CN114620070A

CN114620070A - Driving track planning method, device, equipment and storage medium

Info

Publication number: CN114620070A
Application number: CN202210143460.4A
Authority: CN
Inventors: 彭莹; 邱椿荣; 杨政; 何晓飞
Original assignee: Hangzhou Fabu Technology Co Ltd
Current assignee: Hangzhou Fabu Technology Co Ltd
Priority date: 2022-02-16
Filing date: 2022-02-16
Publication date: 2022-06-14

Abstract

The application provides a driving path planning method, a driving path planning device, equipment and a storage medium, which relate to the technical field of intelligent driving, and the driving path planning method comprises the following steps: the method comprises the steps of determining an expected interaction decision of a target vehicle and an obstacle in a future preset time range, constructing a longitudinal feasible space map of the target vehicle corresponding to the future preset time range based on the expected interaction decision, obtaining a reference speed of the target vehicle based on the longitudinal feasible space map and a first preset cost function, and determining a target track of the target vehicle in the future preset time range based on the reference speed. The method and the device can more accurately obtain the optimal path, improve the reasonability and reliability of the driving track planning result, improve the driving efficiency and improve the driving experience.

Description

Driving track planning method, device, equipment and storage medium

Technical Field

The present application relates to the field of intelligent driving technologies, and in particular, to a method, an apparatus, a device, and a storage medium for driving trajectory planning.

Background

With the development of the automatic driving technology, automatic driving vehicles are gradually developed and applied. When the automatic driving vehicle runs, the planned driving track is provided for the automatic driving vehicle, so that the automatic driving vehicle can automatically run according to the planned driving track.

At present, a sampling-based path optimization method is often adopted to plan a driving track of an autonomous vehicle, and the method specifically comprises the following steps: the method comprises the steps of sampling the convergence position of an automatic driving vehicle, obtaining a plurality of alternative paths, and determining the optimal path from the plurality of alternative paths by taking the current vehicle speed of the vehicle as a reference speed. The trajectory obtained by the above method may not be optimal.

Disclosure of Invention

The application provides a driving track planning method, a driving track planning device, a driving track planning equipment and a storage medium, which are used for solving the problem that the driving track obtained by adopting a sampling-based path optimization method is possibly not optimal.

In a first aspect, the present application provides a driving trajectory planning method, including:

determining an expected interaction decision of the target vehicle and the barrier in a future preset time range, wherein the expected interaction decision is used for representing an interaction behavior of the target vehicle and the barrier and an interaction time window corresponding to the interaction behavior, and the interaction behavior comprises that the target vehicle actively exceeds the barrier, the target vehicle actively gives way to the barrier and the target vehicle actively ignores the barrier;

constructing a longitudinal feasible space map of the target vehicle corresponding to a future preset time range based on the expected interaction decision, wherein the longitudinal feasible space map is used for representing the longitudinal feasible range and longitudinal speed constraint information of the target vehicle corresponding to discrete moments in the future preset time range;

obtaining a reference speed of the target vehicle based on the longitudinal feasible space diagram and a first preset cost function, wherein the first preset cost function is determined based on the running efficiency and the comfort degree of the target vehicle;

based on the reference speed, a target trajectory of the target vehicle within a future preset time range is determined.

Optionally, based on the expected interaction decision, constructing a longitudinal feasible space map of the target vehicle corresponding to a future preset time range, including: based on the expected interactive decision, determining a target longitudinal feasible minimum distance, a target longitudinal feasible maximum distance, a target longitudinal speed minimum value and a target longitudinal speed maximum value corresponding to the discrete time of the target vehicle in a future preset time range; and constructing a longitudinal feasible space map of the target vehicle corresponding to a future preset time range according to 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.

Optionally, determining, based on the expected interaction decision, a minimum longitudinally feasible target distance, a maximum longitudinally feasible target distance, a minimum longitudinally feasible target speed, and a maximum longitudinally feasible target speed corresponding to a discrete time of the target vehicle in a future preset time range, including: determining an initial longitudinally feasible maximum distance, an initial longitudinally feasible minimum distance, a minimum value of an initial longitudinal speed, and a maximum value of the initial longitudinal speed of the target vehicle; aiming at each obstacle corresponding to a discrete moment in a future preset time range, executing the following operations until each obstacle is traversed: based on the expected interactive decision, if the target obstacle needing to be actively yielded by the target vehicle is determined, determining the maximum feasible distance of the target longitudinal direction of the target vehicle according to the space occupied by the target obstacle in the longitudinal direction and the transverse direction, the preset yielding safety distance and the initial maximum feasible distance of the longitudinal direction, determining the maximum value of the target longitudinal speed according to the speed of the target obstacle, determining the maximum feasible distance of the target longitudinal direction as the new initial maximum feasible distance of the longitudinal direction, and determining the maximum value of the target longitudinal speed 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 is required to actively exceed the target obstacle, determining a target longitudinal feasible minimum distance of the target vehicle according to a space occupied by the target obstacle in the longitudinal and transverse directions, a preset overtaking safety distance and an initial longitudinal feasible minimum distance, determining a minimum value of a target longitudinal speed according to the speed of the target obstacle, determining the target longitudinal feasible minimum distance as a new initial longitudinal feasible minimum distance, and determining the minimum value of the target longitudinal speed as a new initial longitudinal speed minimum value.

Optionally, determining an initial longitudinally feasible maximum distance of the target vehicle comprises: determining the maximum feasible distance of the target vehicle within a future preset time range based on the driving scene speed limit of the target vehicle; determining the maximum speed corresponding to the maximum curvature according to the maximum curvature of a running path of the target vehicle in a future preset time range and the corresponding relation between the preset curvature and the speed constraint; obtaining a target maximum feasible distance according to the maximum speed and the maximum feasible distance; and determining the maximum feasible distance of the target as the initial longitudinal feasible maximum distance.

Optionally, after constructing the longitudinal feasible space map of the target vehicle corresponding to the future preset time range, the driving path planning method further includes: aiming at a target longitudinal feasible minimum distance and a target longitudinal feasible maximum distance corresponding to a discrete moment in a future preset time range, obtaining an updated target longitudinal feasible minimum distance corresponding to the current discrete moment according to a target longitudinal feasible minimum distance corresponding to a previous discrete moment and a 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 according to the target maximum feasible distance and the target longitudinal feasible maximum distance corresponding to the current discrete moment; and obtaining an updated longitudinal feasible space map according to the updated minimum distance of the target longitudinal feasible and the updated maximum distance of the target longitudinal feasible.

Optionally, obtaining an updated target longitudinally feasible minimum distance corresponding to the current discrete time according to the target longitudinally feasible minimum distance corresponding to the previous discrete time and the target longitudinally feasible minimum distance corresponding to the current discrete time, and obtaining an updated target longitudinally feasible maximum distance corresponding to the current discrete time according to the target maximum feasible distance and the target longitudinally feasible maximum distance corresponding to the current discrete time, includes: and aiming at the discrete time within the future preset time range, if the updated target longitudinal feasible maximum distance is determined to be smaller than the updated target longitudinal feasible minimum distance, updating the interaction behavior of the corresponding barrier and the target vehicle, and reconstructing a longitudinal feasible space diagram of the target vehicle corresponding to the future preset time range.

Optionally, obtaining the reference speed of the target vehicle based on the longitudinal feasible space map and the first preset cost function includes: based on the longitudinal feasible space diagram, obtaining updated longitudinal feasible range and longitudinal speed constraint information according to the maximum acceleration capacity and the maximum deceleration capacity of the target vehicle, the curvature corresponding to the reference line of the path in the longitudinal feasible range and the corresponding relation between the preset curvature and the speed constraint; obtaining an updated longitudinal feasible space map according to the updated longitudinal feasible range and longitudinal speed constraint information; acquiring a first target position of the target vehicle corresponding to the discrete moment based on the updated longitudinal feasible space diagram and a first preset cost function; and fitting the first target position to obtain the reference speed of the target vehicle.

Optionally, determining a target trajectory of the target vehicle within a future preset time range based on the reference speed includes: obtaining 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, wherein the preset candidate path is obtained by longitudinally and transversely sampling the future driving path of the target vehicle; and determining a target track of the target vehicle in a future preset time range according to the target candidate path and a second preset cost function, wherein the second preset cost function is determined based on the driving safety degree, the comfort degree and the stability of the target vehicle.

