阅读说明：本技术 轨迹规划方法及装置 (Trajectory planning method and device ) 是由 彭夏鹏 卢文建 赵佳佳 韩晋 于 2021-09-23 设计创作，主要内容包括：本申请实施例提供一种轨迹规划方法及装置,该方法包括：获取多个邻近车辆各自对应的第一概率,第一概率为邻近车辆在未来时段内与无人驾驶车辆同车道行驶的概率。根据多个邻近车辆各自对应的第一概率,确定至少一个待选车辆,待选车辆对应的第一概率大于或等于第一阈值,且小于或等于第二阈值。若至少一个待选车辆中存在待选车辆与无人驾驶车辆之间距离小于或等于预设距离,则确定无人驾驶车辆在未来时段内的目标速度轨迹。根据无人驾驶车辆对应的预设路径轨迹和目标速度轨迹,确定无人驾驶车辆的行车轨迹。其中,在距离无人驾驶车辆预设距离之内存在至少一个待选车辆时,对无人驾驶车辆在未来时段内速度轨迹进行修正,以提高轨迹规划的可靠性。(The embodiment of the application provides a track planning method and a device, wherein the method comprises the following steps: a first probability corresponding to each of the plurality of neighboring vehicles is obtained, and the first probability is a probability that the neighboring vehicle travels in the same lane as the unmanned vehicle in a future period. And determining at least one vehicle to be selected according to the first probability corresponding to the adjacent vehicles, wherein the first probability corresponding to the vehicle to be selected is greater than or equal to a first threshold value and is less than or equal to a second threshold value. And if the distance between the vehicle to be selected and the unmanned vehicle is less than or equal to the preset distance in at least one vehicle to be selected, determining the target speed track of the unmanned vehicle in the future period. And determining the driving track of the unmanned vehicle according to the preset path track and the target speed track corresponding to the unmanned vehicle. When at least one vehicle to be selected exists within a preset distance from the unmanned vehicle, the speed track of the unmanned vehicle in a future time period is corrected, so that the reliability of track planning is improved.)

1. A trajectory planning method, applied to an unmanned vehicle, the method comprising:

acquiring first probabilities corresponding to a plurality of adjacent vehicles respectively, wherein the first probabilities are probabilities that the adjacent vehicles travel in the same lane with the unmanned vehicle in a future period;

determining at least one vehicle to be selected according to first probabilities corresponding to the adjacent vehicles respectively, wherein the first probability corresponding to the vehicle to be selected is larger than or equal to a first threshold and smaller than or equal to a second threshold;

if the distance between the vehicle to be selected and the unmanned vehicle is smaller than or equal to the preset distance in the at least one vehicle to be selected, determining a target speed track of the unmanned vehicle in the future period;

and determining the driving track of the unmanned vehicle according to the preset path track and the target speed track corresponding to the unmanned vehicle.

2. The method of claim 1, wherein the determining the target speed trajectory of the unmanned vehicle over the future period of time comprises:

acquiring a current first running speed of the unmanned vehicle;

determining a first acceleration corresponding to the unmanned vehicle according to the first running speed;

determining a target speed trajectory of the unmanned vehicle over the future time period based on the first travel speed and the first acceleration.

3. The method of claim 2, wherein determining a corresponding first acceleration of the unmanned vehicle based on the first travel speed comprises:

acquiring a longitudinal dynamic model of the unmanned vehicle, wherein the longitudinal dynamic model is used for indicating the relation between braking force, accelerator opening, running speed and acceleration;

acquiring preset parameters, wherein the preset parameters comprise a first braking force and a first accelerator opening degree;

and determining a first acceleration corresponding to the unmanned vehicle according to the first running speed, the longitudinal dynamics model and the preset parameters.

4. The method according to any one of claims 1-3, wherein for any one of the plurality of neighboring vehicles; the obtaining of the first probabilities corresponding to the neighboring vehicles, respectively, includes:

acquiring predicted lane information of the adjacent vehicle in the future time period, wherein the predicted lane information comprises a target driving lane of the adjacent vehicle and a second probability corresponding to the target driving lane;

determining a first lane where the unmanned vehicle runs in the future time period according to a preset path track corresponding to the unmanned vehicle;

and determining a first probability corresponding to each of the plurality of adjacent vehicles according to the predicted lane information of the adjacent vehicles and the first lane.

5. The method of claim 4, wherein determining a first probability for each of a plurality of neighboring vehicles based on the predicted lane information for the neighboring vehicle and the first lane comprises:

judging whether the target driving lane of the adjacent vehicle is the same as the first lane or not;

if so, determining a second probability corresponding to the adjacent vehicle in the target driving lane as a first probability corresponding to the adjacent vehicle;

if not, determining the first probability corresponding to the adjacent vehicle as 0.

6. The method of claim 5, wherein the obtaining predicted lane information for the neighboring vehicle over the future time period comprises:

acquiring historical path tracks of the adjacent vehicles;

predicting the path tracks of the adjacent vehicles in the future period according to the historical path tracks of the adjacent vehicles, and determining at least one predicted path track of the adjacent vehicles;

determining the traveling probability of the adjacent vehicle in each lane in the future period according to at least one predicted path track of the adjacent vehicle;

and determining the lane with the maximum driving probability as the target driving lane, and determining the driving probability corresponding to the target driving lane as the second probability.

7. A trajectory planning device for use in an unmanned vehicle, the device comprising:

the device comprises an acquisition module, a judgment module and a display module, wherein the acquisition module is used for acquiring a first probability corresponding to each of a plurality of adjacent vehicles, and the first probability is the probability that the adjacent vehicle runs in the same lane with the unmanned vehicle in a future period;

the first determining module is used for determining at least one vehicle to be selected according to first probabilities corresponding to the adjacent vehicles respectively, wherein the first probability corresponding to the vehicle to be selected is larger than or equal to a first threshold and smaller than or equal to a second threshold;

the processing module is used for determining a target speed track of the unmanned vehicle in the future period if the distance between the vehicle to be selected and the unmanned vehicle is smaller than or equal to a preset distance in the at least one vehicle to be selected;

and the second determination module is used for determining the driving track of the unmanned vehicle according to the preset path track corresponding to the unmanned vehicle and the target speed track.

8. 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 method of any of claims 1 to 6.

9. A computer-readable storage medium comprising instructions which, when executed on a computer, cause the computer to perform the method of any one of claims 1 to 6.

10. A computer program product comprising a computer program, characterized in that the computer program realizes the method of any one of claims 1 to 6 when executed by a processor.

Technical Field

The embodiment of the application relates to the technical field of unmanned driving, in particular to a track planning method and device.

