CN115257729A

CN115257729A - Vehicle trajectory planning method and device, computer equipment and storage medium

Info

Publication number: CN115257729A
Application number: CN202211016579.1A
Authority: CN
Inventors: 张佳桥
Original assignee: DeepRoute AI Ltd
Current assignee: DeepRoute AI Ltd
Priority date: 2022-08-24
Filing date: 2022-08-24
Publication date: 2022-11-01

Abstract

The present application relates to a vehicle trajectory planning method, apparatus, computer device, storage medium and computer program product. The method comprises the following steps: acquiring a current speed and a reference speed corresponding to at least one target traffic object; the target traffic object is a traffic object moving around the target vehicle, and the reference speed is determined based on historical moving environment information of the target traffic object; determining an abnormal traffic object from among the respective target traffic objects based on a speed difference between the reference speed and the current speed; generating motion loss based on the current speed and the reference speed corresponding to the abnormal traffic object, and generating the current efficiency influence degree corresponding to the abnormal traffic object based on the motion loss; and determining a target track from the candidate tracks corresponding to the target vehicle based on the current efficiency influence degree. By adopting the method, the efficiency evaluation time can be prolonged, so that the evaluation is more stable, and the accuracy of the evaluation of the track efficiency of the automatic driving vehicle is improved.

Description

Vehicle trajectory planning method and device, computer equipment and storage medium

Technical Field

The present application relates to the field of computer technologies, and in particular, to a vehicle trajectory planning method, apparatus, computer device, storage medium, and computer program product.

Background

With the development of computer technology, automated driving technology has emerged, which employs advanced communication, computer, network and control technologies to allow computer devices to automatically and safely operate vehicles without any human-active operations.

In the conventional art, when planning a trajectory for a vehicle, it is common to evaluate own information of the trajectory, for example, speed, length, time taken to evaluate candidate trajectories, and determine a target trajectory from a plurality of candidate trajectories based on the evaluation result. However, the target track is determined only based on the self-information of the track, the available information is limited, and the problem of inaccurate track planning exists.

Disclosure of Invention

In view of the above, it is necessary to provide a vehicle trajectory planning method, apparatus, computer device, computer readable storage medium and computer program product capable of improving the accuracy of trajectory planning.

The application provides a vehicle track planning method. The method comprises the following steps:

acquiring a current speed and a reference speed corresponding to at least one target traffic object; the target traffic object is a traffic object moving around a target vehicle, and the reference speed is determined based on historical moving environment information of the target traffic object;

determining an abnormal traffic object from among the respective target traffic objects based on a speed difference between the reference speed and the current speed;

generating a motion loss based on the current speed and the reference speed corresponding to the abnormal traffic object, and generating a current efficiency influence degree corresponding to the abnormal traffic object based on the motion loss;

determining an intermediate track corresponding to the abnormal traffic object from all candidate tracks corresponding to the target vehicle, and generating track efficiency information corresponding to the intermediate track based on the current efficiency influence degree;

determining a target trajectory from the respective candidate trajectories based on the trajectory efficiency information.

The application also provides a vehicle track planning device. The device comprises:

the speed acquisition module is used for acquiring the current speed and the reference speed corresponding to at least one target traffic object; the target traffic object is a traffic object moving around a target vehicle, and the reference speed is determined based on historical moving environment information of the target traffic object;

an abnormal traffic object determination module for determining an abnormal traffic object from the respective target traffic objects based on a speed difference between the reference speed and the current speed;

the current efficiency influence degree calculation module is used for generating motion loss based on the current speed and the reference speed corresponding to the abnormal traffic object and generating the current efficiency influence degree corresponding to the abnormal traffic object based on the motion loss;

the track efficiency information determining module is used for determining an intermediate track corresponding to the abnormal traffic object from each candidate track corresponding to the target vehicle and generating track efficiency information corresponding to the intermediate track based on the current efficiency influence degree;

and the target track determining module is used for determining a target track from the candidate tracks based on the track efficiency information.

A computer device comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the steps of the vehicle track planning method when executing the computer program.

A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned vehicle trajectory planning method.

A computer program product comprising a computer program which, when executed by a processor, carries out the steps of the vehicle trajectory planning method described above.

According to the vehicle track planning method, the vehicle track planning device, the computer equipment, the storage medium and the computer program product, the current speed and the reference speed corresponding to at least one target traffic object are obtained; the target traffic object is a traffic object moving around the target vehicle, and the reference speed is determined based on historical moving environment information of the target traffic object; determining an abnormal traffic object from among the respective target traffic objects based on a speed difference between the reference speed and the current speed; generating motion loss based on the current speed and the reference speed corresponding to the abnormal traffic object, and generating the current efficiency influence degree corresponding to the abnormal traffic object based on the motion loss; determining an intermediate track corresponding to the abnormal traffic object from the candidate tracks corresponding to the target vehicle, generating track efficiency information corresponding to the intermediate track based on the current efficiency influence degree, and determining the target track from the candidate tracks based on the track efficiency information. Therefore, the target traffic object is a traffic object moving around the target vehicle, the abnormal traffic object can be determined from the target traffic object based on the speed difference between the current speed corresponding to the target traffic object and the reference speed, the abnormal traffic object can cause a large influence on the running of the target vehicle, and the accuracy of track planning can be effectively improved by referring to the related information of the abnormal traffic object when the running track of the target vehicle is planned. Furthermore, motion loss can be generated based on the current speed and the reference speed corresponding to the abnormal traffic object, the current efficiency influence degree corresponding to the abnormal traffic object is generated based on the motion loss, the current efficiency influence degree can reflect the influence degree of the abnormal traffic object on the track efficiency of the running track of the target vehicle, track efficiency information of candidate tracks corresponding to the abnormal traffic object is generated based on the current efficiency influence degree, the target track is determined from all candidate tracks corresponding to the target vehicle based on the track efficiency information, and the accuracy of track planning can be effectively improved.

Drawings

FIG. 1 is a diagram of an exemplary vehicle trajectory planning method;

FIG. 2 is a schematic flow chart diagram of a vehicle trajectory planning method in one embodiment;

FIG. 3 is a schematic diagram illustrating an embodiment of determining whether a traffic object is a reference traffic object;

FIG. 4 is a speed change image of a traffic participant in one embodiment;

FIG. 5 is a schematic flow chart illustrating the determination of trajectory efficiency information corresponding to an intermediate trajectory in one embodiment;

FIG. 6 is a diagram illustrating an embodiment of determining whether an abnormal traffic object affects a trajectory;

FIG. 7 is a block diagram showing the structure of a vehicle trajectory planning apparatus according to an embodiment;

FIG. 8 is a diagram illustrating an internal structure of a computer device in one embodiment;

FIG. 9 is a diagram of an internal structure of a computer device in one embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of and not restrictive on the broad application.

