阅读说明：本技术 一种预测车辆运动轨迹的处理方法 (Processing method for predicting vehicle motion trail ) 是由 李伟欣 于 2021-08-31 设计创作，主要内容包括：本发明实施例涉及一种预测车辆运动轨迹的处理方法,所述方法包括：获取第一目标点位置坐标P、第一目标速度矢量V、第一预测时长L和第一预测间隔△t；计算N＝L/△t；在对N个预测点的运动轨迹参数进行顺序预测时,获取前一点预测参数组；对当前预测点的位置坐标进行预测；对当前预测点的行驶速度矢量进行预测；对当前预测点的后轮转向角进行预测；对当前预测点的前轮转向角进行预测；对第一个预测点的行驶加速度矢量进行预测；组成当前点预测参数组；由N个预测参数组构成车辆运动轨迹预测参数序列。通过本发明可以得到多种类型的运动轨迹预测结果,既可用于行为决策,还可用于轨迹规划和驾驶模拟。(The embodiment of the invention relates to a processing method for predicting a vehicle motion track, which comprises the following steps: acquiring a first target point position coordinate P, a first target speed vector V, a first prediction duration L and a first prediction interval delta t; calculating N as L/[ delta ] t; when the motion trail parameters of the N prediction points are sequentially predicted, acquiring a previous point prediction parameter set; predicting the position coordinates of the current prediction point; predicting the running speed vector of the current prediction point; predicting the steering angle of the rear wheel of the current prediction point; predicting the steering angle of the front wheel of the current prediction point; predicting the driving acceleration vector of the first prediction point; forming a current point prediction parameter set; and forming a vehicle motion track prediction parameter sequence by the N prediction parameter groups. The invention can obtain various types of motion track prediction results, and can be used for behavior decision, track planning and driving simulation.)

1. A processing method for predicting a vehicle motion trajectory, the method comprising:

acquiring a first target point position coordinate P, a first target speed vector V, a first prediction duration L and a first prediction interval delta t;

calculating the total number of the predicted points of the motion trail according to the first prediction duration L and the first prediction interval delta t to generate a first total number N, wherein N is L/deltat;

when the motion trail parameters of the N prediction points are sequentially predicted, acquiring a previous point prediction parameter set; the previous point prediction parameter group comprises a previous point position coordinate P_i-1Previous point running speed vector V_i-1The previous point running acceleration vector a_i-1Front wheel steering angle theta of front point_i-1And front point rear wheel steering angle alpha_i-1(ii) a The value of i is from 1 to N;

according to the position coordinate P of the previous point_i-1The previous point running speed vector V_i-1And the first prediction interval delta t, predicting the position coordinates of the current prediction point, and generating the corresponding position coordinates P of the current point_i，P_i＝P_i-1+V_i-1*Δt；

According to the previous point driving speed vector V_i-1The previous point running acceleration vector a_i-1And the first prediction interval delta t, predicting the running speed vector of the current prediction point, and generating a corresponding running speed vector V of the current point_i，V_i＝V_i-1+a_i-1*Δt；

According to a preset first wheel base W and the steering angle theta of the front wheel at the front point_i-1The previous point running speed vector V_i-1The front point rear wheel steering angle alpha_i-1And the first prediction interval delta t is used for predicting the rear wheel steering angle of the current prediction point to generate the corresponding rear wheel steering angle alpha of the current point_i；

Predicting the front wheel steering angle of the current predicted point according to the linear distance from the central point position coordinate of the rear axle of the vehicle body to the position coordinate P of the first target point and the included angle between the connecting line of the linear distance and the central line of the front axle and the central line of the rear axle of the vehicle body, and generating the corresponding front wheel steering angle theta of the current point_i；

According to the current point driving speed vector V_iAnd the first target speed vector V predicts the running acceleration vector of the first predicted point according to the preset running acceleration constraint condition to generate the corresponding running acceleration vector a of the current point_i；

From the current point position coordinates P_iThe current point running speed vector V_iThe current point rear wheel steering angle alpha_iThe current point front wheel steering angle theta_iAnd the current point running acceleration vector a_iForming a current point prediction parameter set; and forming a vehicle motion track prediction parameter sequence by the obtained prediction parameter groups of the N prediction points.