Optionally, obtaining a target candidate route corresponding to a future preset time range according to at least one preset candidate route corresponding to the target vehicle and the reference speed includes: aiming at each preset candidate path, obtaining a longitudinal advance distance corresponding to a 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 advance distance and the preset candidate path; and carrying out coordinate transformation on the second target position to obtain a target candidate path.

In a second aspect, the present application provides a driving path planning apparatus, including:

the system comprises a determining module, a judging module and a judging module, wherein the determining module is used for determining an expected interaction decision of a target vehicle and an obstacle in a future preset time range, the expected interaction decision is used for representing an interaction behavior of the target vehicle and the obstacle and an interaction time window corresponding to the interaction behavior, and the interaction behavior comprises that the target vehicle actively exceeds the obstacle, the target vehicle actively gives way to the obstacle and the target vehicle actively ignores the obstacle;

the construction module is used for constructing a longitudinal feasible space map corresponding to a future preset time range of the target vehicle based on the expected interaction decision, and the longitudinal feasible space map is used for representing the longitudinal feasible range and longitudinal speed constraint information corresponding to discrete moments of the target vehicle in the future preset time range;

the system comprises an acquisition module, a calculation module and a control module, wherein the acquisition module is used for acquiring a reference speed of a target vehicle based on a longitudinal feasible space diagram and a first preset cost function, and the first preset cost function is determined based on the running efficiency and the comfort degree of the target vehicle;

and the processing module is used for determining the target track of the target vehicle in a future preset time range based on the reference speed.

Optionally, the building module is specifically configured to: based on the expected interactive decision, determining a target longitudinal feasible minimum distance, a target longitudinal feasible maximum distance, a target longitudinal speed minimum value and a target longitudinal speed maximum value corresponding to discrete time of the target vehicle in a future preset time range; and constructing a longitudinal feasible space diagram of the target vehicle corresponding to a future preset time range according to 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.

Optionally, when the building module is configured to determine, based on the expected interaction decision, a target longitudinal feasible minimum distance, a target longitudinal feasible maximum distance, a target longitudinal speed minimum value, and a target longitudinal speed maximum value corresponding to a discrete time of the target vehicle within a future preset time range, the building module is specifically configured to: determining an initial longitudinally feasible maximum distance, an initial longitudinally feasible minimum distance, a minimum value of an initial longitudinal speed, and a maximum value of the initial longitudinal speed of the target vehicle; aiming at each obstacle corresponding to the discrete moment in the future preset time range, the following operations are executed until each obstacle is traversed: based on the expected interactive decision, if the target obstacle needing to be actively yielded by the target vehicle is determined, determining the maximum feasible distance of the target longitudinal direction of the target vehicle according to the space occupied by the target obstacle in the longitudinal direction and the transverse direction, the preset yielding safety distance and the initial maximum feasible distance of the longitudinal direction, determining the maximum value of the target longitudinal speed according to the speed of the target obstacle, determining the maximum feasible distance of the target longitudinal direction as the new initial maximum feasible distance of the longitudinal direction, and determining the maximum value of the target longitudinal speed 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 is required to actively exceed the target obstacle, determining a target longitudinal feasible minimum distance of the target vehicle according to a space occupied by the target obstacle in the longitudinal and transverse directions, a preset overtaking safety distance and an initial longitudinal feasible minimum distance, determining a minimum value of a target longitudinal speed according to the speed of the target obstacle, determining the target longitudinal feasible minimum distance as a new initial longitudinal feasible minimum distance, and determining the minimum value of the target longitudinal speed as a new initial longitudinal speed minimum value.

Optionally, the building module, when configured to determine an initial longitudinally feasible maximum distance of the target vehicle, is specifically configured to: determining the maximum feasible distance of the target vehicle within a future preset time range based on the driving scene speed limit of the target vehicle; determining the maximum speed corresponding to the maximum curvature according to the maximum curvature of a running path of the target vehicle in a future preset time range and the corresponding relation between the preset curvature and the speed constraint; obtaining a target maximum feasible distance according to the maximum speed and the maximum feasible distance; and determining the maximum feasible distance of the target as the initial longitudinal feasible maximum distance.

Optionally, the driving trajectory planning apparatus further includes an updating module, configured to, after the construction module constructs a longitudinal feasible space diagram of the target vehicle corresponding to a future preset time range, obtain, according to a target longitudinal feasible minimum distance corresponding to a previous discrete time and a target longitudinal feasible minimum distance corresponding to a current discrete time, an updated target longitudinal feasible minimum distance corresponding to the current discrete time for a target longitudinal feasible minimum distance and a target longitudinal feasible maximum distance corresponding to a discrete time within the future preset time range, and obtain, according to the target maximum feasible distance and the target longitudinal feasible maximum distance corresponding to the current discrete time, an updated target longitudinal feasible maximum distance corresponding to the current discrete time; and obtaining an updated longitudinal feasible space map according to the updated minimum distance of the target longitudinal feasible and the updated maximum distance of the target longitudinal feasible.

Optionally, the updating module is specifically configured to, when the updated target longitudinally feasible minimum distance corresponding to the current discrete time is obtained according to the target longitudinally feasible minimum distance corresponding to the previous discrete time and the target longitudinally feasible minimum distance corresponding to the current discrete time, and the updated target longitudinally feasible maximum distance corresponding to the current discrete time is obtained according to the target maximum feasible distance and the target longitudinally feasible maximum distance corresponding to the current discrete time: and aiming at the discrete time within the future preset time range, if the updated target longitudinal feasible maximum distance is determined to be smaller than the updated target longitudinal feasible minimum distance, updating the interaction behavior of the corresponding barrier and the target vehicle, and reconstructing a longitudinal feasible space diagram of the target vehicle corresponding to the future preset time range.

Optionally, the obtaining module is specifically configured to: based on the longitudinal feasible space diagram, obtaining updated longitudinal feasible range and longitudinal speed constraint information according to the maximum acceleration capacity and the maximum deceleration capacity of the target vehicle, the curvature corresponding to the reference line of the path in the longitudinal feasible range and the corresponding relation between the preset curvature and the speed constraint; obtaining an updated longitudinal feasible space map according to the updated longitudinal feasible range and longitudinal speed constraint information; acquiring a first target position of the target vehicle corresponding to the discrete moment based on the updated longitudinal feasible space diagram and a first preset cost function; and fitting the first target position to obtain the reference speed of the target vehicle.

Optionally, the processing module is specifically configured to: obtaining 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, wherein the preset candidate path is obtained by longitudinally and transversely sampling the future running path of the target vehicle; and determining a target track of the target vehicle in a future preset time range according to the target candidate path and a second preset cost function, wherein the second preset cost function is determined based on the driving safety degree, the comfort degree and the stability of the target vehicle.

Optionally, when the processing module is configured to obtain the target candidate route corresponding to the future preset time range according to at least one preset candidate route corresponding to the target vehicle and the reference speed, the processing module is specifically configured to: aiming at each preset candidate path, acquiring a longitudinal advancing distance corresponding to 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 advance distance and the preset candidate path; and carrying out coordinate transformation on the second target position to obtain a target candidate path.

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

the memory stores computer-executable instructions;

the processor executes the computer-executable instructions stored in the memory to implement the method for trajectory planning according to the first aspect of the present application.

In a fourth aspect, the present application provides a computer-readable storage medium, in which computer program instructions are stored, and when the computer program instructions are executed by a processor, the method for planning a driving trajectory according to the first aspect of the present application is implemented.

In a fifth aspect, the present application provides a computer program product comprising a computer program which, when executed by a processor, implements a driving trajectory planning method according to the first aspect of the present application.