The vehicle trajectory planning method provided by the embodiment of the application can be applied to the application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The data storage system may store data that the server 104 needs to process. The data storage system may be integrated on the server 104, or may be located on the cloud or other network server. The terminal 102 may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, internet of things devices, and portable wearable devices, and the internet of things devices may be smart televisions, smart car-mounted devices, and the like. The portable wearable device may be a smart watch, a smart headset, or the like. The server 104 may be implemented as a stand-alone server or a server cluster consisting of a plurality of servers or a cloud server. The terminal 102 and the server 104 may be directly or indirectly connected through wired or wireless communication, and the application is not limited thereto.

The terminal and the server can be independently used for executing the vehicle track planning method provided by the embodiment of the application.

For example, the terminal obtains a current speed and a reference speed corresponding to at least one target traffic object, and determines an abnormal traffic object from each target traffic object based on a speed difference between the reference speed and the current speed. The target traffic object is a traffic object moving around the target vehicle, and the reference speed is determined based on historical movement environment information corresponding to the target traffic object. The terminal generates motion loss based on the current speed and the reference speed corresponding to the abnormal traffic object, generates the current efficiency influence degree corresponding to the abnormal traffic object based on the motion loss, determines the middle track corresponding to the abnormal traffic object from the candidate tracks corresponding to the target vehicle, and generates track efficiency information corresponding to the middle track based on the current efficiency influence degree. The terminal determines a target trajectory from the candidate trajectories based on the trajectory efficiency information.

The terminal and the server can also be cooperatively used for executing the vehicle track planning method provided in the embodiment of the application.

For example, the terminal sends a vehicle track planning request to the server, the server obtains a current speed and a reference speed corresponding to at least one target traffic object, and abnormal traffic objects are determined from the target traffic objects based on a speed difference between the reference speed and the current speed. The target traffic object is a traffic object moving around the target vehicle, and the reference speed is determined based on historical movement environment information corresponding to the target traffic object. The server generates motion loss based on the current speed and the reference speed corresponding to the abnormal traffic object, generates current efficiency influence degree corresponding to the abnormal traffic object based on the motion loss, determines an intermediate track corresponding to the abnormal traffic object from each candidate track corresponding to the target vehicle, and generates track efficiency information corresponding to the intermediate track based on the current efficiency influence degree. The server determines a target trajectory from the candidate trajectories based on the trajectory efficiency information. And the server sends the target track to the terminal. The terminal can show the target track and can also drive according to the target track.

In one embodiment, the terminal 102 may be installed with an application that enables vehicle trajectory planning. The server 104 may be a backend server of a vehicle trajectory planning application installed in the terminal 102. In one embodiment, the terminal 102 is a target vehicle.

In an embodiment, as shown in fig. 2, a vehicle trajectory planning method is provided, which is exemplified by applying the method to a computer device, where the computer device may be a terminal or a server, and includes the following steps:

step S202, obtaining a current speed and a reference speed corresponding to at least one target traffic object; the target traffic object is a traffic object moving around the target vehicle, and the reference speed is determined based on historical moving environment information of the target traffic object.

The traffic object refers to an object that can move on a road, such as a pedestrian, a non-motor vehicle, a watering cart, a bus, a private car, and the like. The target vehicle is a vehicle needing trajectory planning. The target traffic object is a traffic object moving around the target vehicle, and for example, if the distance between the traffic object and the target vehicle is within a preset range, the traffic object is the target traffic object.

The current speed corresponding to the target traffic object is the speed of the target traffic object in the current time period, and can reflect the current motion condition of the target traffic object. The reference speed corresponding to the target traffic object is determined based on the historical motion environment information corresponding to the target traffic object, and can reflect the past motion situation of the target traffic object. The historical movement environment information is used for reflecting the movement environment of the target traffic object in the historical time period, and the historical movement environment information may include at least one of movement information corresponding to the surrounding movement objects of the target traffic object or traffic speed limit information corresponding to the target traffic object in the historical time period.

Specifically, when the computer device performs the trajectory planning on the target vehicle, the computer device may acquire traffic objects moving around the target vehicle as the target traffic objects, acquire a current speed and a reference speed corresponding to at least one target traffic object, and perform the trajectory planning with reference to the current speed and the reference speed corresponding to the target traffic object, so as to improve the accuracy of the trajectory planning.

In step S204, an abnormal traffic object is determined from the respective target traffic objects based on the speed difference between the reference speed and the current speed.

The abnormal traffic object refers to a target traffic object with a larger difference between the current motion situation and the historical motion situation.

Specifically, after acquiring the current speed and the reference speed of each target traffic object corresponding to the target vehicle, the computer device compares the current speed of the same target traffic object with the reference speed, and determines an abnormal traffic object from each target traffic object based on a speed difference between the reference speed and the current speed. For example, a target traffic object whose speed difference is greater than a preset difference may be determined as an abnormal traffic object. It can be understood that if the speed difference between the reference speed and the current speed is large, it indicates that the current motion situation and the historical motion situation of the target traffic object are large, and the target traffic object has an abnormal situation, such a target traffic object may have a certain influence on the driving of the target vehicle, and such a target traffic object may be used as an abnormal traffic object, and then the related information of the abnormal traffic object is referred to when the track planning is performed on the target vehicle, so that the accuracy of the track planning can be effectively improved.

And S206, generating motion loss based on the current speed and the reference speed corresponding to the abnormal traffic object, and generating the current efficiency influence degree corresponding to the abnormal traffic object based on the motion loss.

The motion loss refers to the loss caused by the abnormal traffic object traveling according to the current speed and the reference speed, and is used for reflecting the difference situation of the past motion and the present motion of the abnormal traffic object. The current efficiency influence degree is the influence degree of the abnormal traffic object on the track efficiency of the running track of the target vehicle, and is used for reflecting the influence degree of the abnormal traffic object on the track efficiency of the running track of the target vehicle.

Specifically, after determining the abnormal traffic object, the computer device may calculate a motion loss based on the current speed and the reference speed corresponding to the abnormal traffic object, for example, may calculate a speed difference between the current speed and the reference speed corresponding to the abnormal traffic object, and derive the motion loss based on the speed difference. Further, the computer device may calculate a current efficiency influence degree corresponding to the abnormal traffic object based on the motion loss. The computer device may use a variety of methods to derive the current degree of efficiency impact based on motion loss calculations. For example, motion loss may be directly taken as the current efficiency influence; the product of the motion loss and a preset value can be used as the current efficiency influence degree; and so on.

Step S208, determining an intermediate track corresponding to the abnormal traffic object from the candidate tracks corresponding to the target vehicle, and generating track efficiency information corresponding to the intermediate track based on the current efficiency influence degree.