2. The processing method for predicting a vehicle motion trail according to claim 1, wherein in sequentially predicting the motion trail parameters of the first predicted point, the method further comprises:

the obtained previous point prediction parameter set is specifically a preset first initial point parameter set; said previous point position coordinate P_i-1Should be the position coordinates P of the first initial point preset in the first initial point parameter set₀(ii) a The previous point running speed vector V_i-1Should be a first initial point running speed vector V preset in the first initial point parameter set₀(ii) a The previous point travel acceleration vector a_i-1Should be a first initial point running acceleration vector a preset in the first initial point parameter set₀(ii) a The steering angle theta of the front wheel of the previous point_i-1The steering angle theta of the front wheel is a first initial point preset in the first initial point parameter set₀(ii) a The front point rear wheel steering angle alpha_i-1Should be a first initial point rear wheel steering angle alpha preset in the first initial point parameter set₀。

3. The method for processing the predicted vehicle motion trail according to claim 1, wherein said vehicle is driven by a preset first wheel base W and a steering angle θ of front wheels at the front point of the previous point_i-1The previous point running speed vector V_i-1The front point rear wheel steering angle alpha_i-1And the first prediction interval delta t is used for predicting the rear wheel steering angle of the current prediction point to generate the corresponding rear wheel steering angle alpha of the current point_iThe method specifically comprises the following steps:

according to the first axial distance W and the steering angle theta of the front wheel of the previous point_i-1Calculating the corresponding minimum turning radius r of the previous point_i-1，r_i-1＝W/tan(θ_i-1)；

According to the previous point driving speed vector V_i-1And the minimum turning radius r of the preceding point_i-1Calculating the corresponding steering angular velocity omega of the front point and the rear wheel_i-1，ω_i-1＝V_i-1/r_i-1；

According to the steering angle alpha of the front point and the rear wheel_i-1The steering angular velocity omega of the front point and the rear wheel_i-1And the first prediction interval Deltat, predicting the corresponding steering angle alpha of the rear wheel at the current point_i，α_i＝α_i-1+ω_i-1*Δt。

4. The method according to claim 1, wherein the front-wheel steering angle of the current predicted point is predicted according to a linear distance from a central point position coordinate of a rear axle of the vehicle body to a position coordinate P of the first target point and an included angle between a connecting line of the linear distance and the central point position coordinate P and a central line of a front axle and a central line of a rear axle of the vehicle body, so as to generate a corresponding current-point front-wheel steering angle θ_iThe method specifically comprises the following steps:

according to the position coordinates of the current pointP_iCalculating the position coordinates of the central points of the front and rear axles of the vehicle body according to preset central point offset information of the front and rear axles of the vehicle body to obtain the position coordinates of the central point of the front axle of the vehicle body and the position coordinates of the central point of the rear axle of the vehicle body;

calculating the linear distance from the position coordinate of the rear axle central point of the automobile body to the position coordinate P of the first target point, and generating the corresponding distance d between the current point and the target point_i；

Connecting the position coordinate of the central point of the rear axle of the automobile body with the position coordinate P of the first target point to generate a first connecting line; connecting the position coordinate of the central point of the rear axle of the vehicle body with the position coordinate of the central point of the front axle of the vehicle body to generate a second connecting line; calculating the included angle between the first connecting line and the second connecting line to generate a corresponding current point-target point included angle delta_i；

According to the first wheelbase W and the current point-target point distance d_iAnd the current point-target point included angle delta_iPredicting the front wheel steering angle of the current prediction point to generate the corresponding front wheel steering angle theta of the current point_i，

5. The method of claim 1, wherein the vector V is based on the current point driving speed_iAnd the first target speed vector V predicts the running acceleration vector of the first predicted point according to the preset running acceleration constraint condition to generate the corresponding running acceleration vector a of the current point_iThe method specifically comprises the following steps:

if the current point driving speed vector V_iIf the current point driving acceleration vector is smaller than the first target speed vector V, the current point driving acceleration vector a is set_iThe acceleration is a preset first acceleration;

if the current point driving speed vector V_iEqual to the first target speed vector V, the current point running acceleration vector a is set_iIs 0;

if the current point driving speed vector V_iIf the current point driving acceleration vector is larger than the first target speed vector V, the current point driving acceleration vector a is set_iIs a preset first deceleration acceleration.