Background

The unmanned vehicle comprises a vehicle-mounted sensor, a track planning device and a vehicle controller, the vehicle-mounted sensor system senses the surrounding environment of the vehicle, the track planning device plans a driving track according to information such as roads, vehicle positions and obstacles obtained by sensing and sends the driving track to the vehicle controller to control the steering and the speed of the vehicle, and finally the vehicle reaches a preset destination.

In the related art, in the process of vehicle driving, if the existence of the obstacle is clearly sensed, the vehicle may drive according to the current driving track, and after the existence of the obstacle is clearly sensed, a new driving track may be generated and sent to the vehicle controller, so that the vehicle is controlled to drive according to the new driving track.

However, the in-vehicle sensor can determine that an obstacle is actually present when the unmanned vehicle is close to the obstacle. If the existence of the obstacle is determined, the driving speed of the unmanned vehicle is too high to achieve effective speed reduction or steering, traffic accidents are easily caused, safety is low, and the problem of low reliability of trajectory planning exists.

Disclosure of Invention

The application provides a track planning method and a track planning device, which are used for improving the reliability of track planning.

In a first aspect, the present application provides a trajectory planning method applied to an unmanned vehicle, the method comprising:

acquiring first probabilities corresponding to a plurality of adjacent vehicles respectively, wherein the first probabilities are probabilities that the adjacent vehicles travel in the same lane with the unmanned vehicle in a future period;

determining at least one vehicle to be selected according to first probabilities corresponding to the adjacent vehicles respectively, wherein the first probability corresponding to the vehicle to be selected is larger than or equal to a first threshold and smaller than or equal to a second threshold;

if the distance between the vehicle to be selected and the unmanned vehicle is smaller than or equal to the preset distance in the at least one vehicle to be selected, determining a target speed track of the unmanned vehicle in the future period;

and determining the driving track of the unmanned vehicle according to the preset path track and the target speed track corresponding to the unmanned vehicle.

In one possible design, the determining a target speed trajectory of the unmanned vehicle over the future time period includes:

acquiring a current first running speed of the unmanned vehicle;

determining a first acceleration corresponding to the unmanned vehicle according to the first running speed;

determining a target speed trajectory of the unmanned vehicle over the future time period based on the first travel speed and the first acceleration.

In one possible design, the determining a corresponding first acceleration of the unmanned vehicle based on the first travel speed includes:

acquiring a longitudinal dynamic model of the unmanned vehicle, wherein the longitudinal dynamic model is used for indicating the relation between braking force, accelerator opening, running speed and acceleration;

acquiring preset parameters, wherein the preset parameters comprise a first braking force and a first accelerator opening degree;

and determining a first acceleration corresponding to the unmanned vehicle according to the first running speed, the longitudinal dynamics model and the preset parameters.

In one possible design, the method further comprises, for any one of the plurality of neighboring vehicles; the obtaining of the first probabilities corresponding to the neighboring vehicles, respectively, includes:

acquiring predicted lane information of the adjacent vehicle in the future time period, wherein the predicted lane information comprises a target driving lane of the adjacent vehicle and a second probability corresponding to the target driving lane;

determining a first lane where the unmanned vehicle runs in the future time period according to a preset path track corresponding to the unmanned vehicle;

and determining a first probability corresponding to each of the plurality of adjacent vehicles according to the predicted lane information of the adjacent vehicles and the first lane.

In one possible design, the determining a first probability for each of a plurality of neighboring vehicles based on the predicted lane information of the neighboring vehicle and the first lane comprises:

judging whether the target driving lane of the adjacent vehicle is the same as the first lane or not;

if so, determining a second probability corresponding to the adjacent vehicle in the target driving lane as a first probability corresponding to the adjacent vehicle;

if not, determining a first probability corresponding to the adjacent vehicle as 0;

in one possible design, the obtaining predicted lane information of the neighboring vehicle over the future time period includes:

acquiring historical path tracks of the adjacent vehicles;

predicting the path tracks of the adjacent vehicles in the future period according to the historical path tracks of the adjacent vehicles, and determining at least one predicted path track of the adjacent vehicles;

determining the traveling probability of the adjacent vehicle in each lane in the future period according to at least one predicted path track of the adjacent vehicle;

and determining the lane with the maximum driving probability as the target driving lane, and determining the driving probability corresponding to the target driving lane as the second probability.

In a second aspect, the present application provides a trajectory planning apparatus for use with an unmanned vehicle, the apparatus comprising:

the device comprises an acquisition module, a judgment module and a display module, wherein the acquisition module is used for acquiring a first probability corresponding to each of a plurality of adjacent vehicles, and the first probability is the probability that the adjacent vehicle runs in the same lane with the unmanned vehicle in a future period;

the first determining module is used for determining at least one vehicle to be selected according to first probabilities corresponding to the adjacent vehicles respectively, wherein the first probability corresponding to the vehicle to be selected is larger than or equal to a first threshold and smaller than or equal to a second threshold;

the processing module is used for determining a target speed track of the unmanned vehicle in the future period if the distance between the vehicle to be selected and the unmanned vehicle is smaller than or equal to a preset distance in the at least one vehicle to be selected;

and the second determination module is used for determining the driving track of the unmanned vehicle according to the preset path track corresponding to the unmanned vehicle and the target speed track.

In one possible design, the processing module is specifically configured to:

acquiring a current first running speed of the unmanned vehicle;

determining a first acceleration corresponding to the unmanned vehicle according to the first running speed;

determining a target speed trajectory of the unmanned vehicle over the future time period based on the first travel speed and the first acceleration.

In one possible design, the processing module is specifically configured to:

acquiring a longitudinal dynamic model of the unmanned vehicle, wherein the longitudinal dynamic model is used for indicating the relation between braking force, accelerator opening, running speed and acceleration;

acquiring preset parameters, wherein the preset parameters comprise a first braking force and a first accelerator opening degree;

and determining a first acceleration corresponding to the unmanned vehicle according to the first running speed, the longitudinal dynamics model and the preset parameters.

In one possible design, the method further comprises, for any one of the plurality of neighboring vehicles; the acquisition module is specifically configured to:

acquiring predicted lane information of the adjacent vehicle in the future time period, wherein the predicted lane information comprises a target driving lane of the adjacent vehicle and a second probability corresponding to the target driving lane;

determining a first lane where the unmanned vehicle runs in the future time period according to a preset path track corresponding to the unmanned vehicle;

and determining a first probability corresponding to each of the plurality of adjacent vehicles according to the predicted lane information of the adjacent vehicles and the first lane.