Wherein, the candidate track refers to the running track which can be selected by the target vehicle. The intermediate trajectory refers to a candidate trajectory corresponding to the abnormal traffic object, and refers to a candidate trajectory that can be affected by the abnormal traffic object. The track efficiency information is used for representing the driving efficiency of the vehicle on the track and is used for representing the track efficiency of the driving track.

Specifically, after determining the current efficiency influence degree corresponding to the abnormal traffic object, the computer device may determine the target trajectory from the candidate trajectories corresponding to the target vehicle based on the current efficiency influence degree. First, the computer device may determine an intermediate trajectory corresponding to the abnormal traffic object from among the candidate trajectories corresponding to the target vehicle, for example, a candidate trajectory that coincides with a travel trajectory of the abnormal traffic object may be taken as the intermediate trajectory corresponding to the abnormal traffic object; the candidate track consistent with the motion direction of the abnormal traffic object can be used as a middle track corresponding to the abnormal traffic object; a candidate track which enables the absolute distance between the abnormal traffic object and the target vehicle to be smaller than the preset distance can be used as a middle track corresponding to the abnormal traffic object; and so on. Further, the computer device may generate trajectory efficiency information corresponding to the intermediate trajectory based on the current efficiency influence degree corresponding to the abnormal traffic object, for example, the current efficiency influence degree may be taken as the trajectory efficiency information; the current efficiency influence degree can be zoomed to obtain track efficiency information; and so on.

In step S210, a target trajectory is determined from the candidate trajectories based on the trajectory efficiency information.

The target trajectory refers to a travel trajectory finally determined by the target vehicle.

Specifically, after determining the trajectory efficiency information corresponding to the intermediate trajectory, the computer device may determine the target trajectory from the candidate trajectories based on the trajectory efficiency information, for example, if the smaller the trajectory efficiency information indicates the higher the trajectory efficiency, the candidate trajectory with the smallest trajectory efficiency information is taken as the target trajectory. After determining the target trajectory, the computer device may instruct the target vehicle to travel according to the target trajectory to improve the travel efficiency of the target vehicle.

It can be understood that, except for the intermediate trajectory, the trajectory efficiency information of the other candidate trajectories may be preset efficiency information, or may be calculated based on a custom formula or an algorithm.

In one embodiment, the candidate trajectories may be divided into a first-class trajectory and a second-class trajectory, where the first-class trajectory is an intermediate trajectory and refers to a candidate trajectory that may be affected by an abnormal traffic object, and the second-class trajectory refers to a candidate trajectory that may not be affected by an abnormal traffic object. The computer device can generate track efficiency information corresponding to the first type of track based on the current efficiency influence degree of the corresponding abnormal traffic object, and generate track efficiency information corresponding to the second type of track based on the track efficiency information corresponding to the first type of track, wherein the track efficiency indicated by the track efficiency information corresponding to the second type of track is higher than the track efficiency indicated by the track efficiency information corresponding to the first type of track. For example, the candidate tracks include a track a and a track B, where the track a is a first-type track and the track B is a second-type track, track efficiency information corresponding to the track a is generated based on a current efficiency influence degree corresponding to an abnormal traffic object that may influence the track a, and track efficiency information corresponding to the track B is generated based on the track efficiency information corresponding to the track a. If the smaller the track efficiency information is, the higher the track efficiency is, the track efficiency information corresponding to the track B should be smaller than the track efficiency information corresponding to the track a.

It can be understood that, compared with the first type of track, the target vehicle is not influenced by the abnormal traffic object when driving on the second type of track, so the track efficiency of the second type of track is higher than that of the first type of track.

In one embodiment, the target vehicle is an autonomous vehicle and the candidate trajectories are generated by a trajectory planning module of the autonomous vehicle. Candidate trajectories may be periodically generated for the autonomous vehicle, multiple candidate trajectories may be generated per planning cycle, and a target trajectory may be determined from the candidate trajectories based on trajectory efficiency information during each trajectory cycle. Further, in addition to the trajectory efficiency information, the target trajectory may be comprehensively determined from each candidate trajectory in combination with other information. For example, a target trajectory can be determined from various candidate trajectories based on trajectory efficiency information, trajectory safety information and trajectory comfort information, and trajectory evaluation can be comprehensively performed from the aspects of safety, comfort and efficiency to help a target vehicle make a final trajectory selection.

In the vehicle trajectory planning method, the current speed and the reference speed corresponding to at least one target traffic object are obtained; the target traffic object is a traffic object moving around the target vehicle, and the reference speed is determined based on historical moving environment information of the target traffic object; determining an abnormal traffic object from among the respective target traffic objects based on a speed difference between the reference speed and the current speed; generating motion loss based on the current speed and the reference speed corresponding to the abnormal traffic object, and generating the current efficiency influence degree corresponding to the abnormal traffic object based on the motion loss; determining a middle track corresponding to the abnormal traffic object from the candidate tracks corresponding to the target vehicle, and generating track efficiency information corresponding to the middle track based on the current efficiency influence degree, and determining a target track from each candidate track based on the track efficiency information. Therefore, the target traffic object is a traffic object moving around the target vehicle, the abnormal traffic object can be determined from the target traffic object based on the speed difference between the current speed corresponding to the target traffic object and the reference speed, the abnormal traffic object can cause a large influence on the running of the target vehicle, and the accuracy of track planning can be effectively improved by referring to the related information of the abnormal traffic object when the running track of the target vehicle is planned. Furthermore, motion loss can be generated based on the current speed and the reference speed corresponding to the abnormal traffic object, the current efficiency influence degree corresponding to the abnormal traffic object is generated based on the motion loss, the current efficiency influence degree can reflect the influence degree of the abnormal traffic object on the track efficiency of the running track of the target vehicle, track efficiency information of candidate tracks corresponding to the abnormal traffic object is generated based on the current efficiency influence degree, the target track is determined from each candidate track corresponding to the target vehicle based on the track efficiency information, and the accuracy of track planning can be effectively improved.

In one embodiment, step S202 includes:

acquiring a traffic object which has a distance with the current target traffic object within a target range in a historical time period and has the same movement direction with the current target traffic object as a reference traffic object corresponding to the current target traffic object; the target range is increased along with the increase of the historical speed of the current target traffic object in the historical time period, and no traffic blocking object exists between the current target traffic object and the reference traffic object; counting the movement speed of the reference traffic object in the historical time period to obtain a reference sub-speed corresponding to the current target traffic object in the historical time period; and obtaining the reference speed corresponding to the current target traffic object based on the reference sub-speed corresponding to the current target traffic object in the historical time period.