6. An electronic device, comprising: a memory, a processor, and a transceiver;

the processor is used for being coupled with the memory, reading and executing the instructions in the memory to realize the method steps of any one of the claims 1-5;

the transceiver is coupled to the processor, and the processor controls the transceiver to transmit and receive messages.

7. A computer-readable storage medium having stored thereon computer instructions which, when executed by a computer, cause the computer to perform the method of any of claims 1-5.

Technical Field

The invention relates to the technical field of data processing, in particular to a processing method for predicting a vehicle motion trail.

Background

Trajectory prediction (trajectory prediction) is an important component of automated driving technology, and its function is to predict the movement trajectory of a vehicle in the future for a period of time, based on road conditions and maps. The predicted data richness directly influences the calculation duration and accuracy of downstream behavior decision and trajectory planning.

Disclosure of Invention

The present invention aims to provide a processing method, an electronic device and a computer readable storage medium for predicting a vehicle motion trail, which predict the position and the running speed of each predicted point under the condition of a given vehicle running target point, predict the steering angles of the front and rear wheels of the vehicle according to a kinematics bicycle model according to the principle of minimum turning radius and maximum front wheel steering angle, and perform constraint configuration on the acceleration of each point according to a set acceleration constraint condition, thereby obtaining a prediction data sequence containing multiple kinds of motion trail prediction information. The invention can not only provide the motion path prediction result of the minimum distance, but also provide the corresponding prediction results of braking control and steering control, and can be used for behavior decision, trajectory planning and driving simulation.

In order to achieve the above object, a first aspect of the embodiments of the present invention provides a processing method for predicting a motion trajectory of a vehicle, where the method includes:

acquiring a first target point position coordinate P, a first target speed vector V, a first prediction duration L and a first prediction interval delta t;

calculating the total number of the predicted points of the motion trail according to the first prediction duration L and the first prediction interval delta t to generate a first total number N, wherein N is L/deltat;

when the motion trail parameters of the N prediction points are sequentially predicted, acquiring a previous point prediction parameter set; the previous point prediction parameter group comprises a previous point position coordinate P_i-1Previous point running speed vector V_i-1The previous point running acceleration vector a_i-1Front wheel steering angle theta of front point_i-1And front point rear wheel steering angle alpha_i-1(ii) a The value of i is from 1 to N;

according to the position coordinate P of the previous point_i-1The previous point running speed vector V_i-1And the first prediction interval delta t, predicting the position coordinates of the current prediction point, and generating the corresponding position coordinates P of the current point_i，P_i＝P_i-1+V_i-1*Δt；

According to the previous point driving speed vector V_i-1The previous pointRunning acceleration vector a_i-1And the first prediction interval delta t, predicting the running speed vector of the current prediction point, and generating a corresponding running speed vector V of the current point_i，V_i＝V_i-1+a_i-1*Δt；

According to a preset first wheel base W and the steering angle theta of the front wheel at the front point_i-1The previous point running speed vector V_i-1The front point rear wheel steering angle alpha_i-1And the first prediction interval delta t is used for predicting the rear wheel steering angle of the current prediction point to generate the corresponding rear wheel steering angle alpha of the current point_i；

Predicting the front wheel steering angle of the current predicted point according to the linear distance from the central point position coordinate of the rear axle of the vehicle body to the position coordinate P of the first target point and the included angle between the connecting line of the linear distance and the central line of the front axle and the central line of the rear axle of the vehicle body, and generating the corresponding front wheel steering angle theta of the current point_i；

According to the current point driving speed vector V_iAnd the first target speed vector V predicts the running acceleration vector of the first predicted point according to the preset running acceleration constraint condition to generate the corresponding running acceleration vector a of the current point_i；

From the current point position coordinates P_iThe current point running speed vector V_iThe current point rear wheel steering angle alpha_iThe current point front wheel steering angle theta_iAnd the current point running acceleration vector a_iForming a current point prediction parameter set; and forming a vehicle motion track prediction parameter sequence by the obtained prediction parameter groups of the N prediction points.