In one possible design, the obtaining module is specifically configured to:

judging whether the target driving lane of the adjacent vehicle is the same as the first lane or not;

if so, determining a second probability corresponding to the adjacent vehicle in the target driving lane as a first probability corresponding to the adjacent vehicle;

if not, determining a first probability corresponding to the adjacent vehicle as 0;

in one possible design, the obtaining module is specifically configured to:

acquiring historical path tracks of the adjacent vehicles;

predicting the path tracks of the adjacent vehicles in the future period according to the historical path tracks of the adjacent vehicles, and determining at least one predicted path track of the adjacent vehicles;

predicting the traveling probability of the adjacent vehicle in each lane in the future period according to at least one predicted path track of the adjacent vehicle;

and determining the lane with the maximum driving probability as the target driving lane, and determining the driving probability corresponding to the target driving lane as the second probability.

In a third aspect, the present application provides a trajectory planning device, including:

a memory for storing a program;

a processor for executing the program stored by the memory, the processor being adapted to perform the method as described above in the first aspect and any one of the various possible designs of the first aspect when the program is executed.

In a fourth aspect, the present application provides a computer-readable storage medium comprising instructions which, when executed on a computer, cause the computer to perform the method as described above in the first aspect and any one of the various possible designs of the first aspect.

In a fifth aspect, the present application provides a computer program product comprising: a computer program stored in a readable storage medium, from which at least one processor of an electronic device can read the computer program, execution of the computer program by the at least one processor causing the electronic device to perform the method as set forth in the first aspect above and any one of the various possible designs of the first aspect.

The application provides a trajectory planning method and a device, and the method comprises the following steps: a first probability corresponding to each of the plurality of neighboring vehicles is obtained, and the first probability is a probability that the neighboring vehicle travels in the same lane as the unmanned vehicle in a future period. And determining at least one vehicle to be selected according to the first probability corresponding to the adjacent vehicles, wherein the first probability corresponding to the vehicle to be selected is greater than or equal to a first threshold value and is less than or equal to a second threshold value. And if the distance between the vehicle to be selected and the unmanned vehicle is less than or equal to the preset distance in at least one vehicle to be selected, determining the target speed track of the unmanned vehicle in the future period. And determining the driving track of the unmanned vehicle according to the preset path track and the target speed track corresponding to the unmanned vehicle. When the distance between the vehicle to be selected and the unmanned vehicle is smaller than or equal to the preset distance in at least one vehicle to be selected, the speed track of the unmanned vehicle in the future period is corrected to be the target speed track, and therefore the running safety of the unmanned vehicle can be improved.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.

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

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

fig. 3 is a second flowchart of a trajectory planning method provided in the embodiment of the present application;

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

fig. 5 is a schematic hardware structure diagram of a trajectory planning device according to an embodiment of the present application.

With the above figures, there are shown specific embodiments of the present application, which will be described in more detail below. These drawings and written description are not intended to limit the scope of the inventive concepts in any manner, but rather to illustrate the inventive concepts to those skilled in the art by reference to specific embodiments.

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.

It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

In order to facilitate understanding of the technical solutions of the present application, the related background art related to the present application is first described:

an in-vehicle detector, a trajectory prediction device, a trajectory planning device, and a vehicle controller are provided in an unmanned vehicle. The vehicle-mounted detector can be an infrared distance measuring instrument or millimeter wave radar and other equipment. The unmanned vehicle detects neighboring vehicles of the unmanned vehicle through the in-vehicle detector, and acquires the conditions of the neighboring vehicles, for example, the distance between the neighboring vehicles and the unmanned vehicle, the lane information on which the neighboring vehicles travel, the travel speed, and the like. According to the lane information and the running speed of the adjacent vehicle, the track prediction equipment can predict the track of the adjacent vehicle to obtain the predicted driving track corresponding to the adjacent vehicle. A trajectory planning device in the unmanned vehicle plans a driving trajectory for the unmanned vehicle according to the predicted driving trajectories corresponding to the adjacent vehicles respectively, so that traffic accidents caused by the fact that the driving routes of the unmanned vehicle are the same as those of the adjacent vehicles in the driving process are avoided, and the driving safety of the unmanned vehicle is improved.

The following describes the prior art, technical problems existing in the prior art, and technical concepts of the present application:

in the prior art, in the process of vehicle running, if the existence of an obstacle is clearly sensed, the vehicle can run according to the current running track, and after the existence of the obstacle is clearly sensed, a new running track is generated and sent to a vehicle controller, so that the vehicle is controlled to run according to the new running track.

However, in the prior art, when the distance between the unmanned vehicle and the obstacle is short, the vehicle-mounted detector can determine that the obstacle is really present. If the existence of the obstacle is determined, the driving speed of the unmanned vehicle is too high to achieve effective speed reduction or steering, traffic accidents are easily caused, safety is low, and the problem of low reliability of trajectory planning exists.

Based on the existing problems, the application provides the following technical concepts: during the driving of the unmanned vehicle, the driving tracks of the neighboring vehicles in the future period are generally predicted according to the historical driving tracks of the neighboring vehicles. And determining a driving lane of the adjacent vehicle in the future time period according to the driving track of the adjacent vehicle in the future time period. Then, the trajectory planning unit plans the driving trajectory of the unmanned vehicle in the future period according to the driving lanes of the adjacent vehicles in the future period so as to avoid the same driving lanes as the adjacent vehicles in the future period. It is considered that when it is clear that the neighboring vehicle is in the same lane as the unmanned vehicle in the future period of time, or it is clear that the neighboring vehicle is not in the same lane as the unmanned vehicle in the future period of time, the trajectory planning unit plans the correct driving trajectory for the unmanned vehicle. But when it is not clear whether the neighboring vehicle is in the same lane as the unmanned vehicle within the future period, then there is a possibility that the trajectory planning unit is incorrect for the planned driving trajectory of the unmanned vehicle. In order to avoid safety accidents caused by incorrect driving track planning, the speed track in the driving track of the unmanned vehicle is corrected when whether the adjacent vehicle is in the same lane with the unmanned vehicle in the future period cannot be determined. Specifically, the speed trajectory of the unmanned vehicle in the future period is corrected by considering a driving thinking of simulating that a human driver does not step on a brake or an accelerator when encountering an unclear driving actual condition and then determines how to drive the next step after the driving intention of an adjacent vehicle is clear. Thus, over time, it becomes more and more clear whether the neighboring vehicle is co-located with the unmanned vehicle within a future time period. When it is clear that the adjacent vehicle is in the same lane with the unmanned vehicle in the future period of time or it is clear that the adjacent vehicle is not in the same lane with the unmanned vehicle in the future period of time, the track planning unit plans a correct driving track for the unmanned vehicle. And then, the unmanned vehicle runs according to the driving track planned by the track planning unit.

