CN115366872A

CN115366872A - Vehicle steering obstacle avoidance method, device and storage medium

Info

Publication number: CN115366872A
Application number: CN202211193209.5A
Authority: CN
Inventors: 张世娟; 范泽华; 董德志
Original assignee: Uisee Shanghai Automotive Technologies Ltd
Current assignee: Uisee Shanghai Automotive Technologies Ltd
Priority date: 2022-09-28
Filing date: 2022-09-28
Publication date: 2022-11-22

Abstract

The embodiment of the invention discloses a vehicle steering obstacle avoidance method, equipment and a storage medium, wherein the method comprises the following steps: when determining that a collision risk exists between the target vehicle and the first obstacle and the target vehicle and the first obstacle meets the steering obstacle avoidance condition, acquiring a steering obstacle avoidance track for the target vehicle; acquiring the lateral speed of a target vehicle, constructing an improved constant rate of rotation and speed model according to the lateral speed, and estimating the improved constant rate of rotation and speed model to acquire final state information; and carrying out steering obstacle avoidance control on the target vehicle according to the steering obstacle avoidance track and the final state information. The lateral speed of a target vehicle is introduced to construct an improved transverse speed and speed model, and the final state information is obtained through the model, so that the obtained final state information is more accurate, steering obstacle avoidance control is carried out along a steering obstacle avoidance track obtained in advance according to the final state information, and accurate control over vehicle steering is achieved when collision risks occur.

Description

Vehicle steering obstacle avoidance method, device and storage medium

Technical Field

The embodiment of the invention relates to the technical field of unmanned driving, in particular to a vehicle steering obstacle avoidance method, equipment and a storage medium.

Background

With the development of automatic Driving perception algorithms and hardware computing power, more and more automobiles are equipped with ADAS (Advanced Driving Assistance System), of which FCW (Forward Collision Warning) and AEB (automatic Emergency braking) are the most representative.

However, the focus of the FCW and the focus of the AEB are in the longitudinal driving direction of the vehicle, and when a collision happens, a human-computer interface reminds a driver to brake or the vehicle to automatically brake so as to realize deceleration and avoidance of the vehicle. However, when the obstacle appears suddenly and the speed of the vehicle is too high, the FCW and the AEB can only realize partial deceleration and cannot realize real obstacle avoidance, and a higher damage risk still exists for the vehicle or the pedestrian.

Disclosure of Invention

The embodiment of the invention provides a vehicle steering obstacle avoidance method, equipment and a storage medium, which are used for realizing steering obstacle avoidance control on a vehicle.

In a first aspect, an embodiment of the present invention provides a vehicle steering obstacle avoidance method, including: when determining that a collision risk exists between a target vehicle and a first obstacle and the target vehicle and the first obstacle meets a steering obstacle avoidance condition, acquiring a steering obstacle avoidance track for the target vehicle;

the method comprises the steps of obtaining the lateral speed of a target vehicle, constructing an improved constant turning rate and speed model according to the lateral speed, and predicting the improved constant turning rate and speed model to obtain final state information, wherein the final state information comprises a vehicle position and a yaw angle;

and carrying out steering obstacle avoidance control on the target vehicle according to the steering obstacle avoidance track and the final state information.

In a second aspect, an embodiment of the present invention provides an electronic device, where the electronic device includes:

one or more processors;

a storage device for storing one or more programs,

when executed by one or more processors, cause the one or more processors to implement the method as described above.

In a third aspect, an embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored, where the computer program is configured to, when executed by a processor, implement the method as described above.

According to the technical scheme of the embodiment of the invention, the improved transverse speed and speed model is constructed by introducing the lateral speed of the target vehicle, the final state information is obtained through the model, so that the obtained final state information is more accurate, and the steering and obstacle avoidance control is carried out along the steering and obstacle avoidance track obtained in advance according to the final state information, so that when collision risks occur, the accurate control of the vehicle steering is realized.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.

Fig. 1 is a flowchart of a method for avoiding obstacles in vehicle steering according to an embodiment of the present invention;

FIG. 2 is a flow chart of a method for obtaining a lateral speed of a vehicle according to an embodiment of the invention;

FIG. 3 is a flow chart of another method for avoiding obstacles for steering a vehicle according to an embodiment of the present invention;

fig. 4 is a schematic structural diagram of a vehicle steering obstacle avoidance apparatus provided in an embodiment of the present invention;

fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.