Preferably, when sequentially predicting motion trajectory parameters of the first predicted point, the method further includes:

the obtained previous point prediction parameter set is specifically a preset first initial point parameter set; said previous point position coordinate P_i-1Should be the position coordinates P of the first initial point preset in the first initial point parameter set₀(ii) a The previous point running speed vector V_i-1Should be the first oneA first initial point running speed vector V preset in the initial point parameter set₀(ii) a The previous point travel acceleration vector a_i-1Should be a first initial point running acceleration vector a preset in the first initial point parameter set₀(ii) a The steering angle theta of the front wheel of the previous point_i-1The steering angle theta of the front wheel is a first initial point preset in the first initial point parameter set₀(ii) a The front point rear wheel steering angle alpha_i-1Should be a first initial point rear wheel steering angle alpha preset in the first initial point parameter set₀。

Preferably, the steering angle θ of the front wheel at the previous point according to the preset first wheel base W_i-1The previous point running speed vector V_i-1The front point rear wheel steering angle alpha_i-1And the first prediction interval delta t is used for predicting the rear wheel steering angle of the current prediction point to generate the corresponding rear wheel steering angle alpha of the current point_iThe method specifically comprises the following steps:

according to the first axial distance W and the steering angle theta of the front wheel of the previous point_i-1Calculating the corresponding minimum turning radius r of the previous point_i-1，r_i-1＝W/tan(θ_i-1)；

According to the previous point driving speed vector V_i-1And the minimum turning radius r of the preceding point_i-1Calculating the corresponding steering angular velocity omega of the front point and the rear wheel_i-1，ω_i-1＝V_i-1/r_i-1；

According to the steering angle alpha of the front point and the rear wheel_i-1The steering angular velocity omega of the front point and the rear wheel_i-1And the first prediction interval Deltat, predicting the corresponding steering angle alpha of the rear wheel at the current point_i，α_i＝α_i-1+ω_i-1*Δt。

Preferably, the front wheel steering angle of the current predicted point is predicted according to the linear distance from the position coordinate of the rear axle center point of the vehicle body to the position coordinate P of the first target point and the included angle between the connecting line of the two and the front and rear axle center lines of the vehicle body, so as to generate the corresponding front wheel steering angle theta of the current point_iThe method specifically comprises the following steps:

according toThe current point position coordinate P_iCalculating the position coordinates of the central points of the front and rear axles of the vehicle body according to preset central point offset information of the front and rear axles of the vehicle body to obtain the position coordinates of the central point of the front axle of the vehicle body and the position coordinates of the central point of the rear axle of the vehicle body;

calculating the linear distance from the position coordinate of the rear axle central point of the automobile body to the position coordinate P of the first target point, and generating the corresponding distance d between the current point and the target point_i；

Connecting the position coordinate of the central point of the rear axle of the automobile body with the position coordinate P of the first target point to generate a first connecting line; connecting the position coordinate of the central point of the rear axle of the vehicle body with the position coordinate of the central point of the front axle of the vehicle body to generate a second connecting line; calculating the included angle between the first connecting line and the second connecting line to generate a corresponding current point-target point included angle delta_i；

According to the first wheelbase W and the current point-target point distance d_iAnd the current point-target point included angle delta_iPredicting the front wheel steering angle of the current prediction point to generate the corresponding front wheel steering angle theta of the current point_i，

Preferably, the current point-based travel speed vector V_iAnd the first target speed vector V predicts the running acceleration vector of the first predicted point according to the preset running acceleration constraint condition to generate the corresponding running acceleration vector a of the current point_iThe method specifically comprises the following steps:

if the current point driving speed vector V_iIf the current point driving acceleration vector is smaller than the first target speed vector V, the current point driving acceleration vector a is set_iThe acceleration is a preset first acceleration;

if the current point driving speed vector V_iEqual to the first target speed vector V, the current point running acceleration vector a is set_iIs 0;

if the current point driving speed vector V_iGreater than the first target speedDegree vector V, then setting the current point running acceleration vector a_iIs a preset first deceleration acceleration.