According to the driving track planning method, the driving track planning device, the driving track planning equipment and the storage medium, the expected interaction decision of the target vehicle and the obstacle in the future preset time range is determined, the longitudinal feasible space diagram of the target vehicle corresponding to the future preset time range is constructed on the basis of the expected interaction decision, the reference speed of the target vehicle is obtained on the basis of the longitudinal feasible space diagram and the first preset cost function, and the target track of the target vehicle in the future preset time range is determined on the basis of the reference speed. According to the method and the device, the information of the static barrier and the dynamic barrier in the driving scene is fully considered, the expected interactive decision is determined, the longitudinal feasible space diagram of the target vehicle is further constructed, the reference speed which is closest to the final execution of the target vehicle is obtained based on the longitudinal feasible space diagram, and the reference speed is used for evaluating the optimal path, so that the optimal path can be obtained more accurately, the reasonability and the reliability of a driving path planning result are improved, the driving efficiency is improved, and the driving experience is improved.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the description below are some embodiments of the present application, and those skilled in the art can obtain other drawings based on the drawings without inventive labor.

Fig. 1 is a schematic view of an application scenario provided in an embodiment of the present application;

fig. 2 is a flowchart of a driving trajectory planning method according to an embodiment of the present application;

FIG. 3 is a flowchart of a driving trajectory planning method according to another embodiment of the present application;

fig. 4 is a schematic structural diagram of a driving path planning apparatus according to an embodiment of the present application;

fig. 5 is a schematic structural diagram of an electronic device provided in the present application.

Detailed Description

In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.

In the technical scheme of the application, the collection, storage, use, processing, transmission, provision, disclosure and other processing of the related information such as financial data or user data and the like all accord with the regulations of related laws and regulations and do not violate the good custom of the public order.

At present, in a driving path planning scheme of automatic driving, a path and speed decoupling planning mode is adopted, and a set of safe and flexible path planning strategy needs to fully consider the influence of static obstacles and dynamic obstacles in a driving scene, namely whether a self-vehicle (namely a current automatic driving vehicle) and the obstacles are simultaneously present at the same position at a certain time in the future. Therefore, when performing alternative path evaluation, it is necessary to provide corresponding speed and time information for discrete path points on the path to analyze whether there is a potential collision risk with an obstacle. For the sampling-based path optimization method, the unreasonable reference speed can cause evaluation deviation, so that the optimal driving path cannot be selected, and the driving safety and the driving efficiency are influenced.

Based on the above problems, the present application provides a driving trajectory planning method, device, equipment, and storage medium, which perform optimization of a reference speed by fully considering information of static obstacles and dynamic obstacles in a driving scene, based on behavior and trajectory prediction results for the obstacles, in combination with execution capability of a target vehicle, on the premise of performing reasonable interaction with the obstacles, thereby obtaining a reference speed closest to the final execution of the target vehicle, and using the reference speed to perform optimal path evaluation, so that an optimal path can be obtained more accurately, and reasonability and reliability of a driving trajectory planning result are improved.

First, an application scenario of the solution provided in the present application will be described below.

Fig. 1 is a schematic view of an application scenario provided in an embodiment of the present application. As shown in fig. 1, in the present application scenario, an autonomous vehicle 101 travels on a road 102 according to a planned driving trajectory. For a specific implementation process of how the automatic driving vehicle 101 obtains the planned driving track, reference may be made to the schemes of the following embodiments.

It should be noted that fig. 1 is only a schematic diagram of an application scenario provided in this embodiment, and this embodiment of the present application does not limit the devices included in fig. 1, and also does not limit the positional relationship between the devices in fig. 1.

Next, a driving path planning method is described by a specific embodiment.

Fig. 2 is a flowchart of a driving trajectory planning method according to an embodiment of the present application. The method of the embodiment of the application can be applied to electronic equipment, and the electronic equipment can be a server or a server cluster and the like. As shown in fig. 2, the method of the embodiment of the present application includes:

s201, determining an expected interaction decision of the target vehicle and the obstacle within a future preset time range.

The expectation interactive decision is used for representing the interactive behavior of the target vehicle and the obstacle and an interactive time window corresponding to the interactive behavior, and the interactive behavior comprises that the target vehicle actively exceeds the obstacle, the target vehicle actively gives way to the obstacle and the target vehicle actively ignores the obstacle.

In the embodiment of the present application, the future preset time range is, for example, 10s in the future. Based on the prediction results of the behavior and the trajectory of the obstacles given by the preset prediction module, and by considering the information of public road driving rules, right of way, speed limit and the like, a preliminary interactive decision is made for each obstacle potentially interacting with the target vehicle, namely, an expected interactive decision of the target vehicle and the obstacle in a future preset time range is determined. Specifically, the interaction behavior of the target vehicle with the obstacle may also be referred to as an interaction attribute, which is defined as three types: the target vehicle needs to actively pass over the obstacle (denoted as pass _ type), the target vehicle needs to actively yield the obstacle (denoted as yield _ type), and the obstacle is actively ignored (denoted as ignore _ type) because it does not create a potential risk to the target vehicle. The three interactive attributes can be understood as the decision of the behavior level.

For an obstacle interacting with a target vehicle, in addition to a behavior level decision, an interaction time window corresponding to the behavior level decision, that is, a corresponding overtaking or yielding time window, needs to be given, and a value range of the interaction time window may be represented as [ t _ min, t _ max ], where t _ min represents a minimum start time of the interaction time window, and t _ max represents a maximum end time of the interaction time window. For example, if it is expected that overtaking of an obstacle whose identity document (id) is represented by obs _0 is realized in 3s to 5s, the interaction attribute of the obstacle is marked as pass _ type, and the corresponding interaction time window is [3,5 ]; for example, if yielding to an obstacle with id represented by obs _1 is expected to be realized in 8s to 10s, marking the interaction attribute of the obstacle as yield _ type and the corresponding interaction time window as [8,10 ]; if an obstacle with id obs _2 does not enter the lane where the target vehicle is located within 10s, or there is no potential interaction, the interaction attribute of the obstacle is labeled as ignore _ type, and the corresponding interaction time window is [0,10 ]. And (4) sequentially carrying out interactive analysis on all obstacles in the driving scene of the target vehicle to obtain an expected interactive decision of the target vehicle and each obstacle.

S202, constructing a longitudinal feasible space diagram of the target vehicle corresponding to a future preset time range based on the expected interaction decision.

The longitudinal feasible space map is used for representing a longitudinal feasible range and longitudinal speed constraint information corresponding to discrete moments of the target vehicle in a future preset time range.

In this step, after the expected interaction decision is obtained, a longitudinal feasible space map of the target vehicle corresponding to a future preset time range may be constructed based on the expected interaction decision, so as to determine a longitudinal feasible range and longitudinal speed constraint information corresponding to discrete moments of the target vehicle within the future preset time range. Illustratively, the abscissa of the longitudinal feasible space map is, for example, discrete time instants within a preset time range in the future, and the ordinate of the longitudinal feasible space map is, for example, a longitudinal feasible range corresponding to each discrete time instant, and there is corresponding longitudinal speed constraint information within the longitudinal feasible range. For how to construct the longitudinal feasible space map corresponding to the future preset time range of the target vehicle based on the expected interaction decision, reference may be made to the subsequent embodiments, which are not described herein again.

S203, obtaining the reference speed of the target vehicle based on the longitudinal feasible space diagram and the first preset cost function.

Wherein the first preset cost function is determined based on the driving efficiency and the comfort of the target vehicle.

For example, a driving speed that is too low may cause a problem of low driving efficiency, while a driving speed that is too high may easily affect the driving experience and may even cause vehicle instability when it is severe, and therefore, when defining the first preset cost function (denoted as cost _ function), it is necessary to balance the driving efficiency and the comfort of the target vehicle, that is: from the viewpoint of traveling efficiency, it is desirable that the traveling distance tends to be as far as possible; for comfort reasons, it is desirable that the ride be smoother, with acceleration and jerk (i.e., the increment of acceleration) tending to be as small as possible. For specific definition of the first preset cost function, reference may be made to the following embodiments, which are not described herein again. In this step, after the longitudinal feasible space map is obtained, the reference speed of the target vehicle may be obtained based on the longitudinal feasible space map and the first preset cost function. As to how to obtain the reference speed of the target vehicle based on the longitudinal feasible space diagram and the first preset cost function, reference may be made to the subsequent embodiments, which are not described herein again.

And S204, determining a target track of the target vehicle in a future preset time range based on the reference speed.