Detailed Description

The present invention will be described in further detail with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention.

It should be further noted that, for the convenience of description, only some but not all of the relevant aspects of the present invention are shown in the drawings. Before discussing exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart may describe the operations (or steps) as a sequential process, many of the operations can be performed in parallel, concurrently or simultaneously. In addition, the order of the operations may be re-arranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figure. The processes may correspond to methods, software implementations, hardware implementations, and so on.

Fig. 1 is a flowchart of a vehicle steering obstacle avoidance method according to an embodiment of the present invention, where the present embodiment is applicable to a case where a vehicle is subjected to steering obstacle avoidance control, and the method may be executed by a vehicle steering obstacle avoidance apparatus according to an embodiment of the present invention, where the apparatus may be implemented in a software and/or hardware manner. As shown in fig. 1, the method specifically includes the following operations:

step S101, when determining that the collision risk exists between the target vehicle and the first obstacle and the target vehicle and the first obstacle meets the steering obstacle avoidance condition, acquiring a steering obstacle avoidance track aiming at the target vehicle.

Optionally, when it is determined that there is a collision risk between the target vehicle and the first obstacle and that the target vehicle and the first obstacle meet the steering obstacle avoidance condition, acquiring a steering obstacle avoidance trajectory for the target vehicle, including: acquiring operation parameters of the target vehicle, and calculating collision time according to the operation parameters and the first associated information, wherein the operation parameters comprise longitudinal speed and longitudinal acceleration; when the collision time is less than the preset time, determining that an obstacle avoidance risk exists between a target vehicle and a first obstacle, and acquiring a minimum braking obstacle avoidance distance and a minimum steering obstacle avoidance distance of the target vehicle; acquiring second associated information of a second obstacle with the distance between the second obstacle and the target vehicle within a preset range; and acquiring a steering obstacle avoidance track for the target vehicle when the steering obstacle avoidance condition is determined to be met according to the minimum braking obstacle avoidance distance, the minimum steering obstacle avoidance distance and the second associated information.

Optionally, when it is determined that a steering obstacle avoidance condition is met according to the minimum braking obstacle avoidance distance, the minimum steering obstacle avoidance distance, and the second correlation information, acquiring a steering obstacle avoidance trajectory for the target vehicle, includes: determining that a steering obstacle avoidance space exists according to the second associated information; and when the minimum steering obstacle avoidance distance is smaller than the minimum braking obstacle avoidance distance, acquiring a steering obstacle avoidance track of the target vehicle based on the steering obstacle avoidance space.

Specifically, the embodiment acquires the operation parameters of the target vehicle and the first associated information of the first obstacle, where the operation parameters specifically include a longitudinal speed and a longitudinal acceleration, and the first associated information specifically includes an obstacle type, an obstacle lateral distance, an obstacle longitudinal distance, an obstacle lateral acceleration, an obstacle longitudinal acceleration, and an obstacle width, and the longitudinal direction in the embodiment refers to a direction along a tangent of the turning obstacle avoidance trajectory and pointing to the vehicle traveling direction, and the lateral direction refers to a direction perpendicular to the longitudinal direction and pointing to the direction of the center of the turning obstacle avoidance trajectory. Therefore, the collision time of the target vehicle and the first obstacle can be calculated according to the longitudinal speed and the longitudinal acceleration of the target vehicle, the longitudinal distance of the first obstacle, the longitudinal acceleration of the first obstacle and other relevant information; if the collision time is more than or equal to the preset time, the automatic Emergency Steering obstacle Avoidance (AES) process is exited, namely, the AES automatic control is not performed on the target vehicle.

It should be noted that, when it is determined that an obstacle avoidance risk exists, in order to avoid collision between the target vehicle and the first obstacle, the target vehicle needs to be interfered in advance at a proper time, and therefore, other obstacles whose distance from the target vehicle is within a preset range are obtained, and the obstacles are marked as second obstacles, where the second related information may specifically include information such as an obstacle type, an obstacle lateral distance, an obstacle longitudinal distance, an obstacle lateral acceleration, an obstacle longitudinal acceleration, and an obstacle width of the second obstacle, and in addition, a minimum braking obstacle avoidance distance D1 and a minimum steering obstacle avoidance distance D2 of the target vehicle are also obtained, and therefore, when it is determined that a steering obstacle avoidance condition is met, a steering obstacle avoidance trajectory for the target vehicle is obtained finally according to the minimum braking obstacle avoidance distance, the minimum steering obstacle avoidance distance, and the second related information.