A second aspect of an embodiment of the present invention provides an electronic device, including: a memory, a processor, and a transceiver;

the processor is configured to be coupled to the memory, read and execute instructions in the memory, so as to implement the method steps of the first aspect;

the transceiver is coupled to the processor, and the processor controls the transceiver to transmit and receive messages.

A third aspect of embodiments of the present invention provides a computer-readable storage medium storing computer instructions that, when executed by a computer, cause the computer to perform the method of the first aspect.

The embodiment of the invention provides a processing method for predicting a vehicle motion trail, electronic equipment and a computer readable storage medium, under the condition of giving a vehicle running target point, the position and the running speed of each predicted point are predicted, the steering angles of front wheels and rear wheels of a vehicle are predicted according to a kinematic bicycle model according to the principle of minimum turning radius and maximum front wheel steering angle, and the acceleration of each point is constrained and configured according to a set acceleration constraint condition, so that a predicted data sequence containing multiple kinds of motion trail prediction information is obtained. The invention not only provides the motion path prediction result of the minimum distance, but also provides the corresponding brake control and steering control prediction results, thereby not only providing data support for behavior decision, but also providing data support for trajectory planning and driving simulation.

Drawings

Fig. 1 is a schematic diagram of a processing method for predicting a vehicle motion trajectory according to an embodiment of the present invention;

fig. 2 is a schematic structural diagram of an electronic device according to a second embodiment of the present invention.

Detailed Description

In order to make the objects, technical solutions and advantages of the present invention clearer, the present invention will be described in further detail with reference to the accompanying drawings, and it is apparent that the described embodiments are only a part of the embodiments of the present invention, not all of the 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 invention.

As shown in fig. 1, which is a schematic diagram of a processing method for predicting a vehicle motion trail according to an embodiment of the present invention, the method mainly includes the following steps:

step 1, acquiring a first target point position coordinate P, a first target speed vector V, a first prediction duration L and a first prediction interval delta t.

Here, the position coordinate P of the first target point is the end position coordinate of the motion trajectory to be predicted currently; the first target speed vector V is the requirement of the position for the driving speed, for example, if the first target point position coordinate P is the lane position, the first target speed vector V may be set according to the maximum driving speed of the lane; the first predicted time length L is a preset time length and is used for identifying the total predicted time length of the motion trail of the vehicle; the first prediction interval Δ t is a preset time interval and is used for identifying the average interval duration between adjacent prediction points of the vehicle motion track prediction.

And 2, calculating the total number of the predicted points of the motion trail according to the first prediction duration L and the first prediction interval delta t to generate a first total number N, wherein N is L/delta t.

Here, the total prediction point number can be obtained by the total duration/interval duration.

Step 3, when sequentially predicting the motion trail parameters of the N prediction points, acquiring a previous point prediction parameter group;

wherein the previous point prediction parameter group comprises the previous point position coordinate P_i-1Previous point running speed vector V_i-1The previous point running acceleration vector a_i-1Front wheel steering angle theta of front point_i-1And front point rear wheel steering angle alpha_i-1(ii) a i takes values from 1 to N.

Here, when performing trajectory parameters for each predicted point on the vehicle motion trajectory, the embodiments of the present invention sequentially predict the predicted points in the order from front to back (from the 1 st to the nth), and the prediction for the current point is performed based on the prediction parameter set of the previous predicted point, that is, the previous prediction parameter set. The prediction parameter set for each prediction point includes five specific trajectory parameters: a predicted point position coordinate parameter, a predicted point running speed vector parameter, a predicted point running acceleration vector parameter, a predicted point front wheel steering angle parameter and a predicted point rear wheel steering angle parameter; the coordinate parameter of the predicted point position is a parameter for simulating and displaying a running path, the vector parameter of the predicted point running speed and the vector parameter of the predicted point running acceleration are parameters for controlling the vehicle speed, and the steering angle parameter of the front wheel of the predicted point and the steering angle parameter of the rear wheel of the predicted point are parameters for controlling the vehicle steering, namely the steering wheel.