Next, an application scenario of the embodiment of the present application is described with reference to fig. 1, and fig. 1 is a schematic diagram of an application scenario provided in the embodiment of the present application.

Referring to fig. 1, 3 lanes of the unidirectional lane include the unmanned vehicle and 3 neighboring vehicles of the unmanned vehicle, the 3 neighboring vehicles being a neighboring vehicle 1, a neighboring vehicle 2, and a neighboring vehicle 3, respectively. Correspondingly, the path trajectories of the respective vehicles in the future time period are also drawn. As shown in fig. 1, the unmanned vehicle, the neighboring vehicle 2, and the neighboring vehicle 3 will continue to travel along the respective current lanes in a future period without changing the travel lanes. The path track of the neighboring vehicle 1 shows 2 bars, indicating that the neighboring vehicle 1 may continue to travel along the current lane and may also change lanes to travel in the lane where the unmanned vehicle is located.

When the unmanned vehicle is not sure about finding the traveling lane of the adjacent vehicle 1, the speed trajectory of the unmanned vehicle is corrected so that the unmanned vehicle coasts forward with a braking force of 0 and an accelerator opening of 0.

The technical means shown in the present application will be described in detail below with reference to specific examples. It should be noted that the following embodiments may exist alone or in combination with each other, and description of the same or similar contents is not repeated in different embodiments.

Based on the technical concept introduced above, the following describes in detail the trajectory planning method provided by the present application with reference to the specific embodiment of fig. 2, and fig. 2 is a first flowchart of the trajectory planning method provided by the embodiment of the present application.

As shown in fig. 2, the method includes:

s201, acquiring first probabilities corresponding to the adjacent vehicles respectively, wherein the first probabilities are probabilities that the adjacent vehicles run in the same lane with the unmanned vehicle in a future period.

The trajectory planning method provided by the application is applied to the unmanned vehicle, and the execution main body of the embodiment of the application can be a processor in the unmanned vehicle or a data processing device arranged in the processor. Wherein the data processing means in the processor may be implemented by a combination of software and/or hardware.

In the embodiment of the present application, the neighboring vehicle means a vehicle that travels near the unmanned vehicle while the unmanned vehicle is traveling.

The in-vehicle detector in the unmanned vehicle detects a vehicle in the vicinity of the unmanned vehicle, and the vehicle-mounted detector can acquire travel information such as travel track information and travel speed of each neighboring vehicle by continuing the detection in a short time. Then, a trajectory prediction unit in the unmanned vehicle predicts the travel trajectory of each neighboring vehicle in a future period of time based on the historical travel trajectory information of the neighboring vehicles to obtain the travel trajectory of each neighboring vehicle in the future period of time. And then, determining the driving lane of each adjacent vehicle in the future period according to the driving track of each adjacent vehicle in the future period. The future time period may be, for example, 8 seconds, 10 seconds, and the like in the future, and may be specifically set according to actual requirements, which is not limited thereto.

In the following, a possible implementation manner of obtaining the first probability corresponding to each of the plurality of neighboring vehicles is exemplarily described.

In one possible implementation, the trajectory of the unmanned vehicle over the future time period is planned according to an in-unmanned-vehicle trajectory planning unit. The driving track comprises a speed track and a path track. According to the path track of the unmanned vehicle in the future period, the lane information of the unmanned vehicle in the future period can be determined. After the lane information of the unmanned vehicle in the future period is obtained, next, the lane information of a plurality of adjacent vehicles in the future period is determined according to the driving paths predicted by the path prediction unit for the adjacent vehicles. The lane information may be, for example, a probability that the neighboring vehicle travels in each lane in a future period. For each adjacent vehicle, determining the probability that the adjacent vehicle runs on the lane on which the unmanned vehicle runs in the future period as a first probability corresponding to the adjacent vehicle. Wherein the first probability is a probability that the neighboring vehicle will travel in the same lane as the unmanned vehicle within a future time period.

S202, determining at least one vehicle to be selected according to the first probability corresponding to each of the plurality of adjacent vehicles, wherein the first probability corresponding to the vehicle to be selected is larger than or equal to a first threshold value and smaller than or equal to a second threshold value.

In this embodiment, the first threshold and the second threshold are both in a range greater than or equal to 0 and less than or equal to 1. Meanwhile, the magnitude relation between the first threshold and the second threshold is that the first threshold is smaller than the second threshold.

In the present application, the magnitude of the first probability corresponding to each neighboring vehicle is taken into account to some extent to be able to indicate whether it will be in the same lane as the unmanned vehicle. Specifically, if the first probability corresponding to the neighboring vehicle is smaller than the first threshold, it indicates that the probability that the neighboring vehicle is in the same lane as the unmanned vehicle is small, and it may be determined that the neighboring vehicle does not travel in the same lane as the unmanned vehicle. Similarly, if the first probability corresponding to the neighboring vehicle is greater than the second threshold, it indicates that the neighboring vehicle has a higher probability of being in the same lane as the unmanned vehicle, and it may be determined that the neighboring vehicle will travel in the same lane as the unmanned vehicle. Based on the fact that the adjacent vehicle and the unmanned vehicle can be definitely driven in the same lane or the adjacent vehicle and the unmanned vehicle cannot be driven in the same lane, the track planning unit plans the correct driving track for the unmanned vehicle. Then for the case of uncertainty (when the first probability corresponding to the neighboring vehicle is less than or equal to the first threshold and less than or equal to the second threshold, i.e., the case of being unable to determine whether the neighboring lane and the unmanned vehicle are traveling in the same lane), the neighboring vehicle corresponding to the first probability being greater than or equal to the first threshold and less than or equal to the second threshold among the plurality of neighboring vehicles is determined as the candidate vehicle.

S203, if the distance between the vehicle to be selected and the unmanned vehicle is smaller than or equal to the preset distance in the at least one vehicle to be selected, determining a target speed track of the unmanned vehicle in a future period.

It should be noted that, if at least one candidate vehicle exists within the preset distance from the unmanned vehicle, the unmanned vehicle needs to correct the speed trajectory in the future period, so as to avoid collision between the unmanned vehicle and the at least one candidate vehicle within the preset distance range. In addition, the specific value of the preset distance can be selected according to actual requirements, and the method is not limited.