In the embodiment, the latest steering time of the target vehicle is determined according to the related information such as the obstacle width, the obstacle transverse distance, the obstacle transverse speed and the like of the first obstacle, and the minimum steering and obstacle avoidance distance D2 of the target vehicle is determined based on the longitudinal speed of the target vehicle, the obstacle longitudinal speed and the latest steering time of the first obstacle and the preset system response time. In addition, a minimum braking distance D1 corresponding to the target vehicle automatic emergency braking AEB decision is also obtained, and D1 may be specifically set in advance by the AEB decision, and the specific setting mode of the minimum braking distance D1 is not limited in this embodiment.

It is worth mentioning that, in this embodiment, when it is determined that the steering obstacle avoidance space exists according to the second related information and the minimum steering obstacle avoidance distance D2 is smaller than the minimum braking obstacle avoidance distance D1, it is further determined whether there is a situation where the driver takes over the target vehicle, if no operation instruction of the driver is received, it is determined that the driver does not take over the target vehicle, at this time, the steering obstacle avoidance direction is determined according to the steering obstacle avoidance space, and the steering obstacle avoidance trajectory is determined based on the steering obstacle avoidance direction and the preset acceleration. The invention carries out steering obstacle avoidance according to the condition that no driver takes over, and when the driver takes over, the automatic driving system quits the automatic control, and the driver operates the automatic driving vehicle. The steering obstacle avoidance direction comprises a leftward direction or a rightward direction, and specifically means that a target vehicle achieves the purpose of avoiding a first obstacle by steering leftwards or changing lanes leftwards; or the target vehicle makes a right turn or changes lane to avoid the first obstacle. If it is determined that only the left side of the target vehicle has the steering obstacle avoidance space, the steering obstacle avoidance direction is leftward; if it is determined that only the right side of the target vehicle has the steering obstacle avoidance space, the steering obstacle avoidance direction is rightward; and if the left side and the right side of the target vehicle are determined to have the steering obstacle avoidance space, determining the steering obstacle avoidance direction according to the relative transverse distance between the target vehicle and the first obstacle. In addition, the preset lateral acceleration is a lateral acceleration of the target vehicle when turning, and is an acceleration set with reference to a coordinate system of the target vehicle. The constraint of the preset lateral acceleration is added into the steering obstacle avoidance edge track, so that the stability of the target vehicle in the emergency steering process can be fundamentally ensured, and the aim of improving the safety of the vehicle is fulfilled. In the embodiment of the present disclosure, a fifth-order polynomial is selected as a trajectory equation of AES, that is, the trajectory equation of AES is an equation shown in the following expression (1):

wherein x represents a longitudinal distance, y represents a transverse distance, y _e Represents the transverse displacement (namely the transverse offset distance required by obstacle avoidance) of the target vehicle to complete the steering lane change process, x _e Indicating the longitudinal displacement of the target vehicle to complete the steer-change process.

During the process that the target vehicle turns to avoid the first obstacle, assuming that the longitudinal speed of the target vehicle is unchanged, the longitudinal distance x and the longitudinal displacement xe can be converted into time amounts t and te, specifically, x = Vx × t and xe = Vx × te, wherein Vx represents the longitudinal speed of the target vehicle, and te represents the time required for the target vehicle to complete turning to avoid the obstacle. Based on expression (1), a set trajectory equation shown in the following expression (2) can be obtained:

wherein t is a first variable and represents time, te is a first coefficient and represents time required by the target vehicle to finish steering and obstacle avoidance, and ye is a second coefficient and represents a lateral offset distance required by obstacle avoidance.