The current target traffic object refers to a currently processed target traffic object. The current target traffic object may be any target traffic object. The reference traffic object refers to a traffic object which is consistent with the movement direction of the target traffic object, moves within the target range of the target traffic object, and has no traffic blocking object with the target traffic object. The traffic blocking object refers to an object capable of blocking a traffic object, such as a traffic light, a sidewalk, and a stop sign.

Specifically, the computer device may count the movement speeds of the traffic objects moving around the target traffic object in the historical time period to generate the reference speed corresponding to the target traffic object.

First, the computer device may use a traffic object that is consistent with the movement direction of the current target traffic object, moves within the target range of the current target traffic object, and has no traffic blocking object between the current target traffic object and the current target traffic object as a reference traffic object corresponding to the current target traffic object. It is understood that when a traffic object around a target traffic object can be used as its reference traffic object, the following condition should be satisfied: 1. the absolute distance between the traffic objects around the target traffic object and the target traffic object cannot exceed a distance threshold, and the distance threshold is positively correlated with the historical speed of the target traffic object in the historical time period. For example, as shown in fig. 3 (a), when the absolute distance between the target traffic object and the surrounding traffic object of the target traffic object exceeds the distance threshold, it indicates that the surrounding traffic object is too far away from the current target traffic object to affect the current target traffic object, so that such surrounding traffic object cannot be used as the reference traffic object. 2. The target traffic object cannot be obstructed from the surrounding traffic object by the traffic obstructing object, for example, as shown in fig. 3 (b), the target traffic object cannot be obstructed from the surrounding traffic object by the red light, and if the target traffic object is behind the red light, the surrounding traffic object before the red light cannot be used as its reference traffic object. 3. The surrounding traffic objects of the target traffic object should have the same movement intention as the target traffic object. For example, as shown in fig. 3 (c), the movement of the target traffic object is intended to go straight ahead, and a surrounding traffic object turning to the right cannot be used as its reference traffic object. The reference traffic object of the target traffic object may be referred to as a reference flow around the target traffic object, and other surrounding traffic objects of the target traffic object may be referred to as non-reference flows around the target traffic object. In addition, at least one reference traffic object may exist for one target traffic object.

Furthermore, the computer device counts the movement speed of the reference traffic object in the historical time period, and obtains the reference sub-speed corresponding to the current target traffic object in the historical time period based on the movement speed of the reference traffic object in the historical time period, for example, the average value of the movement speed of each reference traffic object in the historical time period can be calculated as the reference sub-speed; the weighted average of the movement speeds of the reference traffic objects in the historical time period can be calculated to serve as the reference sub-speed, the weight corresponding to the reference traffic objects can be determined according to the distance between the reference traffic objects and the current target traffic objects, and the closer the distance is, the larger the weight is; and so on. It is understood that the computer device may obtain the movement speed of each traffic object on the road through a sensor provided on the target vehicle or a sensor provided at the road side or from other devices. As shown in fig. 4, the reference traffic objects existing around a certain target traffic object include: sprinkler, bus and non-motor vehicles, fig. 4 shows the speed of movement of each reference traffic object over a historical period of time. When the bus stops at a stop, the bus tends to decelerate to 0 and accelerate again. The sprinkler is usually driven at a near constant speed and kept at a low speed. Non-motor vehicles (such as bicycles) usually have large speed fluctuations, but the whole is kept running at a low speed.

Finally, the computer device may obtain the reference speed corresponding to the current target traffic object based on the reference sub-speed corresponding to the current target traffic object in the historical time period, for example, using the reference sub-speed as the reference speed.

In the above embodiment, the traffic object, which has the distance to the current target traffic object in the historical time period within the target range, is consistent with the motion direction of the current target traffic object, and has no traffic blocking object with the current target traffic object, is obtained as the reference traffic object corresponding to the current target traffic object. The traffic object which can obviously affect the target traffic object is obtained through various limiting conditions and is used as the reference traffic object, and the accuracy of determining the reference speed of the target traffic object is improved. The target range increases as the historical speed of the current target traffic object increases over the historical time period, the greater the speed of movement of the target traffic object, the larger the selection range of the reference traffic object is, the more the accuracy of determining the reference speed of the target traffic object is improved. And counting the movement speed of the reference traffic object in the historical time period to obtain the reference sub-speed of the current target traffic object in the historical time period, and obtaining the reference speed of the current target traffic object based on the reference sub-speed of the current target traffic object in the historical time period. Therefore, the historical speed of the reference traffic object can effectively reflect the speed of the traffic flow around the target traffic object, the reference speed of the current target traffic object is determined based on the movement speed of the reference traffic object in the historical time period, the accuracy of the reference speed can be effectively improved, and the accuracy of the track planning can be further improved.

In one embodiment, a vehicle trajectory planning method includes:

and when the current target traffic object does not have the reference traffic object in the historical time period, obtaining the reference sub-speed corresponding to the current target traffic object in the historical time period based on the traffic speed limit information of the current target traffic object in the historical time period.

The traffic speed limit information is used for limiting the movement speed of the traffic object, and specifically may include speed limit information brought by a road and objects on the road. For example, the traffic speed limit information includes legal speed limit of a road, and a speed limit by an object having road semantic information. The speed limit of the object with the road semantic information includes speed limit of a traffic light, deceleration of a stop sign, and the like.

Specifically, if the current target traffic object does not have the reference traffic object in the historical time period, the computer device may calculate the reference sub-speed according to the traffic speed limit information existing on the driving road where the current target traffic object is located in the historical time period, for example, a minimum value of each speed in the traffic speed limit information may be used as the reference sub-speed; sorting all speeds in the traffic speed limit information from small to large, and calculating the average value of at least two speeds at the front of the sorting as a reference sub-speed; and so on.

In the above embodiment, if no reference traffic object exists in the historical time period of the current target traffic object, in order to avoid that the reference sub-speed is null, the reference sub-speed of the current target traffic object may be calculated according to the traffic speed limit information existing on the road where the current target traffic object is located in the historical time period, and the traffic speed limit information may reflect the movement condition of the target traffic object from the side, and the reference sub-speed determined based on the traffic speed limit information also has certain accuracy, which is also beneficial to effectively improving the accuracy of trajectory planning.

In one embodiment, obtaining the reference speed corresponding to the current target traffic object based on the reference sub-speed corresponding to the current target traffic object in the historical time period includes:

acquiring reference sub-speeds corresponding to the current target traffic object in at least two historical time periods; generating a speed weight of a reference sub-speed corresponding to the historical time period based on the time difference between the historical time period and the current time period; the velocity weight decreases with increasing time difference; and fusing the reference sub-speeds based on the speed weight of each reference sub-speed to obtain the reference speed corresponding to the current target traffic object.