It should be noted that, when sequentially predicting motion trajectory parameters of a first prediction point, the obtained previous point prediction parameter set is specifically a preset first initial point parameter set; corresponding, previous point position coordinate P_i-1Should be the position coordinates P of the first initial point preset in the first initial point parameter set₀(ii) a Previous point driving speed vector V_i-1Should be a first initial point running speed vector V preset in the first initial point parameter set₀(ii) a Acceleration vector a of previous point_i-1Should be a first initial point running acceleration vector a preset in a first initial point parameter set₀(ii) a Front wheel steering angle theta of front point_i-1The steering angle theta of the front wheel is a first initial point preset in the first initial point parameter set₀(ii) a Front point rear wheel steering angle alpha_i-1Should be a preset first initial point rear wheel steering angle alpha in the first initial point parameter set₀。

Step 4, according to the position coordinate P of the previous point_i-1Previous point running speed vector V_i-1And a first prediction interval delta t, predicting the position coordinates of the current prediction point to generate a corresponding current point positionSet coordinate P_i，P_i＝P_i-1+V_i-1*Δt。

Here, the previous point position coordinate P is known_i-1Previous point running speed vector V_i-1And the time from the previous point to the current prediction point, namely a first prediction interval delta t, and the position coordinate P of the previous point_i-1As a starting position, V_i-1Delta t is taken as relative displacement, and the position coordinate P of the ending position, namely the current point can be obtained through the initial position and the relative displacement_i。

Step 5, according to the previous point running speed vector V_i-1The previous point running acceleration vector a_i-1And a first prediction interval Deltat for predicting the running speed vector of the current prediction point to generate a corresponding running speed vector V of the current point_i，V_i＝V_i-1+a_i-1*Δt。

Here, the previous point running speed vector V is set_i-1As the initial speed, the previous point running acceleration vector a is set_i-1Taking the first prediction interval Δ t as the acceleration time as the acceleration, the current speed after the end of the acceleration time, that is, the current point running speed vector V, can be obtained by calculating from the initial speed + the acceleration x the acceleration time_i。

Step 6, according to the preset first wheel base W and the steering angle theta of the front wheel at the front point_i-1Previous point running speed vector V_i-1Front point rear wheel steering angle alpha_i-1And a first prediction interval delta t, predicting the rear wheel steering angle of the current prediction point, and generating the corresponding rear wheel steering angle alpha of the current point_i；

The method specifically comprises the following steps: step 61, according to the first axle distance W and the steering angle theta of the front wheel at the previous point_i-1Calculating the corresponding minimum turning radius r of the previous point_i-1，r_i-1＝W/tan(θ_i-1)；

Here, when the minimum turning radius of the previous point is predicted, the minimum turning radius prediction is performed according to the kinematic bicycle model; for the kinematic bicycle model, reference may be made to the related art, which is not further described herein;

step 62, according to the previous point running speed vector V_i-1And the minimum turning radius r of the previous point_i-1Calculating the corresponding steering angular velocity omega of the front point and the rear wheel_i-1，ω_i-1＝V_i-1/r_i-1；

Here, after the minimum turning radius of the preceding point is obtained, it is known that the angular velocity can be obtained by dividing the linear velocity by the radius, and then the angular velocity of the preceding point, that is, the front-rear-wheel steering angular velocity ω, can be obtained by dividing the preceding-point travel velocity vector by the minimum turning radius_i-1；

Step 63, according to the steering angle alpha of the front point and the rear wheel_i-1Front point and rear wheel steering angular velocity omega_i-1And a first prediction interval Deltat for predicting the corresponding current point rear wheel steering angle alpha_i，α_i＝α_i-1+ω_i-1*Δt。

Here, the front and rear wheels are steered at an angle α_i-1As an initial angle, let ω be_i-1Delta t is used as an increasing angle, and the current angle of the end position, namely the steering angle alpha of the rear wheel at the current point can be obtained through the initial angle and the increasing angle_i。

Step 7, predicting the front wheel steering angle of the current predicted point according to the linear distance from the position coordinate of the rear axle central point of the vehicle body to the position coordinate P of the first target point and the included angle between the connecting line of the two and the front and rear axle central lines of the vehicle body, and generating the corresponding front wheel steering angle theta of the current point_i；