The second derivative is obtained by solving the expression (2) to obtain a relation expression of the lateral acceleration of the target vehicle along with the time in the whole steering obstacle avoidance process, namely the second derivative is calculated as the following expression (3):

wherein, a _y (t) a solving equation for determining the maximum value of the lateral acceleration based on the second derivative equation, which represents the lateral acceleration; and determining the preset lateral acceleration as the maximum value of the lateral acceleration, and determining a first coefficient in a solving equation according to the preset lateral acceleration. And substituting the determined numerical value of the first coefficient into a set track equation to obtain a steering obstacle avoidance track L.

And S102, acquiring the lateral speed of the target vehicle, constructing an improved constant slew rate and speed model according to the lateral speed, and estimating the improved constant slew rate and speed model to acquire final state information.

The final state information includes a vehicle position and a yaw angle.

Optionally, an improved constant slew rate and speed model is constructed according to the lateral speed, and the improved constant slew rate and speed model is estimated to obtain final state information, where the method includes: updating the transverse position according to the lateral speed, and determining second historical state information according to the updated transverse position, wherein the second historical state information comprises the transverse position, the longitudinal position, a yaw angle, a longitudinal speed and a yaw angular speed; constructing an improved constant conversion rate and speed model according to the second historical state information, and predicting the improved constant conversion rate and speed model to obtain second initial state information; and correcting the second initial state information according to the measured longitudinal speed and the measured yaw rate to acquire final state information.

Specifically, in the present embodiment, the lateral velocity v at the time T is acquired _y Then, the transverse position is updated according to the lateral speed

And determining second historical state information according to the updated transverse position, wherein the second historical state information comprises the transverse position, the longitudinal position, the yaw angle, the longitudinal speed and the yaw rate, and the second historical state information can be specifically at the time T adjacent to the current time T +1, and constructing an improved constant rotation rate and speed model according to the second historical state information.

Illustratively, as shown in fig. 2, specifically describing the obtaining of the lateral speed of the target vehicle, the method specifically includes the following steps:

in step S201, first history state information is acquired.

The first history state information includes a lateral velocity and a yaw rate, and the first history state information may be acquired at time T-1 in this embodiment.

Step S202, a vehicle dynamic model is built according to the first historical state information, and the vehicle dynamic model is estimated to obtain first initial state information.

Specifically, in the present embodiment, a vehicle dynamics model is constructed according to the first historical state information, and the following formula (4) is an example of the constructed vehicle dynamics model:

the state quantity in the vehicle dynamics model is Y = [ v ] _y ω] ^T Wherein the measured quantity is a yaw angular velocity omega, and the predicted quantity is a lateral velocity v _y According to the vehicle dynamics model, a Kalman filtering algorithm is adopted for prediction to obtain first initial state information

Wherein A represents a first parameter of the vehicle dynamics model and B represents a second parameter of the vehicle dynamics model.The state quantity of the next moment can be estimated by using the state quantity of the previous moment through a Kalman filtering algorithm, so that when the first historical state information is T-1 moment, the first initial state information of the T moment can be estimated by using the first historical state information.

Step S203, correcting the first initial state information according to the measured yaw rate, and acquiring the corrected first initial state information, wherein the corrected first initial state information includes a lateral speed.

Optionally, the correcting the first initial state information according to the measured yaw rate to obtain the corrected first initial state information includes: acquiring a first covariance matrix corresponding to the first initial state information, and a first measurement matrix and a first noise matrix corresponding to the measured yaw rate; calculating a first Kalman gain according to the first covariance matrix, the first measurement matrix and the first noise matrix; and correcting the first initial state information according to the measured yaw velocity, the first Kalman gain and the first measurement matrix, and acquiring the corrected first initial state information.

Specifically, in this embodiment, the following formula (5) is used to obtain the covariance matrix corresponding to the first initial state information:

M _k+1 ＝A*M _k *A ^T +F (5)

wherein M is _k+1 A first covariance matrix, M, representing the correspondence of the first initial state information _k And the covariance matrix corresponding to the historical state information is represented, A represents a first parameter of a vehicle dynamic model, and F represents a covariance matrix of predicted noise, which is a specified matrix of 2*2.