Specifically, in order to improve the accuracy of the reference speed, the computer device may acquire reference sub-speeds corresponding to the current target traffic object in at least two historical time periods, and perform weighted fusion on the respective reference sub-speeds to generate the reference speed corresponding to the target traffic object. The computer equipment acquires reference sub-speeds of the current target traffic object in at least two historical time periods respectively, and generates speed weights corresponding to the reference sub-speeds corresponding to the historical time periods according to the time difference between the historical time periods and the current time period. It is understood that the reference value of the data reflected by the history time period closer to the current time period is higher, and the reference value of the data reflected by the history time period farther from the current time period is lower, and thus, the speed weight may be decreased as the time difference increases. And then, the computer equipment carries out weighted average on each reference sub-speed based on the speed weight corresponding to each reference sub-speed to obtain the final reference speed of the target traffic object.

In one embodiment, the velocity weight may be calculated by the following formula:

N＝T/delta_t

t _i ＝t _O -i*delta_t

W _i ＝2*(N-i)/(N*(N+1))

wherein, T is the statistical duration of the reference speed, and delta _ T is the statistical period. E.g. statistics every 10 secondsOnce reference sub-velocities, the reference velocity is calculated from each of the reference sub-velocities of the past 5 minutes, then delta _ T is 10 seconds, and T is 5 minutes. N is the total time frame number, t ₀ Is the current time, t _i A past time, W, representing i statistical periods from the current time, for a past time _i Is t _i Corresponding velocity weight.

In one embodiment, the computer device calculates the speed of each reference traffic object corresponding to the target traffic object once every preset time interval, for example, the preset time interval may be 0.1s, and simultaneously calculates the average speed value of all the reference traffic objects as the reference sub-speed of the target traffic object. And the computer device counts the weighted average value of all the reference sub-speeds of the target traffic object in a certain past time period as the reference speed.

In the above embodiment, different speed weights are given to the reference sub-speeds corresponding to the historical time periods according to the difference between the historical time periods and the current time period, and the reference sub-speeds corresponding to the historical time periods closer to the current time period have higher speed weights, and the reference sub-speeds are weighted and averaged to obtain the final reference speed of the target traffic object.

In one embodiment, step S204 includes:

obtaining the speed difference proportion corresponding to each target traffic object based on the ratio of the speed difference corresponding to the same target traffic object to the reference speed; and taking the target traffic object with the speed difference proportion larger than the preset proportion as an abnormal traffic object.

The speed difference corresponding to the target traffic object is a difference value between a reference speed of the target traffic object and a current speed. The preset proportion refers to a threshold value of the speed difference proportion when whether the target traffic object is an abnormal traffic object is judged, and when the speed difference proportion is larger than the threshold value, the target traffic object is judged to be the abnormal traffic object. The preset proportion is a preset proportion threshold value, and can be specifically set according to actual needs.

Specifically, the computer device obtains a reference speed and a current speed corresponding to the target traffic object, calculates a ratio of a speed difference determined by the reference speed and the current speed to the reference speed as a speed difference ratio, and when the speed difference ratio corresponding to a certain target traffic object is greater than a preset ratio, indicates that the difference between the current motion situation and the historical motion situation of the target traffic object is large, so that the target traffic object is judged to be an abnormal traffic object.

In one embodiment, the abnormal traffic object may be determined by the following formula:

wherein, V _ref Is the reference speed, V, of the target traffic object ₁ Is the current speed of the target traffic object,

and T is a threshold value of the speed difference ratio when judging whether the target traffic object is an abnormal traffic object.

And when result is greater than 0, judging that the target traffic object is an abnormal traffic object.

In the above embodiment, whether the target traffic object is an abnormal traffic object is determined by comparing the ratio between the speed difference corresponding to the target traffic object and the reference speed with the preset ratio, so that whether the target traffic object is an abnormal traffic object can be determined quickly.

In one embodiment, generating the motion loss based on the current speed and the reference speed corresponding to the abnormal traffic object comprises:

calculating a first displacement corresponding to the abnormal traffic object based on the current speed and the preset time period corresponding to the abnormal traffic object, and calculating a second displacement corresponding to the abnormal traffic object based on the reference speed and the preset time period corresponding to the abnormal traffic object; based on the first displacement and the second displacement, a motion loss is generated.

The preset time period is a preset time period and can be set according to actual needs. In one embodiment, the preset time period may be a planning cycle of the vehicle trajectory. The first displacement refers to a distance that the abnormal traffic object can travel at the current speed within a preset time period. The second displacement refers to a distance that the abnormal traffic object can travel at the reference speed within a preset time period.

Specifically, the computer device obtains the current speed and the reference speed of the abnormal traffic object, calculates a first displacement and a second displacement obtained by the abnormal traffic object respectively driving for a preset time period according to the current speed and the reference speed, and finally generates the motion loss based on the first displacement and the second displacement. For example, the difference in position between the first displacement and the second displacement is taken as the loss of motion; taking the ratio of the position difference between the first displacement and the second displacement to the second displacement as the motion loss; and so on.

In the above embodiment, the first displacement corresponding to the abnormal traffic object is calculated based on the current speed and the preset time period corresponding to the abnormal traffic object, and the second displacement corresponding to the abnormal traffic object is calculated based on the reference speed and the preset time period corresponding to the abnormal traffic object; based on the first displacement and the second displacement, a motion loss is generated. In this way, the loss generated by the first displacement and the second displacement can reflect the difference between the past and present motion of the abnormal traffic object, and such motion loss helps to improve the accuracy of the subsequent calculation of the current efficiency influence degree.

In one embodiment, generating a current efficiency influence degree corresponding to the abnormal traffic object based on the motion loss includes:

acquiring the statistical times of the historical efficiency influence degree corresponding to the abnormal traffic object; generating a loss weight based on the statistical number; the loss weight increases along with the increase of the statistical times and approaches to a target value; based on the loss weight and the motion loss, a current efficiency influence degree is generated.

The historical efficiency influence degree refers to the efficiency influence degree of the abnormal traffic object calculated in the historical time period. The computer device can calculate the efficiency influence degree corresponding to the abnormal traffic object at regular time, and when the current efficiency influence degree is calculated, the efficiency influence degree obtained by the previous calculation is the historical efficiency influence degree. For example, the efficiency influence degree is calculated once for one abnormal traffic object in each trajectory period, and as the number of planning periods increases, the statistical number of the historical efficiency influence degrees also increases.