In the present embodiment, when it is not clear whether the neighboring vehicle is in the same lane as the unmanned vehicle within the future period, then there is a possibility that the trajectory planning unit is incorrect for the trajectory planned by the unmanned vehicle. In order to avoid a safety accident caused by incorrect planning of the driving track, the inventor considers that the speed track in the driving track of the unmanned vehicle is corrected when it is not clear whether the adjacent vehicle is in the same lane with the unmanned vehicle in the future period. Specifically, the inventor considers the driving thinking of simulating that the driver does not step on the brake or the accelerator when the driver encounters an undefined driving condition, and determines how to drive the next step after the driving intention of an adjacent vehicle is clear, and corrects the speed track of the unmanned vehicle in a future period of time so as to improve the reliability of track planning and further improve the driving safety of the unmanned vehicle.

In the embodiment, whether the speed trajectory of the unmanned vehicle in the future period is to be corrected is determined by judging whether the distance between the candidate vehicle and the unmanned vehicle is less than or equal to the preset distance in at least one candidate vehicle. If the speed track does not exist in the future period, the speed track of the unmanned vehicle in the future period does not need to be corrected, and the track planning unit is adopted to plan the speed track of the unmanned vehicle in the future period for driving. If so, the velocity trajectory of the unmanned vehicle over the future time period needs to be corrected. Specifically, the correction method is to determine a target speed trajectory by targeting the unmanned vehicle to coast with a braking force of 0 and an accelerator opening of 0.

The target speed trajectory is used to describe speed information of the unmanned vehicle at a plurality of time points determined at intervals of a preset time period in a future period. Specifically, the speed information corresponding to each time point may include, but is not limited to, a driving speed, an acceleration, and a driving distance of the unmanned vehicle. Wherein, the driving distance is the distance driven by the current time point from the previous time point.

In the following, taking the number of time points in a future period as K as an example, an exemplary description will be given of a possible implementation manner of determining speed information corresponding to each time point with the target of coasting in a state where the brake is 0 and the accelerator opening is 0.

It is emphasized that, before determining the travel speed and the travel distance of the unmanned vehicle at each time point, the acceleration of the unmanned vehicle at each time point needs to be determined.

First, a possible implementation of determining the acceleration corresponding to each time point in a future time period is exemplarily described.

In one possible implementation, a longitudinal dynamics model corresponding to the unmanned vehicle is obtained, and the running speed of the unmanned vehicle is obtained. The longitudinal dynamic model corresponding to the unmanned vehicle is used for describing the relation among braking force, accelerator opening, vehicle running speed and acceleration. Specifically, the longitudinal dynamics model corresponding to the unmanned vehicle may be in the form of a functional expression, for example, as shown in formula one:

formula one

Wherein Ldm () represents a function of the relationship among braking force, accelerator opening, vehicle running speed, acceleration,indicates the magnitude of the braking force (brake) of the unmanned vehicle,v is a value representing the accelerator opening of the unmanned vehicle, v is a value representing the traveling speed of the unmanned vehicle,is a representation of the corresponding travel speed of the unmanned vehicle.

It should be noted that, in the following description,in (1),judging the running speed of the unmanned vehicle when the distance between the vehicle to be selected and the unmanned vehicle is less than or equal to the preset distance in at least one vehicle to be selected,is the travel speed of the unmanned vehicle at the ith point in time within the future period. In a corresponding manner, the first and second optical fibers are,to obtain the driving speed of the unmanned vehicle according to the longitudinal dynamic model of the unmanned vehicleAcceleration when the braking force is 0 and the accelerator opening degree is 0.To obtain a running speed of the unmanned vehicle at an i-th time point of a future period based on a longitudinal dynamics model of the unmanned vehicleBraking force of 0 and accelerator opening ofAcceleration at 0.

Next, in a possible implementation manner, a calculation method for determining the driving speed of the unmanned vehicle in the target speed trajectory at each time point can be referred to as formula two.

Formula two

Wherein the content of the first and second substances,is the travel speed of the unmanned vehicle at the ith second in the future period;t is a preset time period for the acceleration of the unmanned vehicle at the ith second in the future period.

It should be noted that, in the following description,when the distance between the vehicle to be selected and the unmanned vehicle is determined to be less than or equal to the preset distance in at least one vehicle to be selected, the current running speed of the unmanned vehicle can be obtained through measurement by a vehicle speed sensor in the unmanned vehicle.

In the following, in a possible implementation manner, a calculation method for determining the travel distance corresponding to each time segment point in the target speed trajectory may refer to formula three, for example.

Formula three

Wherein the content of the first and second substances,the distance traveled by the unmanned automobile from the i-1 st time point to the i-th time point is obtained.

Based on the above, the running speed of the unmanned vehicle in the future period is determined, that is, the speed track of the unmanned vehicle in the future period is determined. It should be noted that specific values of the future time period and the preset time period may be selected according to actual situations. Next, taking the future period as 8 seconds, taking 1 second as a preset duration as an example, that is, 8 time points are determined by taking 1 second as an interval for the future 8 seconds. The speed trace points for each time point in the target speed trace of the unmanned vehicle within the future 8 seconds may be as shown in table 1.

TABLE 1

Thus, the target velocity trajectory in the future 8 seconds is: {，，，，，，，}。

And S204, determining the driving track of the unmanned vehicle according to the preset path track and the target speed track corresponding to the unmanned vehicle.

The preset path track corresponding to the unmanned vehicle is determined for the unmanned vehicle by the track planning unit in the unmanned vehicle. For a specific implementation method for determining the preset path trajectory, reference may be made to a method in the prior art, and details are not described herein.

For example, the preset path trajectory of the unmanned vehicle is identified in the form of a function, for example, with reference to formula four.

Formula four

Wherein Path () is a function corresponding to a preset Path trajectory of the unmanned vehicle,the longitude at which the unmanned vehicle was at the ith time point in the future period,is the dimension that the unmanned vehicle is in at the ith time point in the future period.

Determining a target speed track of the unmanned vehicle based on the step S203, and forming a corresponding preset path track and the target speed track of the unmanned vehicle, namely a driving track of the unmanned vehicle. That is, the unmanned vehicle travels along the preset path trajectory and the target speed trajectory in the future period.

Next, an exemplary description is given of a possible implementation manner of determining the driving trajectory of the unmanned vehicle according to the preset path trajectory and the target speed trajectory corresponding to the unmanned vehicle.

In one possible implementation, the longitude and latitude of the unmanned vehicle at each time point in the future period are determined according to a preset path track of the unmanned vehicle. And determining the driving track of the unmanned vehicle at each time point in the future period according to the speed information of the unmanned vehicle at each time point in the future period and the longitude and latitude of the unmanned vehicle.