Here, the maximum steering angle of the front wheel is predicted from the minimum distance between two points according to the kinematic bicycle model;

the method specifically comprises the following steps: step 71, according to the position coordinates P of the current point_iCalculating the position coordinates of the central points of the front axle and the rear axle of the vehicle body according to the preset central point offset information of the front axle and the rear axle of the vehicle body to obtain the position coordinates of the central point of the front axle and the central point of the rear axle of the vehicle body;

here, because the current point position coordinate P_iHas a certain offset relation with the center of the front and rear axle of the vehicle body, so the front and rear axle of the vehicle body needs to be calculated according to the corresponding offset relationCoordinates of the position of the center point; specifically, if the current point position coordinate P_iSetting the position of the center of mass of the vehicle, and calculating according to the offset relationship between the center of mass and the center point of the front and rear axles of the vehicle body; if the current point position coordinate P_iSetting the central point position of the rear axle of the vehicle body as P_iObtaining the position coordinates of the center point of the front axle of the vehicle body according to the center point offset of the front axle and the rear axle;

step 72, calculating the linear distance from the central point position coordinate of the rear axle of the vehicle body to the position coordinate P of the first target point, and generating the corresponding distance d between the current point and the target point_i；

Here, the current point-target point distance d_iThe minimum distance between the current prediction point and the target point is obtained;

step 73, connecting the position coordinate of the central point of the rear axle of the vehicle body with the position coordinate P of the first target point to generate a first connecting line; connecting the position coordinate of the central point of the rear shaft of the vehicle body with the position coordinate of the central point of the front shaft of the vehicle body to generate a second connecting line; calculating the included angle between the first connecting line and the second connecting line to generate a corresponding included angle delta between the current point and the target point_i；

Here, the current point-target point angle δ_iThe included angle between the target point and the central connecting line of the front axle and the rear axle of the vehicle relative to the central point of the rear axle of the vehicle is obtained;

step 74, according to the first wheelbase W and the current point-target point distance d_iAngle delta with current point-target point_iPredicting the front wheel steering angle of the current prediction point to generate the corresponding front wheel steering angle theta of the current point_i，

Here, according to the kinematic bicycle model principle, at a known current point-target point distance d_iAngle delta with current point-target point_iThe maximum steering angle of the front wheel of the vehicle at the current predicted point can be calculated according to the formula.

Step 8, according to the current point driving speed vector V_iAnd a first target speed vector V for the second time according to the preset running acceleration constraint conditionThe running acceleration vector of a predicted point is predicted to generate a corresponding running acceleration vector a of the current point_i；

Here, the driving acceleration constraint condition preset by the embodiment of the present invention is a piecewise function:

the method specifically comprises the following steps: step 81, if the current point driving speed vector V_iIf the current point driving acceleration vector is smaller than the first target speed vector V, the current point driving acceleration vector a is set_iThe acceleration is a preset first acceleration; turning to step 9;

here, when V_iLess than V, i.e. (V)_i-V)<When the speed is 0, the predicted running speed does not exceed the highest speed limit value of the target lane, and the target lane can be accelerated continuously, wherein the first acceleration is a preset acceleration parameter for acceleration, and is set to be 0.4 for example;

step 82, if the current point driving speed vector V_iEqual to the first target speed vector V, the current point running acceleration vector a is set_iIs 0; turning to step 9;

here, when V_iEqual to V, i.e. (V)_iWhen V) is 0, it means that the predicted driving speed has reached the highest speed limit of the target lane, and cannot be accelerated continuously, and it is sufficient to keep driving at a constant speed, so that the current point driving acceleration vector a is used_iSet to 0;

step 83, if the current point running speed vector V_iIf the current point driving acceleration vector is larger than the first target speed vector V, the current point driving acceleration vector a is set_iIs a preset first deceleration acceleration.

Here, when V_iGreater than V, i.e. (V)_i-V)>When 0, the predicted running speed exceeds the highest speed limit value of the target lane, and the road speed limit requirement is violated in both acceleration and constant speed running, so the speed is reduced, and the first deceleration acceleration is a preset acceleration parameter for deceleration and is set to be-1, for example.