Specifically, when generating the current efficiency influence amount based on the motion loss, the accuracy of the current efficiency influence amount may be improved with reference to the number of times of calculation of the efficiency influence amount. After the motion loss corresponding to the abnormal traffic object is obtained, the computer device calculates the loss weight based on the statistical times of the historical efficiency influence degree corresponding to the abnormal traffic object. The loss weight increases as the number of statistics increases, that is, the loss weight becomes higher as the number of efficiency influence calculations increases, and the loss weight approaches the target value as the number of statistics increases, that is, the loss weight does not increase blindly and gradually becomes smooth and approaches the target value as the number of efficiency influence calculations increases. After the loss weight corresponding to the abnormal traffic object is obtained, the computer device calculates and obtains the current efficiency influence degree corresponding to the abnormal traffic object based on the loss weight and the motion loss. For example, the product of the loss weight and the motion loss is taken as the current efficiency influence degree; adding a constant value to the product of the loss weight and the motion loss to serve as the current efficiency influence degree; and so on.

In one embodiment, the motion loss and current efficiency impact may be calculated by the following equations:

wherein i-1 is the statistical number of the influence degree of the historical efficiency corresponding to the abnormal traffic object, J _i For loss of motion, V _ref Is a reference speed, V, of the target traffic object _i Is the current speed of the target traffic object. t is t _i Is a constant parameter, and is mainly determined by the time period of the predicted trajectory of the object (i.e., the trajectory planning period), e.g., t _i May be 5s. cost _i The degree of influence of the efficiency (which may also be referred to as the current degree of influence of the efficiency) calculated for the ith time corresponding to the abnormal traffic object, γ is a ratio of the historical efficiency influence degree and the current efficiency influence degree, and is a constant value, for example, γ may be a constant value of 0.5. It will be understood that the larger i, the cost _i The closer to J _i When i is gradually accumulated, cost _i The term will gradually approach to J _i The data is guaranteed to be smooth and not increase indefinitely as the accumulation increases over time.

In the above embodiment, the computer device generates the loss weight based on the statistical frequency of the historical efficiency influence degree corresponding to the abnormal traffic object, and performs weighting calculation on the loss weight and the motion loss to obtain the current efficiency influence degree, and when the statistical frequency is gradually accumulated, the current efficiency influence degree gradually tends to the motion loss, so that the current efficiency influence degree value is ensured to be smoother, and cannot be increased along with the increase of the accumulated frequency, the accuracy of the current efficiency influence degree is improved, and further, the accuracy of trajectory planning is effectively improved.

In one embodiment, as shown in fig. 5, step S208 includes:

step S502, determining corresponding intermediate tracks from all candidate tracks based on the current motion information of the abnormal traffic objects, and obtaining the intermediate tracks corresponding to all the abnormal traffic objects respectively.

Step S504, track efficiency information is generated based on the current efficiency influence degree of each abnormal traffic object corresponding to the same middle track, and track efficiency information corresponding to each middle track is obtained.

The current motion information refers to current motion information of the abnormal traffic object, and is used for reflecting the current motion condition of the abnormal traffic object. The current motion information may specifically include at least one of a travel track of the abnormal traffic object, a motion intention, whether or not a traffic blocking object is present with the target vehicle, and a distance with the target vehicle. The intermediate trajectory refers to a trajectory affected by an abnormal traffic object among the candidate trajectories of the target vehicle.

Specifically, the computer device obtains current motion information of all abnormal traffic objects corresponding to the target vehicle and candidate tracks of the target vehicle, and judges whether each abnormal traffic object affects the candidate tracks of the target vehicle. When the computer device judges that the abnormal traffic object affects one candidate track of the target vehicle, the candidate track is determined as a middle track corresponding to the abnormal traffic object. Therefore, when calculating the track efficiency information, the track efficiency information corresponding to one intermediate track is generated based on the current efficiency influence degree of each abnormal traffic object corresponding to the same intermediate track, and finally the track efficiency information corresponding to each intermediate track can be obtained. For example, for one intermediate trajectory, an average value of the current efficiency influence degrees of the abnormal traffic objects may be calculated as trajectory efficiency information; the maximum value in each current efficiency influence degree can be used as track efficiency information; and so on.

In one embodiment, the current motion information includes a travel track of the abnormal traffic object, a motion intention, whether or not there is a traffic blocking object with the target vehicle, a distance with the target vehicle, and the like. Judging that the abnormal traffic object does not influence the candidate track, and meeting the following conditions: 1. the travel track of the abnormal traffic object does not obstruct the travel track of the target vehicle, for example, as shown in fig. 6 (a), if the track of the target vehicle is a lane-changing detour or a detour front abnormal traffic object in the lane; 2. a traffic blocking object exists between the abnormal traffic object and the target vehicle, for example, as shown in fig. 6 (b), the abnormal traffic object and the target vehicle are blocked by a red light; 3. the movement intention of the abnormal traffic object is different from that of the target vehicle, for example, as shown in fig. 6 (c), the movement intention of the target vehicle is forward straight, and the movement intention of the abnormal traffic object is right turn; 4. the distance between the abnormal traffic object and the target vehicle is greater than the distance threshold, and the size of the distance threshold is in positive correlation with the speed of the target vehicle, for example, as shown in fig. 6 (d), the distance between the abnormal traffic object and the target vehicle is greater than the distance threshold.

In an embodiment, the computer device may use the trajectory efficiency information corresponding to the intermediate trajectory as the initial trajectory efficiency, perform normalization processing on the initial trajectory efficiency to obtain the target trajectory efficiency, and use the target trajectory efficiency as the trajectory efficiency information. The normalization process may be to determine a maximum value of the track efficiency from the initial track efficiencies, and use a ratio of the initial track efficiency and the maximum value of the track efficiency as the target track efficiency.

In the above embodiment, the computer device determines, based on the current motion information of all the abnormal traffic objects corresponding to the target vehicle, a trajectory affected by the abnormal traffic object in the candidate trajectories of the target vehicle as an intermediate trajectory, and calculates the trajectory efficiency information corresponding to the intermediate trajectory based on the current efficiency influence degree of all the abnormal traffic objects affecting the trajectory on the intermediate trajectory, so that the accuracy of the trajectory efficiency information corresponding to the intermediate trajectory can be effectively improved. The intermediate track is a track which can be influenced by abnormal traffic participants in the candidate tracks of the target vehicle, and track efficiency information of the intermediate track is referred when the running track of the target vehicle is planned, so that the accuracy of track planning can be effectively improved.

In a specific embodiment, the vehicle trajectory planning method can be applied in an automatic driving scenario.

The vehicle track planning method comprises the following steps:

1. calculating reference speeds of traffic participants (i.e., target traffic objects)

The reference sub-speed of each traffic participant is calculated periodically and all the reference sub-speeds calculated over a certain period of time are recorded. And if the reference traffic flow exists around the traffic participant, taking the average speed of the reference traffic flow as the reference sub-speed. And if the reference traffic flow does not exist around the traffic participant, calculating the reference sub-speed by combining the legal speed limit of the road and the speed limit brought by the object with the road semantic information. The final reference speed of the traffic participant is the result of weighted averaging of the reference sub-speeds over a period of time, so that the calculation can effectively reduce the fluctuation caused by the environment or the inaccuracy of the upstream data.