Note that, since the measurement amount in the present embodiment is the yaw rate ω, the first measurement matrix S = [ 01 ] corresponding to the measured yaw rate]A first noise matrix W = set _ noise _ W ^2 and according to the first covariance matrix M _k+1 A first measurement matrix S and a first noise matrix W, a first kalman gain is calculated using the following equation (6):

K _k1 ＝M _k+1 S ^T (SM _k+1 S ^T +W) ^-1 (6)

the state quantity measured by the sensor is as follows n _k+1 ＝[w]And then, according to the measured state quantity, the first kalman gain and the first measurement matrix, correcting the first initial state information according to the following formula (7), and acquiring the corrected first initial state information:

Y′ _K+1 ＝Y _K+1 +K _k1 (n _k+1 -SY _K+1 ) (7)

wherein, Y' _K+1 Indicating the first initial state information, Y, obtained after correction _K+1 Representing first initial state information, K _k1 Representing a first Kalman gain, n _k+1 Representing the measured yaw rate, S represents a first measurement matrix, where Y' _K+1 Including lateral velocities, which may be

And the first initial state information at this time may specifically be at the last time, that is, at time T.

On the basis of acquiring the lateral speed, an improved constant rotation rate and speed model can be constructed according to the lateral speed, and the following formula (8) is an example of the improved constant rotation rate and speed model:

the state quantity in the improved constant rotation rate and speed model is

Wherein the measured quantity is the longitudinal velocity v _x And yaw rate ω, pre-measured as longitudinal position x, lateral position y, and yaw angle

And performing prediction on the improved constant rotation rate and speed model by adopting a Kalman filtering algorithm to obtain second initial state information X (k + 1).

Optionally, the correcting the second initial state information according to the measured longitudinal velocity and the measured yaw rate to obtain the final state information includes: acquiring a second covariance matrix corresponding to second initial state information, and a second measurement matrix and a second noise matrix corresponding to the measured longitudinal velocity and the measured yaw velocity; calculating a second Kalman gain according to the second covariance matrix, the second measurement matrix and the second noise matrix; and correcting the second initial state information according to the measured longitudinal speed and yaw angular speed, the second Kalman gain and the second measurement matrix to obtain final state information.

Specifically, for each term in equation (8)

And respectively solving the partial derivatives of each state variable to form a Jacobian matrix J as follows:

specifically, in this embodiment, the obtained jacobian matrix J is used to obtain a second covariance matrix corresponding to the second initial state information by using the following formula (10):

P _k+1 ＝J*P _k *J ^T +Q (10)

wherein, P _k+1 Representing a second covariance matrix corresponding to the second initial state information, J representing a Jacobian matrix, Q being a covariance matrix of the predicted noise, P _k And representing the covariance matrix corresponding to the second historical state information.

It should be noted that the introduction of noise in the improved constant slew rate and speed models mainly comes from two points: linear acceleration u _a And yaw acceleration

And (3) noise. The effect of these two acceleration metrics on the state variables is expressed as follows:

assuming linear acceleration u _a And yaw angular acceleration

The mean of the noise is 0 and the variance is

These two quantities are set in the model.

The covariance matrix Q of the predicted noise can therefore be expressed by the following equation (13):

Q＝E[(X-E(X)·(X-E(X) ^T )]＝E[Guu ^T G ^T ]＝G·E[uu ^T ]·G ^T (13)

improving the longitudinal velocity v of a target vehicle in a constant slew and velocity model _x The yaw rate ω, and thus a second measurement matrix corresponding to the measured longitudinal and yaw rates

The measurement noise mainly originates from a velocity measurement error and a yaw-rate measurement error. set _ noise _ v =0.02; set _ noise _ w =0.01, so the second noise matrix

And according to the second covariance matrix P _k+1 A second measurement matrix H and a second noise matrix R, a second kalman gain is calculated using the following equation (14):

K _k2 ＝P _k+1 H ^T (HP _k+1 H ^T +R) ^-1 (14)

the state quantity measured by the sensor is as follows z _k+1 ＝[v _x w]And then, according to the measured state quantity, the second kalman gain and the second measurement matrix, correcting the second initial state information according to the following formula (15), and acquiring final state information:

X′ _K+1 ＝X _K+1 +K _k2 (z _k+1 -HX _K+1 ) (15)

wherein, X' _K+1 Indicating the final state information, X, obtained after correction _K+1 Indicating second initial state information, K _k Representing the second Kalman gain, z _k+1 Representing the measured longitudinal and yaw rates, and H represents a second measurement matrix.