Step 9, from the position coordinates P of the current point_iCurrent point running speed vector V_iSteering angle alpha of rear wheel at current point_iCurrent point front wheel steering angle theta_iAnd the current point running acceleration vector a_iForming a current point prediction parameter set; and forming a vehicle motion track prediction parameter sequence by the obtained prediction parameter groups of the N prediction points.

After the motion trail prediction is finished for each prediction point, a corresponding prediction parameter group is obtained; the prediction parameter sets from the 1 st to the Nth prediction points are sequenced in sequence, and the position coordinates P from the preset initial point, namely the position coordinates P of the first initial point can be obtained₀And starting to travel to a preset target point, namely a first target point position coordinate P, wherein the travel time length is a vehicle motion track information sequence of a first predicted time length L, namely a vehicle motion track predicted parameter sequence. The sequence includes N predicted location points, and five specific trajectory parameters at each predicted location point: the system comprises a predicted point position coordinate parameter, a predicted point running speed vector parameter, a predicted point running acceleration vector parameter, a predicted point front wheel steering angle parameter and a predicted point rear wheel steering angle parameter.

When the vehicle motion track prediction parameter sequence is used for track reproduction, an out-of-track route can be constructed according to the prediction point position coordinate parameter of each prediction position point; when the device is used for power control, the power output of the vehicle can be adjusted according to the predicted point running speed vector parameter and the predicted point running acceleration vector parameter of each predicted position point; when the method is used for steering control, the angle of a steering wheel or a steering control module of a vehicle can be adjusted according to the predicted point front wheel steering angle parameter and the predicted point rear wheel steering angle parameter of each predicted position point.

Fig. 2 is a schematic structural diagram of an electronic device according to a second embodiment of the present invention. The electronic device may be the terminal device or the server, or may be a terminal device or a server connected to the terminal device or the server and implementing the method according to the embodiment of the present invention. As shown in fig. 2, the electronic device may include: a processor 301 (e.g., a CPU), a memory 302, a transceiver 303; the transceiver 303 is coupled to the processor 301, and the processor 301 controls the transceiving operation of the transceiver 303. Various instructions may be stored in memory 302 for performing various processing functions and implementing the processing steps described in the foregoing method embodiments. Preferably, the electronic device according to an embodiment of the present invention further includes: a power supply 304, a system bus 305, and a communication port 306. The system bus 305 is used to implement communication connections between the elements. The communication port 306 is used for connection communication between the electronic device and other peripherals.

The system bus 305 mentioned in fig. 2 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The system bus may be divided into an address bus, a data bus, a control bus, and the like. For ease of illustration, only one thick line is shown, but this does not mean that there is only one bus or one type of bus. The communication interface is used for realizing communication between the database access device and other equipment (such as a client, a read-write library and a read-only library). The Memory may include a Random Access Memory (RAM) and may also include a Non-Volatile Memory (Non-Volatile Memory), such as at least one disk Memory.

The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), a Graphics Processing Unit (GPU), and the like; but also Digital Signal Processors (DSPs), Application Specific Integrated Circuits (ASICs), Field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.

It should be noted that the embodiment of the present invention also provides a computer-readable storage medium, which stores instructions that, when executed on a computer, cause the computer to execute the method and the processing procedure provided in the above-mentioned embodiment.

The embodiment of the present invention further provides a chip for executing the instructions, where the chip is configured to execute the processing steps described in the foregoing method embodiment.

The embodiment of the invention provides a processing method for predicting a vehicle motion trail, electronic equipment and a computer readable storage medium, under the condition of giving a vehicle running target point, the position and the running speed of each predicted point are predicted, the steering angles of front wheels and rear wheels of a vehicle are predicted according to a kinematic bicycle model according to the principle of minimum turning radius and maximum front wheel steering angle, and the acceleration of each point is constrained and configured according to a set acceleration constraint condition, so that a predicted data sequence containing multiple kinds of motion trail prediction information is obtained. The invention not only provides the motion path prediction result of the minimum distance, but also provides the corresponding brake control and steering control prediction results, thereby not only providing data support for behavior decision, but also providing data support for trajectory planning and driving simulation.

Those of skill would further appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.

The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied in hardware, a software module executed by a processor, or a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.