2. Determining abnormal traffic participants (i.e. abnormal traffic objects) from various traffic participants

The computer equipment acquires the current speed of the traffic participant and judges whether the target traffic object is an abnormal traffic object or not according to the following formula:

and when result is greater than 0, judging the traffic participant as an abnormal traffic participant.

3. Calculating the current efficiency influence degree corresponding to the abnormal traffic participants

The computer equipment acquires the current speed and the reference speed of the abnormal traffic participant, and calculates the current efficiency influence degree corresponding to the abnormal traffic participant through the following formula:

4. computing trajectory efficiency information for candidate trajectories

The computer device obtains a plurality of candidate tracks of the automatic driving vehicle, and judges whether the abnormal traffic participant has influence on the candidate tracks according to the running track and the movement intention of the abnormal traffic participant, whether a traffic blocking object exists between the abnormal traffic participant and the automatic driving vehicle, and the distance between the abnormal traffic participant and the automatic driving vehicle. The candidate trajectory to be affected by the abnormal traffic participant is taken as the intermediate trajectory. When one intermediate trajectory corresponds to only one abnormal traffic participant, the computer device takes the current efficiency influence degree of the abnormal traffic participant as the trajectory efficiency information of the intermediate trajectory, and when one intermediate trajectory corresponds to at least two abnormal traffic participants, the computer device takes the maximum value in the current efficiency influence degrees of the abnormal traffic participants as the trajectory efficiency information of the intermediate trajectory. The computer device determines track efficiency information corresponding to the remaining candidate tracks based on the track efficiency information of the intermediate tracks, wherein the track efficiency indicated by the track efficiency information corresponding to the remaining candidate tracks is higher than the track efficiency indicated by the track efficiency information corresponding to the intermediate tracks.

5. Determining a target trajectory from the candidate trajectories

The computer device determines a target trajectory of the autonomous vehicle based on the trajectory efficiency information for each candidate trajectory. Furthermore, the candidate tracks can be evaluated from multiple aspects such as safety, comfort, efficiency and the like, and the target track of the automatic driving vehicle can be determined. The autonomous vehicle travels according to the target trajectory.

The computer equipment can carry out traffic flow track planning at regular time and update the target track of the automatic driving vehicle at regular time.

In the embodiment, the computer device acquires and uses the movement speed, the movement intention and the movement environment information of each traffic participant in the historical time period when evaluating the track efficiency, so that the observation time of efficiency evaluation can be increased, and the evaluation is more accurate and stable. When the current efficiency influence degree of the abnormal traffic participants is calculated, the calculation idea of first-order low-pass filtering is referred, the calculation values at the current moment and the historical moment are weighted, the influence caused by environmental change, upstream data value fluctuation and the like is reduced, the obtained current efficiency influence degree value is smoother, and the vehicle track evaluation can be more stable and accurate.

It should be understood that, although the steps in the flowcharts related to the above embodiments are shown in sequence as indicated by the arrows, the steps are not necessarily performed in sequence as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least a part of the steps in the flowcharts related to the above embodiments may include multiple steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of performing the steps or stages is not necessarily sequential, but may be performed alternately or alternately with other steps or at least a part of the steps or stages in other steps.

Based on the same inventive concept, the embodiment of the application also provides a vehicle trajectory planning device for realizing the vehicle trajectory planning method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the method, so that specific limitations in one or more embodiments of the vehicle trajectory planning device provided below can be referred to the limitations of the vehicle trajectory planning method in the foregoing, and details are not repeated herein.

In one embodiment, as shown in fig. 7, there is provided a vehicle trajectory planning apparatus including: a speed acquisition module 702, an abnormal traffic object determination module 704, a current efficiency influence degree calculation module 706, a trajectory efficiency information determination module 708, and a target trajectory determination module 710, wherein:

a speed obtaining module 702, configured to obtain a current speed and a reference speed corresponding to at least one target traffic object; the target traffic object is a traffic object moving around the target vehicle, and the reference speed is determined based on historical moving environment information of the target traffic object.

And an abnormal traffic object determination module 704 for determining an abnormal traffic object from the respective target traffic objects based on a speed difference between the reference speed and the current speed.

And the current efficiency influence degree calculation module 706 is configured to generate a motion loss based on the current speed and the reference speed corresponding to the abnormal traffic object, and generate a current efficiency influence degree corresponding to the abnormal traffic object based on the motion loss.

The track efficiency information determining module 708 is configured to determine an intermediate track corresponding to the abnormal traffic object from the candidate tracks corresponding to the target vehicle, and generate track efficiency information corresponding to the intermediate track based on the current efficiency influence degree.

And a target track determining module 710 for determining a target track from the candidate tracks based on the track efficiency information.

According to the vehicle track planning device, the target traffic object is a traffic object moving around the target vehicle, the abnormal traffic object can be determined from the target traffic object based on the speed difference between the current speed corresponding to the target traffic object and the reference speed, the abnormal traffic object can cause a large influence on the running of the target vehicle, the related information of the abnormal traffic object is referred when the running track of the target vehicle is planned, and the track planning accuracy can be effectively improved. Furthermore, motion loss can be generated based on the current speed and the reference speed corresponding to the abnormal traffic object, the current efficiency influence degree corresponding to the abnormal traffic object is generated based on the motion loss, the current efficiency influence degree can reflect the influence degree of the abnormal traffic object on the track efficiency of the running track of the target vehicle, track efficiency information of candidate tracks corresponding to the abnormal traffic object is generated based on the current efficiency influence degree, the target track is determined from each candidate track corresponding to the target vehicle based on the track efficiency information, and the accuracy of track planning can be effectively improved.

In one embodiment, the speed acquisition module 702 is further configured to:

In one embodiment, the abnormal traffic object determination module 704 is further configured to:

In one embodiment, the current efficiency impact calculation module 706 is further configured to:

In one embodiment, the trajectory efficiency information determination module 708 is further configured to:

determining corresponding intermediate tracks from the candidate tracks based on the current motion information of the abnormal traffic objects to obtain the intermediate tracks corresponding to the abnormal traffic objects respectively; and generating track efficiency information based on the current efficiency influence degree of each abnormal traffic object corresponding to the same middle track to obtain the track efficiency information corresponding to each middle track.

The modules in the vehicle trajectory planning device may be wholly or partially implemented by software, hardware, or a combination thereof. The modules can be embedded in a hardware form or independent of a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.

In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 8. The computer device includes a processor, a memory, an Input/Output interface (I/O for short), and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer equipment is used for storing data such as reference speed, traffic speed limit information and the like corresponding to the target traffic object. The input/output interface of the computer device is used for exchanging information between the processor and an external device. The communication interface of the computer device is used for connecting and communicating with an external terminal through a network. The computer program is executed by a processor to implement a vehicle trajectory planning method.