In the following, such a possible implementation is explained by a specific example.

Taking the future time interval as 8 seconds, taking 1 second as a preset time length as an example, 8 time points are determined by taking 1 second as an interval for the future 8 seconds. The travel track points for each time point in the target speed trajectory of the unmanned vehicle within the future 8 seconds may be as shown in table 2.

TABLE 2

Therefore, the trajectory of the unmanned vehicle in the future 8 seconds is: {，，，，，，，}。

The trajectory planning method provided by the embodiment of the application comprises the following steps: a first probability corresponding to each of the plurality of neighboring vehicles is obtained, and the first probability is a probability that the neighboring vehicle travels in the same lane as the unmanned vehicle in a future period. And determining at least one vehicle to be selected according to the first probability corresponding to the adjacent vehicles, wherein the first probability corresponding to the vehicle to be selected is greater than or equal to a first threshold value and is less than or equal to a second threshold value. And if the distance between the vehicle to be selected and the unmanned vehicle is less than or equal to the preset distance in at least one vehicle to be selected, determining the target speed track of the unmanned vehicle in the future period. And determining the driving track of the unmanned vehicle according to the preset path track and the target speed track corresponding to the unmanned vehicle. When the distance between the vehicle to be selected and the unmanned vehicle is smaller than or equal to the preset distance in at least one vehicle to be selected, the speed track of the unmanned vehicle in the future period is corrected to be the target speed track, and therefore the running safety of the unmanned vehicle can be improved.

On the basis of the foregoing embodiment, the trajectory planning method provided by the present application is further described below with reference to a specific embodiment, and is described with reference to fig. 3, where fig. 3 is a second flowchart of the trajectory planning method provided by the embodiment of the present application.

As shown in fig. 3, the method includes:

s301, acquiring historical path tracks of adjacent vehicles.

In the present embodiment, the neighboring vehicle is a vehicle that travels near the unmanned vehicle during travel of the unmanned vehicle.

In the following, a possible implementation of obtaining historical path trajectories for neighboring vehicles is exemplarily described.

In one possible implementation, the path trajectories of neighboring vehicles traveling near the unmanned vehicle may be detected by an onboard detector disposed on the unmanned vehicle. The path trajectory of the neighboring vehicle may be longitude and latitude where the neighboring vehicle is located. Therefore, the history path trajectories corresponding to each of the plurality of neighboring vehicles within the history period can be obtained by the on-vehicle detector of the unmanned vehicle.

S302, according to the historical path tracks of the adjacent vehicles, the path tracks of the adjacent vehicles in the future period are predicted, and at least one predicted path track of the adjacent vehicles is determined.

One possible implementation of determining at least one predicted path trajectory for a neighboring vehicle based on historical path trajectories for the neighboring vehicle is illustrated below.

In one possible implementation, for each neighboring vehicle, a historical path trajectory corresponding to the neighboring vehicle is input to a trajectory prediction unit within the unmanned vehicle. Thereafter, the trajectory prediction unit outputs at least one predicted path trajectory corresponding to the neighboring vehicle. And each predicted path track comprises position information, running speed and other information corresponding to each time point of the adjacent vehicle in a future period.

And S303, determining the traveling probability of the adjacent vehicle in each lane in the future period according to at least one predicted path track of the adjacent vehicle.

One possible implementation of determining at least one predicted path trajectory for a neighboring vehicle based on historical path trajectories for the neighboring vehicle is illustrated below.

In one possible implementation, the respective probabilities of the at least one predicted path trajectory of the neighboring vehicle may be obtained by a trajectory prediction unit. According to the position information of the adjacent vehicle in the predicted path track, the driving probability of the unmanned vehicle on each lane can be determined, namely the probability corresponding to the predicted path is the probability of the adjacent vehicle on each lane. Therefore, the driving probability of the adjacent vehicle in each lane in the future period is determined according to the respective probability of the adjacent vehicle in the at least one predicted path.

S304, determining the lane with the maximum driving probability as a target driving lane, and determining the driving probability corresponding to the target driving lane as a second probability.

Based on the above step S302, after determining the traveling probabilities of the respective pairs of neighboring vehicles in the respective lanes, a target traveling lane in a future period is determined for the respective neighboring vehicles.

In one possible implementation manner, for each neighboring vehicle, the lane with the highest traveling probability is determined as the target traveling lane corresponding to the neighboring vehicle, and the traveling probability corresponding to the target traveling lane is determined as the second probability corresponding to the neighboring vehicle.

S305, determining a first lane where the unmanned vehicle runs in a future time period according to the preset path track corresponding to the unmanned vehicle.

In this embodiment, the trajectory planning unit in the unmanned vehicle has planned a path trajectory of the unmanned vehicle within a future time period, i.e. a preset path trajectory, for the unmanned vehicle. The preset path track comprises a plurality of positions where the unmanned vehicle is to run in a future period of time, and the plurality of positions can form the path track. Where each location may be a specific coordinate point, e.g. (longitude, latitude).

It should be noted that, according to the positioning system in the unmanned vehicle, the road information where the unmanned vehicle is located may be acquired. The road information may include, for example, the number of lanes and a position range of each lane. The Positioning System may be, for example, a beidou Positioning System, a Global Positioning System (GPS), and the like, which is not limited thereto.

In the following, an exemplary description is given of a possible implementation manner of determining a first lane traveled by the unmanned vehicle in a future time period according to a preset path track corresponding to the unmanned vehicle.

In one possible implementation manner, the lane where the unmanned vehicle runs in the future time period is judged according to a plurality of positions in the preset path track corresponding to the unmanned vehicle and the position range of each lane. Specifically, if the plurality of positions are within the position range of one lane, the lane in which the unmanned vehicle is traveling in the future period is the lane. Then the lane is determined as the first lane in which the unmanned vehicle will travel within the future period.

S306, judging whether the target driving lane of the adjacent vehicle is the same as the first lane, if so, executing S307, and if not, executing S308.

In the present embodiment, in order to determine whether or not the neighboring vehicle is traveling in the same lane as the unmanned vehicle within the future period, it is necessary to decide by determining whether or not the target traveling lane of the target traveling vehicle of the neighboring vehicle is the same as the first lane. Specifically, when the target driving lane of the adjacent vehicle is the same as the first lane, it is determined that the adjacent vehicle and the unmanned vehicle will drive in the same lane in a future period; conversely, when the target travel lane of the neighboring vehicle is a different lane than the first lane, then it is determined that the neighboring vehicle will not travel in the same lane as the unmanned vehicle for the future period of time.