And S103, carrying out steering obstacle avoidance control on the target vehicle according to the steering obstacle avoidance track and the final state information.

Optionally, the steering obstacle avoidance control of the target vehicle according to the steering obstacle avoidance trajectory and the final state information includes: determining a target point position which is closest to the target vehicle position on the steering obstacle avoidance track; determining a transverse deviation according to the position of the target point and the current vehicle position, and determining a course deviation according to the current yaw angle; and carrying out steering obstacle avoidance control on the target vehicle according to the transverse deviation and the course deviation.

Specifically, the vehicle position (x, y) and the yaw angle are included in the final state information

Therefore, the target vehicle is estimated by improving the constant rotation rate and speed model, and the real-time estimated final state information is obtained

And after turning to and keeping away barrier orbit L, will turn to and keep away barrier control to the target vehicle, the vehicle turns to and keeps away the barrier control algorithm and includes: stant Li Suanfa Stanley, linear quadratic optimal control algorithm LQR, model predictive control algorithm MPC, pure tracking Pure Pursuit etc., and the specific type of the vehicle steering obstacle avoidance control algorithm is not limited in this embodiment.

In a specific implementation, when the Stanley algorithm is adopted, a target point position closest to the target vehicle position is determined on a steering obstacle avoidance track according to the target vehicle position, the target point position and the current target vehicle position are calculated to determine a transverse deviation e, and a course deviation theta is calculated according to the target vehicle yaw angle, so that the rotation angle control is performed by adopting the Stanley algorithm according to the transverse deviation e and the course deviation theta, and the steering obstacle avoidance control of the target vehicle is realized.

According to the technical scheme of the embodiment of the invention, the lateral speed of the target vehicle is introduced to construct the improved transverse speed and speed model, and the final state information is obtained through the model, so that the obtained final state information is more accurate, and the steering and obstacle avoidance control is carried out in real time along the pre-obtained steering and obstacle avoidance track according to the final state information comprising the vehicle position and the yaw angle, so that the accurate control of the vehicle steering is realized when collision risks occur.

Fig. 3 is a flowchart of another vehicle steering obstacle avoidance method according to an embodiment of the present invention, which specifically describes acquiring a first obstacle based on the foregoing embodiment of the present embodiment, and the method specifically includes the following steps:

step S301, determining whether candidate obstacles exist in the sensing range through the fusion sensing module.

Specifically, the fusion sensing module may be used in the present embodiment to determine whether candidate obstacles exist in the sensing range, where the candidate obstacles are objects that may affect the form safety of the target vehicle, such as pedestrians, vehicles, trees, and animals presenting larger size, and sudden falling objects. In the embodiment, a plurality of candidate obstacles are obtained through a fusion perception algorithm based on, but not limited to, a sensor such as an on-board millimeter wave radar or a camera radar arranged on a target vehicle.

Step S302, if it is determined that the candidate obstacle exists, acquiring first association information of the candidate obstacle.

Specifically, when it is determined that there are candidate obstacles according to the information of the sensor, first related information of each candidate obstacle is obtained, where the first related information includes an obstacle type, an obstacle lateral distance, an obstacle longitudinal distance, an obstacle lateral acceleration, an obstacle longitudinal acceleration, and an obstacle width, and the specific content of the first related information is not limited in this embodiment.

And step S303, screening candidate obstacles according to the first correlation information, acquiring the obstacle with the highest collision risk, and taking the obstacle with the highest collision risk as the first obstacle.

Specifically, in the present embodiment, the risk coefficient of each candidate obstacle is obtained by screening candidate obstacles according to the first related information of each candidate obstacle, and the risk coefficient is proportional to the collision risk, so that the obstacle with the highest collision risk is used as the first obstacle. Since the obstacle candidates in the present embodiment include multiple types such as pedestrians and vehicles, the risk coefficients may be calculated in different manners for the pedestrians and the vehicles, and the specific calculation manner of the risk coefficients is not limited in the present embodiment.

Step S304, when the collision risk between the target vehicle and the first obstacle is determined and the steering obstacle avoidance condition is met, the steering obstacle avoidance track for the target vehicle is obtained.

