Its implementation principles and technical effects are similar and will not be repeated here.

[0131] Figure 5 This is a schematic diagram of the structure of an electronic device provided in this application. Figure 5 As shown, the electronic device 500 may include: at least one processor 501 and a memory 502.

[0132] The memory 502 is used to store programs. Specifically, the programs may include program codes, and the program codes include computer operation instructions.

[0133] The memory 502 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0134] The processor 501 is used to execute the computer-executable instructions stored in the memory 502 to implement the driving trajectory planning method described in the aforementioned method embodiment. Among them, the processor 501 may be a central processing unit (CPU), or an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application. Specifically, when implementing the driving trajectory planning method described in the aforementioned method embodiment, the electronic device may be, for example, an electronic device with processing functions such as a terminal and a server. When implementing the driving trajectory planning method described in the aforementioned method embodiment, the electronic device may be, for example, an electronic control unit on a vehicle.

[0135] Optionally, the electronic device 500 may further include a communication interface 503. In a specific implementation, if the communication interface 503, the memory 502, and the processor 501 are implemented independently, the communication interface 503, the memory 502, and the processor 501 may be interconnected via a bus and communicate with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. Buses can be divided into address buses, data buses, control buses, etc., but this does not mean that there is only one bus or only one type of bus.

[0136] Optionally, in a specific implementation, if the communication interface 503, the memory 502 and the processor 501 are integrated on a chip, the communication interface 503, the memory 502 and the processor 501 can complete communication through an internal interface.

[0137] The present application also provides a computer-readable storage medium, which may include: a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, and other media that can store program codes. Specifically, the computer-readable storage medium stores program instructions, and the program instructions are used in the driving trajectory planning method in the above embodiment.

[0138] The present application also provides a computer program product, comprising execution instructions stored in a readable storage medium. At least one processor of an electronic device can read the execution instructions from the readable storage medium, and at least one processor executes the execution instructions to cause the electronic device to implement the vehicle trajectory planning methods provided in the various embodiments described above.

[0139] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A driving trajectory planning method, characterized in that: include: Determining an expected interaction decision between the target vehicle and the obstacle within a preset future time range, the expected interaction decision being used to characterize an interaction behavior between the target vehicle and the obstacle and an interaction time window corresponding to the interaction behavior, wherein the interaction behavior includes the target vehicle actively overtaking the obstacle, the target vehicle actively yielding to the obstacle, and the target vehicle actively ignoring the obstacle; Based on the expected interactive decision, construct a longitudinal feasible space graph of the target vehicle corresponding to the future preset time range, wherein the longitudinal feasible space graph is used to represent the longitudinal feasible range and longitudinal speed constraint information of the target vehicle corresponding to discrete moments within the future preset time range; Obtaining a reference speed of the target vehicle based on the longitudinal feasible space map and a first preset cost function, wherein the first preset cost function is determined based on the driving efficiency and comfort of the target vehicle; determining a target trajectory of the target vehicle within the future preset time range based on the reference speed; The obtaining of a reference speed of the target vehicle based on the longitudinal feasible space graph and a first preset cost function includes: Based on the longitudinal feasible space graph, obtaining updated longitudinal feasible range and longitudinal speed constraint information according to the maximum acceleration capability and maximum deceleration capability of the target vehicle, the curvature corresponding to the reference line of the path within the longitudinal feasible range, and the correspondence between the preset curvature and the speed constraint; Obtaining an updated longitudinal feasible space diagram according to the updated longitudinal feasible range and longitudinal speed constraint information; Based on the updated longitudinal feasible space graph and the first preset cost function, obtaining a first target position of the target vehicle corresponding to the discrete moment; A fitting process is performed on the first target position to obtain a reference speed of the target vehicle.