In this step, after the reference speed is obtained, a target trajectory of the target vehicle within a future preset time range may be determined based on the reference speed. For how to determine the target trajectory of the target vehicle within the future preset time range based on the reference speed, reference may be made to the following embodiments, which are not described herein again.

After the target track of the target vehicle in the future preset time range is determined, the optimal track of the target vehicle in the future preset time range is determined, and the target vehicle can be controlled to run according to the planned optimal track.

According to the driving track planning method provided by the embodiment of the application, the expected interaction decision of the target vehicle and the obstacle in the future preset time range is determined, the longitudinal feasible space diagram of the target vehicle corresponding to the future preset time range is constructed on the basis of the expected interaction decision, the reference speed of the target vehicle is obtained on the basis of the longitudinal feasible space diagram and the first preset cost function, and the target track of the target vehicle in the future preset time range is determined on the basis of the reference speed. According to the method and the device, the information of the static barrier and the dynamic barrier in the driving scene is fully considered, the expected interactive decision is determined, the longitudinal feasible space diagram of the target vehicle is further constructed, the reference speed which is closest to the final execution of the target vehicle is obtained based on the longitudinal feasible space diagram, and the reference speed is used for evaluating the optimal path, so that the optimal path can be obtained more accurately, the reasonability and the reliability of a driving path planning result are improved, the driving efficiency is improved, and the driving experience is improved.

Fig. 3 is a flowchart of a driving trajectory planning method according to another embodiment of the present application. On the basis of the above embodiments, the embodiments of the present application further explain how to plan the driving trajectory. As shown in fig. 3, the method of the embodiment of the present application may include:

s301, determining the expected interaction decision of the target vehicle and the obstacle in the future preset time range.

For a detailed description of this step, reference may be made to the description related to S201 in the embodiment shown in fig. 2, and details are not described here.

In this embodiment of the application, the step S202 in fig. 2 may further include the following two steps S302 and S303:

s302, determining a target longitudinal feasible minimum distance, a target longitudinal feasible maximum distance, a target longitudinal speed minimum value and a target longitudinal speed maximum value corresponding to discrete time of the target vehicle in a future preset time range based on the expected interaction decision.

In this step, the expectation-interaction decision may provide a reference for the construction of the longitudinal feasible space of the target vehicle at each discrete time within the future preset time range. After the expected interaction decision of the target vehicle and the obstacle in the future preset time range is obtained, the minimum distance of the target vehicle in the longitudinal direction, the maximum distance of the target vehicle in the longitudinal direction, the minimum value of the target vehicle in the longitudinal direction and the maximum value of the target vehicle in the longitudinal direction can be determined corresponding to the discrete time of the target vehicle in the future preset time range based on the expected interaction decision.

Further, optionally, determining, based on the desired interactive decision, a target longitudinally feasible minimum distance, a target longitudinally feasible maximum distance, a target longitudinal speed minimum value, and a target longitudinal speed maximum value corresponding to the target vehicle at the discrete time within the future preset time range may include: determining an initial longitudinally feasible maximum distance, an initial longitudinally feasible minimum distance, a minimum value of an initial longitudinal speed, and a maximum value of the initial longitudinal speed of the target vehicle; aiming at each obstacle corresponding to a discrete moment in a future preset time range, executing the following operations until each obstacle is traversed: based on the expected interactive decision, if the target obstacle needing to be actively yielded by the target vehicle is determined, determining the maximum feasible distance of the target longitudinal direction of the target vehicle according to the space occupied by the target obstacle in the longitudinal direction and the transverse direction, the preset yielding safety distance and the initial maximum feasible distance of the longitudinal direction, determining the maximum value of the target longitudinal speed according to the speed of the target obstacle, determining the maximum feasible distance of the target longitudinal direction as the new initial maximum feasible distance of the longitudinal direction, and determining the maximum value of the target longitudinal speed 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 is required to actively exceed the target obstacle, determining a target longitudinal feasible minimum distance of the target vehicle according to a space occupied by the target obstacle in the longitudinal and transverse directions, a preset overtaking safety distance and an initial longitudinal feasible minimum distance, determining a minimum value of a target longitudinal speed according to the speed of the target obstacle, determining the target longitudinal feasible minimum distance as a new initial longitudinal feasible minimum distance, and determining the minimum value of the target longitudinal speed as a new initial longitudinal speed minimum value.

Wherein, optionally, determining the initial longitudinal feasible maximum distance of the target vehicle may comprise: determining the maximum feasible distance of the target vehicle within a future preset time range based on the driving scene speed limit of the target vehicle; determining the maximum speed corresponding to the maximum curvature according to the maximum curvature of a running path of the target vehicle in a future preset time range and the corresponding relation between the preset curvature and the speed constraint; obtaining a target maximum feasible distance according to the maximum speed and the maximum feasible distance; and determining the maximum feasible distance of the target as the initial longitudinal feasible maximum distance.

Illustratively, the future preset time range is, for example, 10s in the future, and the preset curvature-speed constraint correspondence is, for example, a curvature-speed constraint table calibrated offline in advance. Illustratively, the maximum feasible distance (denoted as max _ reliable _ length) of the target vehicle in a non-interference state, namely the farthest feasible distance of the target vehicle in the future for 10s under physical constraint is obtained, and the calculation process fully considers the driving scene speed limit of the target vehicle and the limit of the lane shape on the driving speed. Firstly, considering the driving scene speed limit of a target vehicle, initializing max _ replaceable _ length according to the driving scene speed limit of the target vehicle, and obtaining an initial value of the max _ replaceable _ length; secondly, considering driving stability, the maximum speed of the target vehicle is affected by the shape of the lane, for example, if the target vehicle drives into a quarter turn road section, corresponding deceleration needs to be performed according to the curvature of the path, the curvature-speed constraint table can be queried to obtain a corresponding maximum speed based on the curvature-speed constraint table calibrated offline and the maximum curvature of a future section of the path, and max _ dry _ length is updated according to the maximum speed and the initial value of max _ dry _ length, that is, the maximum feasible target distance is obtained.

And determining the target maximum feasible distance as an initial longitudinal feasible maximum distance, wherein the initial longitudinal feasible minimum distance is 0 for example, the minimum value of the initial longitudinal speed is 0 for example, and the maximum value of the initial longitudinal speed is the driving scene speed limit of the target vehicle for example. After the initial longitudinally feasible maximum distance, the initial longitudinally feasible minimum distance, the minimum value of the initial longitudinal velocity and the maximum value of the initial longitudinal velocity of the target vehicle are obtained, carrying out discretization processing on the future preset time range at equal time intervals to obtain corresponding discrete time, comprehensively considering the reasonable interaction between the target vehicle and each obstacle at each discrete time, to obtain boundary values for a target longitudinal feasible distance (i.e., longitudinal feasible range) for the target vehicle, including a target longitudinal feasible minimum distance (denoted as s _ ego _ min) and a target longitudinal feasible maximum distance (denoted as s _ ego _ max), wherein, the boundary value of the target longitudinal feasible distance corresponding to each discrete moment needs to satisfy the basic constraint, that is, s _ ego _ min > is 0, and s _ ego _ max < ═ max _ replaceable _ length.