In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 9. The computer apparatus includes a processor, a memory, an input/output interface, a communication interface, a display unit, and an input device. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The input/output interface of the computer device is used for exchanging information between the processor and an external device. The communication interface of the computer device is used for carrying out wired or wireless communication with an external terminal, and the wireless communication can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. The computer program is executed by a processor to implement a vehicle trajectory planning method. The display unit of the computer equipment is used for forming a visual and visible picture, and can be a display screen, a projection device or a virtual reality imaging device, the display screen can be a liquid crystal display screen or an electronic ink display screen, the input device of the computer equipment can be a touch layer covered on the display screen, a key, a track ball or a touch pad arranged on the shell of the computer equipment, and can also be an external keyboard, a touch pad or a mouse and the like.

Those skilled in the art will appreciate that the configurations shown in fig. 8 and 9 are merely block diagrams of some configurations relevant to the present disclosure, and do not constitute a limitation on the computing devices to which the present disclosure may be applied, and that a particular computing device may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.

In one embodiment, a computer device is further provided, which includes a memory and a processor, the memory stores a computer program, and the processor implements the steps of the above method embodiments when executing the computer program.

In an embodiment, a computer-readable storage medium is provided, in which a computer program is stored which, when being executed by a processor, carries out the steps of the above-mentioned method embodiments.

In one embodiment, a computer program product or computer program is provided that includes computer instructions stored in a computer-readable storage medium. The computer instructions are read by a processor of the computer device from a computer-readable storage medium, and the computer instructions are executed by the processor to cause the computer device to perform the steps of the above-described method embodiments.

It should be noted that, the user information (including but not limited to user equipment information, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data need to comply with the relevant laws and regulations and standards of the relevant country and region.

It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware related to instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include a Read-Only Memory (ROM), a magnetic tape, a floppy disk, a flash Memory, an optical Memory, a high-density embedded nonvolatile Memory, a resistive Random Access Memory (ReRAM), a Magnetic Random Access Memory (MRAM), a Ferroelectric Random Access Memory (FRAM), a Phase Change Memory (PCM), a graphene Memory, and the like. Volatile Memory can include Random Access Memory (RAM), external cache Memory, and the like. By way of illustration and not limitation, RAM can take many forms, such as Static Random Access Memory (SRAM) or Dynamic Random Access Memory (DRAM), among others. The databases referred to in various embodiments provided herein may include at least one of relational and non-relational databases. The non-relational database may include, but is not limited to, a block chain based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic devices, quantum computing based data processing logic devices, etc., without limitation.

All possible combinations of the technical features in the above embodiments may not be described for the sake of brevity, but should be considered as being within the scope of the present disclosure as long as there is no contradiction between the combinations of the technical features.

The above examples only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the present application. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A method of vehicle trajectory planning, the method comprising:

generating motion loss based on the current speed and the reference speed corresponding to the abnormal traffic object, and generating the current efficiency influence degree corresponding to the abnormal traffic object based on the motion loss;

determining an intermediate track corresponding to the abnormal traffic object from each candidate track corresponding to the target vehicle, and generating track efficiency information corresponding to the intermediate track based on the current efficiency influence degree;

2. The method of claim 1, wherein the obtaining the current speed and the reference speed corresponding to the at least one target traffic object comprises:

acquiring a traffic object which has a distance with a current target traffic object within a target range in a historical time period and has the same motion direction with the current target traffic object as a reference traffic object corresponding to the current target traffic object; the target range increases with an increase in the historical speed of the current target traffic object over the historical time period, there being no traffic blocking object between the current target traffic object and the reference traffic object;

counting the movement speed of the reference traffic object in the historical time period to obtain a reference sub-speed corresponding to the current target traffic object in the historical time period;

and obtaining the reference speed corresponding to the current target traffic object based on the reference sub-speed corresponding to the current target traffic object in the historical time period.

3. The method of claim 2, further comprising:

and when the current target traffic object does not have the reference traffic object in the historical time period, obtaining the reference sub-speed of the current target traffic object in the historical time period based on the traffic speed limit information of the current target traffic object in the historical time period.

4. The method of claim 2, wherein obtaining the reference speed corresponding to the current target traffic object based on the reference sub-speed corresponding to the current target traffic object in the historical time period comprises:

acquiring reference sub-speeds respectively corresponding to the current target traffic object in at least two historical time periods;

generating a speed weight of a reference sub-speed corresponding to the historical time period based on the time difference between the historical time period and the current time period; the velocity weight decreases with increasing time difference;

and fusing the reference sub-speeds based on the speed weight of each reference sub-speed to obtain the reference speed corresponding to the current target traffic object.

5. The method according to claim 1, wherein the determining of abnormal traffic objects from among the respective target traffic objects based on the speed difference between the reference speed and the current speed comprises;

obtaining the speed difference proportion corresponding to each target traffic object based on the ratio of the speed difference corresponding to the same target traffic object to the reference speed;

and taking the target traffic object with the speed difference proportion larger than the preset proportion as the abnormal traffic object.

6. The method of claim 1, wherein generating the motion loss based on the current speed and the reference speed corresponding to the abnormal traffic object comprises:

calculating a first displacement corresponding to the abnormal traffic object based on the current speed and the preset time period corresponding to the abnormal traffic object, and calculating a second displacement corresponding to the abnormal traffic object based on the reference speed and the preset time period corresponding to the abnormal traffic object;

generating the motion loss based on the first displacement and the second displacement.

7. The method of claim 1, wherein generating a current efficiency influence corresponding to the abnormal traffic object based on the motion loss comprises:

acquiring the statistical times of the historical efficiency influence degree corresponding to the abnormal traffic object;

generating a loss weight based on the statistics; the loss weight increases along with the increase of the statistical times and approaches to a target value;

generating the current efficiency influencing amount based on the loss weight and the motion loss.

8. The method according to claim 1, wherein the determining an intermediate trajectory corresponding to the abnormal traffic object from the candidate trajectories corresponding to the target vehicle, and generating trajectory efficiency information corresponding to the intermediate trajectory based on the current efficiency influence degree comprises:

determining corresponding intermediate tracks from the candidate tracks based on the current motion information of the abnormal traffic objects to obtain the intermediate tracks corresponding to the abnormal traffic objects respectively;

and generating track efficiency information based on the current efficiency influence degree of each abnormal traffic object corresponding to the same middle track to obtain the track efficiency information corresponding to each middle track.

9. A vehicle trajectory planning apparatus, characterized in that the apparatus comprises:

10. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the method of any of claims 1 to 8.