S307, determining the second probability corresponding to the adjacent vehicle in the target driving lane as the first probability corresponding to the adjacent vehicle.

The first probability refers to a probability that the neighboring vehicle travels in the same lane as the unmanned vehicle.

In this embodiment, for each neighboring vehicle, when it is determined that the target driving lane of the neighboring vehicle is the same as the first lane, the second probability that the neighboring vehicle corresponds to the target driving lane is determined as the first probability that the neighboring vehicle corresponds to.

And S308, determining the first probability corresponding to the adjacent vehicle as 0.

In the present embodiment, for each of the neighboring vehicles, when it is determined that the target traveling lane of the neighboring vehicle is a different lane from the first lane, that is, the probability of driving an unlimited car in the same lane as the neighboring vehicle in the future period is 0, the first probability corresponding to the neighboring vehicle is determined to be 0.

S309, determining at least one vehicle to be selected according to the first probability corresponding to each of the plurality of adjacent vehicles, wherein the first probability corresponding to the vehicle to be selected is larger than or equal to a first threshold value and smaller than or equal to a second threshold value.

It should be noted that, when the first probability of the neighboring vehicle pair is less than or equal to the first threshold, the neighboring vehicle is determined not to travel in the same lane as the unmanned vehicle; and when the first probability of one of the neighboring vehicles being in the lane is greater than the second threshold, then the neighboring vehicle is considered to be determined to be traveling in the same lane as the unmanned vehicle.

The first probability corresponding to the vehicle to be selected is larger than or equal to the first threshold value and smaller than or equal to the second threshold value. It is understood that the possibility that the candidate vehicle travels in the same lane as the unmanned vehicle is not certain. Under the condition that whether the vehicle to be selected and the unmanned vehicle run on the same lane or not is uncertain, a track prediction unit in the unmanned vehicle is difficult to plan a correct driving track for the unmanned vehicle so as to ensure the driving safety.

In this embodiment, for each neighboring vehicle, if the first probability corresponding to the neighboring vehicle is greater than or equal to the first threshold and less than or equal to the second threshold, the neighboring vehicle is determined as the vehicle to be selected.

S310, if the distance between the vehicle to be selected and the unmanned vehicle is smaller than or equal to the preset distance in the at least one vehicle to be selected, the current first running speed of the unmanned vehicle is obtained.

After at least one candidate vehicle is determined in the plurality of adjacent vehicles based on the steps, whether the distance between the candidate vehicle and the unmanned vehicle is smaller than or equal to a preset distance in the at least one candidate vehicle is judged.

In a possible implementation manner, the distance between the unmanned vehicle and each candidate vehicle is detected by an on-board detector in the unmanned vehicle, so that the distance between at least one candidate vehicle and the unmanned vehicle can be obtained by the on-board detector.

Next, whether a vehicle to be selected exists within a preset distance from the unmanned vehicle is determined by judging the magnitude relation between the distance between the vehicle to be selected and the unmanned vehicle and the preset distance.

The first running speed is the running speed of the unmanned vehicle when the fact that the distance between the vehicle to be selected and the unmanned vehicle is smaller than or equal to the preset distance exists in at least one vehicle to be selected is determined.

In one possible implementation, a current first driving speed of the unmanned vehicle is acquired by a vehicle speed sensor.

S311, a longitudinal dynamic model of the unmanned vehicle is obtained, and the longitudinal dynamic model is used for indicating the relation among braking force, accelerator opening, running speed and acceleration.

It should be noted that each vehicle corresponds to its own longitudinal dynamics model. The longitudinal dynamic model is used for indicating the relation among the braking force, the accelerator opening, the running speed and the acceleration of the vehicle. Illustratively, the longitudinal dynamics model may be presented, for example, in tabular form.

In one possible implementation manner, a longitudinal dynamic model corresponding to the unmanned vehicle is obtained according to the model of the unmanned vehicle.

And S312, acquiring preset parameters, wherein the preset parameters comprise a first braking force and a first accelerator opening degree.

S313, determining a first acceleration corresponding to the unmanned vehicle according to the first running speed, the longitudinal dynamic model and the preset parameters.

Next, step S312 and step S313 will be described together.

In the embodiment, the inventor considers the driving thinking of simulating "neither brake nor accelerator is stepped on, and the like, and then determines how to drive next step after the driving intention of the adjacent vehicle is clear" when the human driving meets the unclear driving actual condition, and corrects the speed track of the unmanned vehicle in the future period. Therefore, the prediction parameters are acquired, where the first braking force is 0 and the first accelerator opening degree is 0. It is understood that, when it is determined that at least one candidate vehicle exists within the preset distance, the first braking force of the unmanned vehicle is controlled to 0 and the first throttle opening degree is 0, that is, the unmanned vehicle is allowed to coast forward at the current speed without braking and refueling.

In one possible implementation, a table corresponding to the longitudinal dynamics model is consulted to determine a first acceleration corresponding to the unmanned vehicle according to a first driving speed of the unmanned vehicle, a first braking force of 0 and an accelerator opening of 0.

And S314, determining a target speed track of the unmanned vehicle in a future period according to the first running speed and the first acceleration.

In the present embodiment, an implementation manner of determining the target speed trajectory of the unmanned vehicle in the future period according to the first running speed and the first acceleration may refer to the implementation manner in step S203.

And S315, determining the driving track of the unmanned vehicle according to the preset path track and the target speed track corresponding to the unmanned vehicle.

Step S315 is similar to the implementation of step S204, and is not described herein again.

The trajectory planning method provided by the embodiment of the application comprises the following steps: historical path trajectories of neighboring vehicles are obtained. And predicting the path tracks of the adjacent vehicles in a future period according to the historical path tracks of the adjacent vehicles, and determining at least one predicted path track of the adjacent vehicles. And determining the traveling probability of the adjacent vehicle in each lane in the future period according to at least one predicted path track of the adjacent vehicle. And determining the lane with the maximum driving probability as the target driving lane, and determining the driving probability corresponding to the target driving lane as the second probability. And determining a first lane in which the unmanned vehicle runs in a future period according to a preset path track corresponding to the unmanned vehicle. And judging whether the target driving lane of the adjacent vehicle is the same as the first lane or not. And if so, determining the second probability corresponding to the adjacent vehicle in the target driving lane as the first probability corresponding to the adjacent vehicle. If not, the first probability corresponding to the adjacent vehicle is determined to be 0. And determining at least one vehicle to be selected according to the first probability corresponding to the adjacent vehicles, wherein the first probability corresponding to the vehicle to be selected is greater than or equal to a first threshold value and is less than or equal to a second threshold value. And if the distance between the vehicle to be selected and the unmanned vehicle in the at least one vehicle to be selected is smaller than or equal to the preset distance, acquiring the current first running speed of the unmanned vehicle. And acquiring a longitudinal dynamic model of the unmanned vehicle, wherein the longitudinal dynamic model is used for indicating the relation among braking force, accelerator opening, running speed and acceleration. And acquiring preset parameters, wherein the preset parameters comprise a first braking force and a first accelerator opening degree. And determining a first acceleration corresponding to the unmanned vehicle according to the first running speed, the longitudinal dynamics model and the preset parameters. A target speed trajectory of the unmanned vehicle over a future period of time is determined based on the first travel speed and the first acceleration. And determining the driving track of the unmanned vehicle according to the preset path track and the target speed track corresponding to the unmanned vehicle.