Specifically, for each obstacle corresponding to each discrete time within a preset time range in the future, taking discrete time t0 as an example, for each obstacle (denoted as obs _ i) in a driving scene of a target vehicle, first, information of a space (denoted as sdoundary) occupied by the obstacle in a longitudinal direction and a transverse direction of the obstacle in an obstacle trajectory prediction result corresponding to t0 is queried, sdoundary is a rectangular box used for representing a lane position space occupied by the obstacle at a certain discrete time, and a range of the space occupied by the target obstacle in the longitudinal direction is as follows: [ obs _ i _ s _ min, obs _ i _ s _ max ], wherein obs _ i _ s _ min represents a minimum value of a space occupied by the target obstacle in the longitudinal direction, and obs _ i _ s _ max represents a maximum value of the space occupied by the target obstacle in the longitudinal direction; the extent of the space laterally occupied by the target obstacle is: [ obs _ i _ d _ min, obs _ i _ d _ max ], wherein obs _ i _ d _ min represents the minimum value of the space occupied by the target obstacle in the lateral direction, obs _ i _ d _ max represents the maximum value of the space occupied by the target obstacle in the lateral direction, and the non-passable area of the target vehicle is determined according to sdbondary information. Next, consider a desired interaction decision of the target vehicle with the obstacle:

if a yielding obstacle is needed, in order to ensure that the target vehicle has enough reaction time and space in an emergency situation, the longitudinal feasible maximum distance of the target vehicle is considered to be a reserved yielding safe distance (denoted as lon _ buffer) on the basis of obs _ i _ s _ min occupied by the obstacle, and the longitudinal feasible maximum distance of the target vehicle is related to the speed of the obstacle, so that the target longitudinal feasible maximum distance (denoted as s _ ego _ max _ t0) of the target vehicle corresponding to t0 is updated to the smaller value of s _ ego _ max _ t0 and (obs _ i _ s _ min _ lon _ buffer); if the obstacle is a closer obstacle to the target vehicle, the maximum value of the target longitudinal speed of the target vehicle (denoted as v _ ego _ max _ t0) needs to be updated to the speed of the obstacle;

if an obstacle needs to be transcendered, considering a longitudinal safe distance reserved for overtaking on the basis of the obs _ i _ s _ max occupied by the obstacle, which is related to the speed of the obstacle, so that the target longitudinal minimum feasible distance (denoted as s _ ego _ min _ t0) of the corresponding target vehicle when interacting with the obstacle obs _ i is updated to the larger of s _ ego _ min _ t0 and (obs _ i _ s _ max + lon _ buffer); if the obstacle is a close obstacle to the target vehicle, the minimum value of the target longitudinal speed of the target vehicle (denoted as v _ ego _ min _ t0) needs to be updated to the speed of the obstacle.

Traversing all the obstacles which need to yield and exceed, the boundary value of the longitudinal feasible range of the target vehicle corresponding to t0 can be updated, the boundary value comprises the target longitudinal feasible minimum distance and the target longitudinal feasible maximum distance, i.e. the longitudinal feasible range of the target vehicle is [ s _ ego _ min _ t0, s _ ego _ max _ t0], and the obstacle id corresponding to the boundary value and the longitudinal speed constraint information of the target vehicle, the longitudinal speed constraint information comprises the minimum value of the target longitudinal speed and the maximum value of the target longitudinal speed, i.e. the longitudinal speed constraint range of the target vehicle is [ v _ ego _ min _ t0, v _ ego _ max _ t0 ].

Referring to the execution step at the discrete time t0, the longitudinal feasible range (corresponding to the target longitudinal feasible minimum distance and the target longitudinal feasible maximum distance) and the longitudinal speed constraint information (corresponding to the minimum value of the target longitudinal speed and the maximum value of the target longitudinal speed) of the target vehicle from 0s to 10s in the future can be obtained.

S303, constructing a longitudinal feasible space map of the target vehicle corresponding to a future preset time range according to 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.

In this step, illustratively, the future preset time range is, for example, 10s in the future, and after the target longitudinally feasible minimum distance, the target longitudinally feasible maximum distance, the minimum value of the target longitudinal speed and the maximum value of the target longitudinal speed are obtained, according to 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 map corresponding to the target vehicle within 10s in the future can be constructed, to describe the minimum longitudinal feasible distance and the maximum longitudinal feasible distance of the target vehicle at each discrete time in the future 10s, namely, the longitudinal feasible range corresponding to the target vehicle at each discrete time in the future 10s, and the longitudinal feasible range has the corresponding minimum value of the target longitudinal speed and the maximum value of the target longitudinal speed, namely, the longitudinal speed constraint information.

S304, aiming at the target longitudinal feasible minimum distance and the target longitudinal feasible maximum distance corresponding to the discrete time within the future preset time range, obtaining the updated target longitudinal feasible minimum distance corresponding to the current discrete time according to the target longitudinal feasible minimum distance corresponding to the previous discrete time and the target longitudinal feasible minimum distance corresponding to the current discrete time, and obtaining the updated target longitudinal feasible maximum distance corresponding to the current discrete time according to the target longitudinal feasible minimum distance and the target longitudinal feasible maximum distance corresponding to the current discrete time.

This step can be understood as a plausibility check of the boundary values of the longitudinal feasible range corresponding to the discrete time within the future preset time range. For example, because the trajectory planning is not a reverse trajectory planning, it needs to be ensured that the longitudinally feasible minimum distance of the target corresponding to the current discrete time is not less than the longitudinally feasible minimum distance of the target corresponding to the adjacent previous discrete time, and the driving process is constrained by the maximum feasible distance of the target.

In this step, further, optionally, obtaining an updated target longitudinally feasible minimum distance corresponding to the current discrete time according to the target longitudinally feasible minimum distance corresponding to the previous discrete time and the target longitudinally feasible minimum distance corresponding to the current discrete time, and obtaining an updated target longitudinally feasible maximum distance corresponding to the current discrete time according to the target maximum feasible distance and the target longitudinally feasible maximum distance corresponding to the current discrete time, may include: and aiming at the discrete time within the future preset time range, if the updated longitudinal feasible maximum distance of the target is determined to be smaller than the updated longitudinal feasible minimum distance of the target, updating the interaction behavior of the corresponding barrier and the target vehicle, and reconstructing a longitudinal feasible space diagram of the target vehicle corresponding to the future preset time range.

In the above process of checking the rationality of the boundary values of the longitudinal feasible range, if a discrete time S _ ego _ max < S _ ego _ min indicates that the expected interaction decision needs to be satisfied, that is, if exceeding a certain obstacle results in an obstacle that cannot be encountered next, considering the highest safety, the expected interaction decision of the obstacle of pass _ type needs to be modified into yield _ type, and the above steps S302, S303, and S304 are repeatedly executed until the boundary values of the longitudinal feasible ranges corresponding to all the discrete times are reasonable values, and a longitudinal feasible space diagram of the target vehicle corresponding to a future preset time range is reconstructed.

S305, obtaining an updated longitudinal feasible space map according to the updated minimum distance of the target longitudinal feasible and the updated maximum distance of the target longitudinal feasible.

In this step, after the updated target longitudinal feasible minimum distance and the updated target longitudinal feasible maximum distance corresponding to each discrete time within the future preset time range are obtained, the updated longitudinal feasible space map can be obtained according to the updated target longitudinal feasible minimum distance and the updated target longitudinal feasible maximum distance.

In this embodiment of the application, the step S203 in fig. 2 may further include the following four steps S306 to S309:

and S306, based on the longitudinal feasible space diagram, obtaining updated longitudinal feasible range and longitudinal speed constraint information according to the maximum acceleration capacity and the maximum deceleration capacity of the target vehicle, the curvature corresponding to the reference line of the path in the longitudinal feasible range and the corresponding relation of the preset curvature and the speed constraint.

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. Illustratively, the following three constraints are first determined: (1) considering the running experience and the continuity of a controller control signal (such as a control signal corresponding to the opening degree of an accelerator pedal) of a target vehicle, it is required to ensure the continuity of the initial state (i.e. speed and acceleration) of the target vehicle, i.e. the planned initial speed and the initial acceleration at a starting point are initialized according to the current execution state of the target vehicle; (2) determining constraint information of an executable speed at a next discrete time and constraint information of an executable distance at the next discrete time, taking into account execution capabilities of the target vehicle, such as a maximum acceleration capability and a maximum deceleration capability of the target vehicle; (3) considering the shape of a lane, for example, an excessively high vehicle speed during turning easily causes instability of a target vehicle or reduces riding experience, and therefore, it is necessary to add a constraint 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 satisfies 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, the corresponding curvature information on the reference line (reference line) is queried, and the maximum speed constraint at the position is obtained by querying the preset curvature and speed constraint correspondence.

And based on the longitudinal feasible range and the longitudinal speed constraint information corresponding to the discrete time of the target vehicle in the longitudinal feasible space diagram in the future preset time range, and according to the three constraint conditions, obtaining the updated longitudinal feasible range and longitudinal speed constraint information.

And S307, obtaining an updated longitudinal feasible space map according to the updated longitudinal feasible range and longitudinal speed constraint information.