2. The driving trajectory planning method according to claim 1, characterized in that: The step of constructing a longitudinal feasible space diagram of the target vehicle corresponding to the future preset time range based on the expected interactive decision includes: Determining, based on the expected interactive decision, a target feasible minimum longitudinal distance, a target feasible maximum longitudinal distance, a minimum target longitudinal speed, and a maximum target longitudinal speed for the target vehicle at discrete moments within the future preset time range; A longitudinal feasible space diagram of the target vehicle corresponding to the future preset time range is constructed according to the target feasible minimum longitudinal distance, the target feasible maximum longitudinal distance, the minimum value of the target longitudinal speed, and the maximum value of the target longitudinal speed.

3. The driving trajectory planning method according to claim 2, characterized in that: The determining, based on the expected interactive decision, of a target feasible minimum longitudinal distance, a target feasible maximum longitudinal distance, a minimum target longitudinal speed, and a maximum target longitudinal speed corresponding to discrete moments within the future preset time range of the target vehicle includes: Determining an initial feasible maximum longitudinal distance, an initial feasible minimum longitudinal distance, a minimum initial longitudinal velocity, and a maximum initial longitudinal velocity of the target vehicle; For each obstacle corresponding to a discrete moment in the future preset time range, perform the following operations until all obstacles are traversed: If it is determined, based on the expected interactive decision, that the target vehicle is required to actively yield to the target obstacle, determining a target longitudinal maximum distance for the target vehicle based on the longitudinal and lateral spaces occupied by the target obstacle, the preset yield safety distance, and the initial longitudinal maximum distance, and determining a maximum value of a target longitudinal speed based on the speed of the target obstacle, determining the target longitudinal maximum distance as a new initial longitudinal maximum distance, and determining the maximum value of the target longitudinal speed as a new maximum value of the initial longitudinal speed; Alternatively, based on the expected interactive decision, if it is determined that the target vehicle needs to actively overtake the target obstacle, the target longitudinal minimum distance of the target vehicle is determined based on the longitudinal and lateral space occupied by the target obstacle, the preset overtaking safety distance, and the initial longitudinal minimum feasible distance, and the minimum value of the target longitudinal speed is determined based on the speed of the target obstacle, the target longitudinal minimum distance is determined as the new initial longitudinal minimum feasible distance, and the minimum value of the target longitudinal speed is determined as the new minimum value of the initial longitudinal speed.

4. The driving trajectory planning method according to claim 3, characterized in that: Determining the initial feasible maximum longitudinal distance of the target vehicle includes: Determining a maximum feasible distance of the target vehicle within the future preset time range based on the driving scenario speed limit of the target vehicle; Determining a maximum speed corresponding to the maximum curvature according to a maximum curvature of a path traveled by the target vehicle within the future preset time range and a correspondence between the preset curvature and the speed constraint; Obtaining a target maximum feasible distance according to the maximum speed and the maximum feasible distance; The target maximum feasible distance is determined to be the initial longitudinal maximum feasible distance.

5. The driving trajectory planning method according to claim 4, characterized in that: After constructing the longitudinal feasible space graph of the target vehicle corresponding to the future preset time range, the method further includes: For the target feasible minimum longitudinal distance and the target feasible maximum longitudinal distance corresponding to discrete moments within the future preset time range, obtain an updated target feasible minimum longitudinal distance corresponding to the current discrete moment based on the target feasible minimum longitudinal distance corresponding to the previous discrete moment and the target feasible minimum longitudinal distance corresponding to the current discrete moment, and obtain an updated target feasible maximum longitudinal distance corresponding to the current discrete moment based on the target maximum feasible distance and the target feasible maximum longitudinal distance corresponding to the current discrete moment; An updated longitudinal feasible space graph is obtained according to the updated target longitudinal feasible minimum distance and the updated target longitudinal feasible maximum distance.