Fig. 4 is a schematic structural diagram of a trajectory planning device according to an embodiment of the present application. As shown in fig. 4, the apparatus 40 includes: an acquisition module 401, a first determination module 402, a processing module 403, and a second determination module 404.

An obtaining module 401, configured to obtain a first probability corresponding to each of a plurality of neighboring vehicles, where the first probability is a probability that the neighboring vehicle travels in the same lane as the unmanned vehicle in a future period;

a first determining module 402, configured to determine at least one candidate vehicle according to first probabilities corresponding to the multiple neighboring vehicles, where the first probability corresponding to the candidate vehicle is greater than or equal to a first threshold and is less than or equal to a second threshold;

a processing module 403, configured to determine a target speed trajectory of the unmanned vehicle in the future time period if a distance between a candidate vehicle and the unmanned vehicle is smaller than or equal to a preset distance in the at least one candidate vehicle;

a second determining module 404, configured to determine a driving trajectory of the unmanned vehicle according to a preset path trajectory and the target speed trajectory that correspond to the unmanned vehicle.

In one possible design, the processing module 403 is specifically configured to:

acquiring a current first running speed of the unmanned vehicle;

determining a first acceleration corresponding to the unmanned vehicle according to the first running speed;

determining a target speed trajectory of the unmanned vehicle over the future time period based on the first travel speed and the first acceleration.

In one possible design, the processing module 403 is specifically configured to:

acquiring a longitudinal dynamic model of the unmanned vehicle, wherein the longitudinal dynamic model is used for indicating the relation between braking force, accelerator opening, running speed and acceleration;

acquiring preset parameters, wherein the preset parameters comprise a first braking force and a first accelerator opening degree;

and determining a first acceleration corresponding to the unmanned vehicle according to the first running speed, the longitudinal dynamics model and the preset parameters.

In one possible design, the method further comprises, for any one of the plurality of neighboring vehicles; the obtaining module 401 is specifically configured to:

acquiring predicted lane information of the adjacent vehicle in the future time period, wherein the predicted lane information comprises a target driving lane of the adjacent vehicle and a second probability corresponding to the target driving lane;

determining a first lane where the unmanned vehicle runs in the future time period according to a preset path track corresponding to the unmanned vehicle;

and determining a first probability corresponding to each of the plurality of adjacent vehicles according to the predicted lane information of the adjacent vehicles and the first lane.

In one possible design, the obtaining module 401 is specifically configured to:

judging whether the target driving lane of the adjacent vehicle is the same as the first lane or not;

if so, determining a second probability corresponding to the adjacent vehicle in the target driving lane as a first probability corresponding to the adjacent vehicle;

if not, determining a first probability corresponding to the adjacent vehicle as 0;

in one possible design, the obtaining module 401 is specifically configured to:

acquiring historical path tracks of the adjacent vehicles;

predicting the path tracks of the adjacent vehicles in the future period according to the historical path tracks of the adjacent vehicles, and determining at least one predicted path track of the adjacent vehicles;

predicting the traveling probability of the adjacent vehicle in each lane in the future period according to at least one predicted path track of the adjacent vehicle;

and determining the lane with the maximum driving probability as the target driving lane, and determining the driving probability corresponding to the target driving lane as the second probability.

The apparatus provided in this embodiment may be used to implement the technical solutions of the above method embodiments, and the implementation principles and technical effects are similar, which are not described herein again.

Fig. 5 is a schematic diagram of a hardware structure of a trajectory planning device provided in an embodiment of the present application, and as shown in fig. 5, a trajectory planning device 50 of the embodiment includes: a processor 501 and a memory 502; wherein

A memory 502 for storing computer-executable instructions;

the processor 501 is configured to execute the computer-executable instructions stored in the memory to implement the steps performed by the trajectory planning method in the above embodiments. Reference may be made in particular to the description relating to the method embodiments described above.

Alternatively, the memory 502 may be separate or integrated with the processor 501.

When the memory 502 is provided separately, the trajectory planning device further comprises a bus 503 for connecting the memory 502 and the processor 501.

An embodiment of the present application provides a computer-readable storage medium, where computer-executable instructions are stored in the computer-readable storage medium, and when a processor executes the computer-executable instructions, the trajectory planning method implemented by the above trajectory planning device is implemented.

An embodiment of the present application further provides a computer program product, where the program product includes: a computer program, stored in a readable storage medium, from which at least one processor of the electronic device can read the computer program, the at least one processor executing the computer program causing the electronic device to perform the solution provided by any of the embodiments described above.

In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described device embodiments are merely illustrative, and for example, the division of the modules is only one logical division, and other divisions may be realized in practice, for example, a plurality of modules may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or modules, and may be in an electrical, mechanical or other form.

The integrated module implemented in the form of a software functional module may be stored in a computer-readable storage medium. The software functional module is stored in a storage medium and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) or a processor (processor) to execute some steps of the methods according to the embodiments of the present application.

It should be understood that the Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.

The memory may comprise a high-speed RAM memory, and may further comprise a non-volatile storage NVM, such as at least one disk memory, and may also be a usb disk, a removable hard disk, a read-only memory, a magnetic or optical disk, etc.

The bus may be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended ISA (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, the buses in the figures of the present application are not limited to only one bus or one type of bus.

The storage medium may be implemented by any type or combination of volatile or non-volatile memory devices, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic or optical disks. A storage media may be any available media that can be accessed by a general purpose or special purpose computer.

Those of ordinary skill in the art will understand that: all or a portion of the steps of implementing the above-described method embodiments may be performed by hardware associated with program instructions. The program may be stored in a computer-readable storage medium. When executed, the program performs steps comprising the method embodiments described above; and the aforementioned storage medium includes: various media that can store program codes, such as ROM, RAM, magnetic or optical disks.

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 the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present application.