In this step, after the updated longitudinal feasible range and longitudinal speed constraint information corresponding to each discrete time within the future preset time range are obtained, the updated longitudinal feasible space map can be obtained according to the updated longitudinal feasible range and longitudinal speed constraint information.

And S308, acquiring a first target position of the target vehicle corresponding to the discrete moment based on the updated longitudinal feasible space diagram and the first preset cost function.

Illustratively, the first preset cost function cost _ function is defined as:

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

the coefficient _ vel, the coefficient _ acc and the coefficient _ jerk respectively represent a weight factor of the speed, a weight factor of the acceleration and a weight factor of the jerk of the first preset cost function, and all the weight factors are positive values; veli represents the speed of the ith discrete time sampling point, and max _ veli represents the maximum speed determined by the ith discrete time according to the maximum curvature of the path; acc _ i represents the acceleration of the ith discrete time sampling point; jerk _ i represents the jerk of the ith discrete time sample point.

In this step, after the updated longitudinal feasible space map is obtained, the first target position of the target vehicle corresponding to each discrete time may be obtained based on the updated longitudinal feasible space map and the first preset cost function, which may be understood as a dynamic programming solution process in a limited space, and finally a discrete sequence satisfying a boundary value of the longitudinal feasible range and increasing according to time may be searched for and obtained and represented as an s-t discrete sequence, where s represents the first target position of the target vehicle corresponding to the discrete time t.

S309, fitting the first target position to obtain the reference speed of the target vehicle.

In this step, after the first target position is obtained, fitting processing may be performed on the first target position to obtain a reference speed of the target vehicle. Illustratively, fitting the S-t discrete sequence points obtained in the step S308 to obtain a reference speed of the target vehicle, which is specifically described as a form of S-t curve (curve).

In this embodiment of the application, the step S204 in fig. 2 may further include the following two steps S310 and S311:

s310, obtaining a target candidate route corresponding to a future preset time range according to at least one preset candidate route corresponding to the target vehicle and the reference speed.

The preset candidate path is obtained by longitudinally and transversely sampling the future driving path of the target vehicle respectively.

Exemplarily, taking the current position of the target vehicle as a longitudinal starting position, sampling the longitudinal position at equal intervals along the direction of a reference line, inquiring the corresponding lane width as a transverse sampling space for each sampling point (denoted as ss), and dividing the transverse sampling space at equal intervals to obtain discrete points (ss, dd), wherein dd denotes the transverse position of the target vehicle relative to the reference line. And smoothly connecting the starting point and each sampling point by using a spline curve to generate a plurality of initial candidate paths, wherein the initial candidate paths are described in a ss-dd curve form, namely, the preset candidate paths corresponding to the target vehicle are obtained. After the preset candidate path corresponding to the target vehicle is obtained, the target candidate path corresponding to the future preset time range can be obtained according to the preset candidate path corresponding to the target vehicle and the reference speed.

Further, optionally, obtaining a target candidate route corresponding to a future preset time range according to at least one preset candidate route corresponding to the target vehicle and the reference speed, includes: aiming at each preset candidate path, acquiring a longitudinal advancing distance corresponding to 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 advance distance and the preset candidate path; and carrying out coordinate transformation on the second target position to obtain a target candidate path.

Illustratively, for each preset candidate path, at equal time intervals, that is, for each discrete time, the longitudinal advance distance of the target vehicle corresponding to the discrete time is obtained by querying on the reference speed curve s-t curve, the longitudinal advance distance is represented by s1 for example, and the transverse position of the target vehicle relative to the reference line is obtained by interpolating through s1 on ss-dd curve, the transverse position is represented by dd1 for example, and the spatial coordinates (x, y) of the target vehicle can be obtained by performing coordinate transformation on the (s1, dd1) position information of the discrete time. In the above manner, a target candidate path (which may also be referred to as a target candidate trajectory) of a future preset time range (for example, 10s) may be derived.

S311, determining a target track of the target vehicle in a future preset time range according to the target candidate path and the second preset cost function.

Wherein the second preset cost function is determined based on the driving safety, comfort and stability of the target vehicle.

Exemplarily, after a target candidate path (i.e., a target candidate trajectory) is obtained, for each target candidate trajectory, when a second preset cost function (denoted as cost _ function _2) is defined, a potential collision risk of the target candidate trajectory, that is, a probability that a target vehicle and an obstacle appear at the same position at the same time, needs to be comprehensively considered for determining a driving safety degree of the target vehicle; comfort of the trajectory, i.e. acceleration of the trajectory and its rate of change; the stability of the trajectory, i.e., the maximum yaw rate of the target candidate trajectory. Thus, the second preset cost function is defined as:

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

wherein, the trajectory _ risk _ value, trajectory _ max _ jerk, trajectory _ max _ acc, and trajectory _ max _ yaw _ rate respectively represent a collision risk, a maximum jerk, a maximum acceleration, and a maximum yaw rate of the entire target candidate trajectory; the coefficient _ risk, the coefficient _ comfort, and the coefficient _ stability respectively represent a weight factor of potential collision risk, a weight factor of comfort, and a weight factor of stability of the target candidate trajectory, and are all positive values.

In this step, after the target candidate path is obtained, the target trajectory of the target vehicle within a future preset time range may be determined according to the target candidate path and the second preset cost function, that is, an optimal trajectory is finally selected and determined to be issued to the control module of the target vehicle, so as to control the target vehicle to travel according to the planned optimal trajectory.

According to the driving track planning method provided by the embodiment of the application, the expected interactive decision of the target vehicle and the barrier in the future preset time range is determined; determining a target longitudinal feasible minimum distance, a target longitudinal feasible maximum distance, a target longitudinal speed minimum value and a target longitudinal speed maximum value corresponding to the target vehicle at the discrete time within the future preset time range based on the expected interactive decision, and constructing a longitudinal feasible space map of the target vehicle corresponding to the future preset time range according to the target longitudinal feasible minimum distance, the target longitudinal feasible maximum distance, the target longitudinal speed minimum value and the target longitudinal speed maximum value; aiming at a target longitudinal feasible minimum distance and a target longitudinal feasible maximum distance corresponding to a discrete moment in a future preset time range, obtaining an updated target longitudinal feasible minimum distance corresponding to the current discrete moment according to a target longitudinal feasible minimum distance corresponding to a previous discrete moment and a 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 according to a target maximum feasible distance and a target longitudinal feasible maximum distance corresponding to the current discrete moment; obtaining an updated longitudinal feasible space map according to the updated minimum distance of the target longitudinal feasible and the updated maximum distance of the target longitudinal feasible; based on the longitudinal feasible space diagram, obtaining updated longitudinal feasible range and longitudinal speed constraint information according to the maximum acceleration capacity and the maximum deceleration capacity of the target vehicle, the curvature corresponding to the reference line of the path in the longitudinal feasible range and the corresponding relation between the preset curvature and the speed constraint; obtaining an updated longitudinal feasible space map according to the updated longitudinal feasible range and longitudinal speed constraint information; acquiring a first target position of the target vehicle corresponding to the discrete moment based on the updated longitudinal feasible space diagram and a first preset cost function; fitting the first target position to obtain the reference speed of the target vehicle; obtaining 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 the reference speed; and determining a target track of the target vehicle in a future preset time range according to the target candidate path and the second preset cost function. According to the method and the device, the information of the static barrier and the dynamic barrier in the driving scene is fully considered, the expected interaction decision is determined, the longitudinal feasible space diagram of the target vehicle is further constructed, the reference speed which is closest to the final execution of the target vehicle is obtained on the premise of reasonably interacting with the barrier based on the longitudinal feasible space diagram and by combining the execution capacity of the target vehicle, and the reference speed is used for evaluating the optimal path, so that the optimal path can be obtained more accurately, the reasonability and the reliability of a driving path planning result are improved, the driving efficiency is improved, and the driving experience is improved.

The following are embodiments of the apparatus of the present application that may be used to perform embodiments of the method of the present application. For details which are not disclosed in the embodiments of the apparatus of the present application, reference is made to the embodiments of the method of the present application.