6. The driving trajectory planning method according to claim 5, characterized in that: The step of obtaining an updated target longitudinal feasible minimum distance corresponding to the current discrete moment based on the target longitudinal feasible minimum distance corresponding to the previous discrete moment and the target longitudinal feasible minimum distance corresponding to the current discrete moment, and obtaining an updated target longitudinal feasible maximum distance corresponding to the current discrete moment based on the target maximum feasible distance and the target longitudinal feasible maximum distance corresponding to the current discrete moment, includes: For discrete moments within the future preset time range, if it is determined that the updated target longitudinal feasible maximum distance is less than the updated target longitudinal feasible minimum distance, the interaction behavior between the corresponding obstacle and the target vehicle is updated, and the longitudinal feasible space diagram of the target vehicle corresponding to the future preset time range is reconstructed.

7. The driving trajectory planning method according to claim 1, characterized in that: Determining the target trajectory of the target vehicle within the future preset time range based on the reference speed includes: Obtaining a target candidate path corresponding to the future preset time range based on at least one preset candidate path corresponding to the target vehicle and the reference speed, wherein the preset candidate path is obtained by longitudinally and transversely sampling the future travel path of the target vehicle; The target trajectory of the target vehicle within the future preset time range is determined based on the target candidate path and a second preset cost function, wherein the second preset cost function is determined based on the driving safety, comfort and stability of the target vehicle.

8. The driving trajectory planning method according to claim 7, characterized in that: The obtaining, based on the at least one preset candidate path corresponding to the target vehicle and the reference speed, of a target candidate path corresponding to the future preset time range includes: For each of the preset candidate paths, obtaining a longitudinal forward distance of the target vehicle corresponding to the discrete moment according to the reference speed; Obtaining a second target position of the target vehicle corresponding to the discrete moment according to the longitudinal forward distance and the preset candidate path; Perform coordinate transformation on the second target position to obtain the candidate target path.

9. A driving trajectory planning device, characterized in that: include: a determination module, configured to determine an expected interaction decision between the target vehicle and the obstacle within a preset future time range, wherein the expected interaction decision is used to characterize an interaction behavior between the target vehicle and the obstacle and an interaction time window corresponding to the interaction behavior, wherein the interaction behavior includes the target vehicle actively overtaking the obstacle, the target vehicle actively yielding to the obstacle, and the target vehicle actively ignoring the obstacle; a construction module, configured to construct, based on the expected interactive decision, a longitudinal feasible space graph of the target vehicle corresponding to the future preset time range, the longitudinal feasible space graph being used to represent the longitudinal feasible range and longitudinal speed constraint information of the target vehicle corresponding to discrete moments within the future preset time range; an acquisition module, configured to obtain a reference speed of the target vehicle based on the longitudinal feasible space map and a first preset cost function, wherein the first preset cost function is determined based on the driving efficiency and comfort of the target vehicle; a processing module, configured to determine a target trajectory of the target vehicle within the future preset time range based on the reference speed; The acquisition module is specifically configured to obtain updated longitudinal feasible range and longitudinal speed constraint information based on the longitudinal feasible space graph, the maximum acceleration capability and the maximum deceleration capability of the target vehicle, the curvature corresponding to the reference line of the path within the longitudinal feasible range, and the correspondence between the preset curvature and the speed constraint; Obtaining an updated longitudinal feasible space diagram according to the updated longitudinal feasible range and longitudinal speed constraint information; Based on the updated longitudinal feasible space graph and the first preset cost function, obtaining a first target position of the target vehicle corresponding to the discrete moment; A fitting process is performed on the first target position to obtain a reference speed of the target vehicle.

10. An electronic device, characterized in that: include: a processor, and a memory communicatively connected to the processor; The memory stores computer-executable instructions; The processor executes the computer-executable instructions stored in the memory to implement the driving trajectory planning method according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer program instructions, and when the computer program instructions are executed by the processor, the driving trajectory planning method according to any one of claims 1 to 8 is implemented.

12. A computer program product comprising a computer program, characterized in that When the computer program is executed by a processor, the driving trajectory planning method according to any one of claims 1 to 8 is implemented.

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