Fig. 4 is a schematic structural diagram of a driving path planning apparatus according to an embodiment of the present application, and as shown in fig. 4, a driving path planning apparatus 400 according to an embodiment of the present application includes: a determination module 401, a construction module 402, an acquisition module 403 and a processing module 404. Wherein:

the determining module 401 is configured to determine an expected interaction decision of the target vehicle with the obstacle within a future preset time range, where the expected interaction decision is used to represent an interaction behavior of the target vehicle with the obstacle and an interaction time window corresponding to the interaction behavior, and the interaction behavior includes that the target vehicle actively exceeds the obstacle, the target vehicle actively gives way to the obstacle, and the target vehicle actively ignores the obstacle.

The building module 402 is configured to build a longitudinal feasible space map corresponding to the target vehicle in a future preset time range based on the expected interaction decision, where the longitudinal feasible space map is used to represent a longitudinal feasible range corresponding to a discrete time of the target vehicle in the future preset time range and longitudinal speed constraint information.

An obtaining module 403, 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 the comfort level of the target vehicle.

And a processing module 404, configured to determine a target trajectory of the target vehicle within a future preset time range based on the reference speed.

In some embodiments, the building module 402 may be specifically configured to: based on the expected interactive decision, determining a target longitudinal feasible minimum distance, a target longitudinal feasible maximum distance, a target longitudinal speed minimum value and a target longitudinal speed maximum value corresponding to discrete time of the target vehicle in a future preset time range; and constructing a longitudinal feasible space diagram of the target vehicle corresponding to a future preset time range according to 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.

Optionally, when the building module 402 is configured to determine, based on the desired interaction decision, a target longitudinally feasible minimum distance, a target longitudinally feasible maximum distance, a target longitudinal speed minimum value, and a target longitudinal speed maximum value corresponding to a discrete time of the target vehicle within a future preset time range, the building module may be specifically configured to: determining an initial longitudinally feasible maximum distance, an initial longitudinally feasible minimum distance, a minimum value of an initial longitudinal speed, and a maximum value of the initial longitudinal speed of the target vehicle; aiming at each obstacle corresponding to a discrete moment in a future preset time range, executing the following operations until each obstacle is traversed: based on the expected interactive decision, if the target obstacle needing to be actively yielded by the target vehicle is determined, determining the maximum feasible distance of the target longitudinal direction of the target vehicle according to the space occupied by the target obstacle in the longitudinal direction and the transverse direction, the preset yielding safety distance and the initial maximum feasible distance of the longitudinal direction, determining the maximum value of the target longitudinal speed according to the speed of the target obstacle, determining the maximum feasible distance of the target longitudinal direction as the new initial maximum feasible distance of the longitudinal direction, and determining the maximum value of the target longitudinal speed 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 is required to actively exceed the target obstacle, determining a target longitudinal feasible minimum distance of the target vehicle according to a space occupied by the target obstacle in the longitudinal direction and the transverse direction, a preset overtaking safety distance and an initial longitudinal feasible minimum distance, determining a minimum value of a target longitudinal speed according to the speed of the target obstacle, determining the target longitudinal feasible minimum distance as a new initial longitudinal feasible minimum distance, and determining the minimum value of the target longitudinal speed as a new initial longitudinal speed minimum value.

Optionally, the building module 402, when configured to determine the initial longitudinally feasible maximum distance of the target vehicle, may be specifically configured to: determining the maximum feasible distance of the target vehicle within a future preset time range based on the driving scene speed limit of the target vehicle; determining the maximum speed corresponding to the maximum curvature according to the maximum curvature of a running path of the target vehicle in a future preset time range and the corresponding relation between the preset curvature and the speed constraint; obtaining a target maximum feasible distance according to the maximum speed and the maximum feasible distance; and determining the maximum feasible distance of the target as the initial longitudinal feasible maximum distance.

In some embodiments, the driving trajectory planning apparatus further includes an updating module 405, configured to, after the construction module 402 constructs a longitudinal feasible space map of the target vehicle corresponding to a future preset time range, obtain, for a target longitudinal feasible minimum distance and a target longitudinal feasible maximum distance corresponding to a discrete time within the future preset time range, an updated target longitudinal feasible minimum distance corresponding to a current discrete time according to a target longitudinal feasible minimum distance corresponding to a previous discrete time and a target longitudinal feasible minimum distance corresponding to a current discrete time, and obtain an updated target longitudinal feasible maximum distance corresponding to a current discrete time according to a target maximum feasible distance and a target longitudinal feasible maximum distance corresponding to a current discrete time; and obtaining an updated longitudinal feasible space map according to the updated minimum distance of the target longitudinal feasible and the updated maximum distance of the target longitudinal feasible.

Optionally, the updating module 405 may be specifically configured to, when the updating module is configured to obtain the updated target longitudinally feasible minimum distance corresponding to the current discrete time according to the target longitudinally feasible minimum distance corresponding to the previous discrete time and the target longitudinally feasible minimum distance corresponding to the current discrete time, and obtain the updated target longitudinally feasible maximum distance corresponding to the current discrete time according to the target maximum feasible distance and the target longitudinally feasible maximum distance corresponding to the current discrete time: and aiming at the discrete time within the future preset time range, if the updated target longitudinal feasible maximum distance is determined to be smaller than the updated target longitudinal feasible minimum distance, updating the interaction behavior of the corresponding barrier and the target vehicle, and reconstructing a longitudinal feasible space diagram of the target vehicle corresponding to the future preset time range.

In some embodiments, the obtaining module 403 may be specifically configured to: based on the longitudinal feasible space diagram, obtaining updated longitudinal feasible range and longitudinal speed constraint information according to the maximum acceleration capacity and the maximum deceleration capacity of the target vehicle, the curvature corresponding to the reference line of the path in the longitudinal feasible range and the corresponding relation between the preset curvature and the speed constraint; obtaining an updated longitudinal feasible space map according to the updated longitudinal feasible range and longitudinal speed constraint information; acquiring a first target position of the target vehicle corresponding to the discrete moment based on the updated longitudinal feasible space diagram and a first preset cost function; and fitting the first target position to obtain the reference speed of the target vehicle.

In some embodiments, the processing module 404 may be specifically configured to: obtaining 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, wherein the preset candidate path is obtained by longitudinally and transversely sampling the future driving path of the target vehicle; and determining a target track of the target vehicle in a future preset time range according to the target candidate path and a second preset cost function, wherein the second preset cost function is determined based on the driving safety degree, the comfort degree and the stability of the target vehicle.

Optionally, when the processing module 404 is configured to obtain the target candidate route corresponding to the future preset time range according to at least one preset candidate route corresponding to the target vehicle and the reference speed, the processing module may specifically be configured to: aiming at each preset candidate path, acquiring a longitudinal advancing distance corresponding to 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 advance distance and the preset candidate path; and carrying out coordinate transformation on the second target position to obtain a target candidate path.

The apparatus of this embodiment may be configured to implement the technical solution of any one of the above-mentioned method embodiments, and the implementation principle and the technical effect are similar, which are not described herein again.

Fig. 5 is a schematic structural diagram of an electronic device provided in the present application. As shown in fig. 5, the electronic device 500 may include: at least one processor 501 and memory 502.

The memory 502 is used for storing programs. In particular, the program may include program code including computer operating instructions.

Memory 502 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.

The processor 501 is configured to execute computer-executable instructions stored in the memory 502 to implement the driving path planning method described in the foregoing method embodiments. The processor 501 may be a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement the embodiments of the present Application. Specifically, when the driving trajectory planning method described in the foregoing method embodiment is implemented, the electronic device may be, for example, an electronic device with a processing function, such as a terminal and a server. When implementing the driving trajectory planning method described in the foregoing method embodiment, the electronic device may be, for example, an electronic control unit on a vehicle.

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 connected to each other through a bus and perform communication with each other. The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. Buses may be classified as address buses, data buses, control buses, etc., but do not represent only one bus or type of bus.

Optionally, in a specific implementation, if the communication interface 503, the memory 502, and the processor 501 are integrated into a chip, the communication interface 503, the memory 502, and the processor 501 may complete communication through an internal interface.

The present application also provides a computer-readable storage medium, which may include: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, are specifically, the computer-readable storage medium stores program instructions, and the program instructions are used in the method for planning a driving trajectory in the foregoing embodiment.

The present application also provides a computer program product comprising executable instructions stored in a readable storage medium. The at least one processor of the electronic device may read the execution instruction from the readable storage medium, and the execution of the execution instruction by the at least one processor causes the electronic device to implement the driving path planning method provided in the above-described various embodiments.

Finally, it should be noted that: the above embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and these modifications or substitutions do not depart from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for planning a driving trajectory is characterized by comprising the following steps:

determining a desired interaction decision of a target vehicle and an obstacle within a future preset time range, wherein the desired interaction decision is used for representing an interaction behavior of the target vehicle and the obstacle and an interaction time window corresponding to the interaction behavior, and the interaction behavior comprises that the target vehicle actively exceeds the obstacle, the target vehicle actively yields the obstacle and the target vehicle actively ignores the obstacle;

constructing a longitudinal feasible space map of the target vehicle corresponding to the future preset time range based on the expected interaction decision, wherein the longitudinal feasible space map is used for representing a longitudinal feasible range and longitudinal speed constraint information of the target vehicle corresponding to discrete time within the future preset time range;

determining a target trajectory of the target vehicle within the future preset time range based on the reference speed.

2. The method for planning a driving trajectory according to claim 1, wherein the constructing a longitudinal feasible space map of the target vehicle corresponding to the future preset time range based on the desired interaction decision comprises:

determining a target longitudinal feasible minimum distance, a target longitudinal feasible maximum distance, a target longitudinal speed minimum value and a target longitudinal speed maximum value corresponding to discrete time of the target vehicle in the future preset time range based on the expected interaction decision;

and constructing a longitudinal feasible space map of the target vehicle corresponding to the future preset time range according to 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.

3. The method for planning a driving trajectory according to claim 2, wherein the determining, based on the desired interaction decision, a minimum target longitudinal feasible distance, a maximum target longitudinal feasible distance, a minimum target longitudinal speed, and a maximum target longitudinal speed corresponding to the target vehicle at the discrete time within the future preset time range comprises:

determining an initial longitudinally feasible maximum distance, an initial longitudinally feasible minimum distance, a minimum value of initial longitudinal velocity, and a maximum value of initial longitudinal velocity for the target vehicle;

and executing the following operations for each obstacle corresponding to the discrete time within the future preset time range until each obstacle is traversed:

based on the expected interactive decision, if it is determined that the target vehicle is required to actively yield the target obstacle, determining a target longitudinal feasible maximum distance of the target vehicle according to a space occupied by the target obstacle in the longitudinal and transverse directions, a preset yielding safety distance and the initial longitudinal feasible maximum distance, determining a maximum value of a target longitudinal speed according to the speed of the target obstacle, determining the target longitudinal feasible maximum distance as a new initial longitudinal feasible maximum distance, and determining the maximum value of the target longitudinal speed as a new maximum value of an initial longitudinal speed;

or, based on the expected interactive decision, if it is determined that the target vehicle is required to actively exceed the target obstacle, determining a target longitudinal feasible minimum distance of the target vehicle according to a space occupied by the target obstacle in the longitudinal and lateral directions, a preset overtaking safety distance and the initial longitudinal feasible minimum distance, determining a minimum value of a target longitudinal speed according to the speed of the target obstacle, determining the target longitudinal feasible minimum distance as a new initial longitudinal feasible minimum distance, and determining the minimum value of the target longitudinal speed as a new initial longitudinal speed minimum value.

4. The method of claim 3, wherein said determining an initial longitudinal feasible maximum distance of the target vehicle comprises:

determining the maximum feasible distance of the target vehicle within the future preset time range based on the driving scene speed limit of the target vehicle;

determining the maximum speed corresponding to the maximum curvature according to the maximum curvature of the running path of the target vehicle in the future preset time range and the corresponding relation between the preset curvature and the speed constraint;

obtaining a target maximum feasible distance according to the maximum speed and the maximum feasible distance;

determining the target maximum feasible distance as the initial longitudinally feasible maximum distance.

5. The method for planning a driving trajectory according to claim 4, wherein after the constructing the longitudinal feasible space map of the target vehicle corresponding to the preset time range in the future, the method further comprises:

aiming at the target longitudinal feasible minimum distance and the target longitudinal feasible maximum distance corresponding to the discrete time within the future preset time range, obtaining the updated target longitudinal feasible minimum distance corresponding to the current discrete time according to the target longitudinal feasible minimum distance corresponding to the previous discrete time and the target longitudinal feasible minimum distance corresponding to the current discrete time, and obtaining the updated target longitudinal feasible maximum distance corresponding to the current discrete time according to the target maximum feasible distance and the target longitudinal feasible maximum distance corresponding to the current discrete time;

and obtaining an updated longitudinal feasible space map according to the updated minimum distance of the target longitudinal feasible and the updated maximum distance of the target longitudinal feasible.

6. The method for planning a driving trajectory according to claim 5, wherein the obtaining an updated target longitudinally feasible minimum distance corresponding to the current discrete time according to a target longitudinally feasible minimum distance corresponding to a previous discrete time and a target longitudinally feasible minimum distance corresponding to the current discrete time, and obtaining an updated target longitudinally feasible maximum distance corresponding to the current discrete time according to the target maximum feasible distance and a target longitudinally feasible maximum distance corresponding to the current discrete time comprise:

and if the updated maximum distance of the target longitudinal feasible is determined to be smaller than the updated minimum distance of the target longitudinal feasible for the discrete time within the future preset time range, updating the interaction behavior of the corresponding barrier and the target vehicle, and reconstructing a longitudinal feasible space diagram of the target vehicle corresponding to the future preset time range.

7. The driving trajectory planning method according to any one of claims 1 to 6, wherein the obtaining the reference speed of the target vehicle based on the longitudinal feasible space map and a first preset cost function comprises:

based on the longitudinal feasible space map, obtaining updated longitudinal feasible range and longitudinal speed constraint information according to the maximum acceleration capacity and the maximum deceleration capacity of the target vehicle, the curvature corresponding to the reference line of the path in the longitudinal feasible range and the corresponding relation between preset curvature and speed constraint;

obtaining an updated longitudinal feasible space map according to the updated longitudinal feasible range and longitudinal speed constraint information;

acquiring a first target position of the target vehicle corresponding to the discrete moment based on the updated longitudinal feasible space diagram and the first preset cost function;

and fitting the first target position to obtain the reference speed of the target vehicle.

8. The method of claim 7, wherein the determining the target trajectory of the target vehicle within the future preset time range based on the reference speed comprises:

obtaining a target candidate path corresponding to the future preset time range according to 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 running path of the target vehicle;

and determining a target track of the target vehicle in the future preset time range according to the target candidate path and a second preset cost function, wherein the second preset cost function is determined based on the driving safety degree, the comfort degree and the stability of the target vehicle.

9. The method for planning a driving trajectory according to claim 8, wherein the obtaining a target candidate route corresponding to the future preset time range according to at least one preset candidate route corresponding to the target vehicle and the reference speed comprises:

for each preset candidate path, obtaining a longitudinal advance distance corresponding to the target vehicle corresponding to the discrete moment according to the reference speed;

according to the longitudinal advancing distance and the preset candidate path, a second target position of the target vehicle corresponding to the discrete moment is obtained;

and carrying out coordinate transformation on the second target position to obtain the target candidate path.

10. A trajectory planning device, comprising:

the construction module is used for constructing a longitudinal feasible space map of the target vehicle corresponding to the future preset time range based on the expected interaction decision, wherein the longitudinal feasible space map is used for representing the longitudinal feasible range and longitudinal speed constraint information of the target vehicle corresponding to discrete time within the future preset time range;

an obtaining module, 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 a driving efficiency and a comfort level of the target vehicle;

and the processing module is used for determining a target track of the target vehicle in the future preset time range based on the reference speed.

11. An electronic device, comprising: a processor, and a memory communicatively coupled to the processor;

the memory stores computer-executable instructions;

the processor executes computer-executable instructions stored by the memory to implement the trajectory planning method of any one of claims 1 to 9.

12. A computer-readable storage medium, in which computer program instructions are stored, which, when executed by a processor, implement a driving trajectory planning method according to any one of claims 1 to 9.

13. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements a driving trajectory planning method according to any one of claims 1 to 9.