Optionally, when it is determined that there is a collision risk between the target vehicle and the first obstacle and that the target vehicle and the first obstacle meet the steering obstacle avoidance condition, acquiring a steering obstacle avoidance trajectory for the target vehicle, including: acquiring operation parameters of the target vehicle, and calculating collision time according to the operation parameters and the first associated information, wherein the operation parameters comprise longitudinal speed and longitudinal acceleration; when the collision time is less than the preset time, determining that an obstacle avoidance risk exists between the target vehicle and the first obstacle; acquiring a minimum braking obstacle avoidance distance and a minimum steering obstacle avoidance distance of the target vehicle; acquiring second associated information of a second obstacle with the distance between the second obstacle and the target vehicle within a preset range; and acquiring a steering obstacle avoidance track for the target vehicle when determining that a steering obstacle avoidance condition is met according to the minimum braking obstacle avoidance distance, the minimum steering obstacle avoidance distance and the second correlation information.

And S305, acquiring the lateral speed of the target vehicle, constructing an improved constant slew rate and speed model according to the lateral speed, and estimating the improved constant slew rate and speed model to acquire final state information.

Optionally, the obtaining the lateral speed of the target vehicle includes: acquiring first historical state information, wherein the first historical state information comprises a lateral speed and a yaw rate; constructing a vehicle dynamics model according to the first historical state information, and estimating the vehicle dynamics model to obtain first initial state information; and correcting the first initial state information according to the measured yaw rate, and acquiring corrected first initial state information, wherein the corrected first initial state information comprises the lateral speed.

Optionally, an improved constant slew rate and speed model is constructed according to the lateral speed, and the improved constant slew rate and speed model is estimated to obtain final state information, including: updating the transverse position according to the lateral speed, and determining second historical state information according to the updated transverse position, wherein the second historical state information comprises the transverse position, the longitudinal position, a yaw angle, a longitudinal speed and a yaw angular speed; constructing an improved constant rate of rotation and speed model according to the second historical state information, and predicting the improved constant rate of rotation and speed model to obtain second initial state information; and correcting the second initial state information according to the measured longitudinal speed and the measured yaw rate to acquire final state information.

And S306, carrying out steering obstacle avoidance control on the target vehicle according to the steering obstacle avoidance track and the final state information.

Optionally, the steering obstacle avoidance control of the target vehicle according to the steering obstacle avoidance trajectory and the final state information includes: determining a target point position which is closest to the target vehicle position on the steering obstacle avoidance track; determining a transverse deviation according to the position of the target point and the position of the target vehicle, and determining a course deviation according to the yaw angle of the target vehicle; and carrying out steering obstacle avoidance control on the target vehicle according to the transverse deviation and the course deviation.

Fig. 4 is a schematic structural diagram of a vehicle steering obstacle avoidance device provided in an embodiment of the present invention, where the device includes: a steering obstacle avoidance trajectory acquisition module 410, a final state information acquisition module 420 and a steering obstacle avoidance control module 430.

The steering obstacle avoidance trajectory acquiring module 410 is configured to acquire a steering obstacle avoidance trajectory for the target vehicle when it is determined that a collision risk exists between the target vehicle and the first obstacle and the steering obstacle avoidance condition is met;

a final state information obtaining module 420, configured to obtain a lateral speed of the target vehicle, construct an improved constant rotation rate and speed model according to the lateral speed, and estimate the improved constant rotation rate and speed model to obtain final state information, where the final state information includes a vehicle position and a yaw angle;

and the steering obstacle avoidance control module 430 is used for performing steering obstacle avoidance control on the target vehicle according to the steering obstacle avoidance track and the final state information.

Optionally, the final state information obtaining module includes a lateral speed obtaining submodule, configured to obtain first historical state information, where the first historical state information includes a lateral speed and a yaw rate;

constructing a vehicle dynamics model according to the first historical state information, and estimating the vehicle dynamics model to obtain first initial state information;

and correcting the first initial state information according to the measured yaw rate, and acquiring corrected first initial state information, wherein the corrected first initial state information comprises the lateral speed.

Optionally, the lateral velocity obtaining sub-module is configured to obtain a first covariance matrix corresponding to the first initial state information, and a first measurement matrix and a first noise matrix corresponding to the measured yaw rate;

calculating a first Kalman gain according to the first covariance matrix, the first measurement matrix and the first noise matrix;

and correcting the first initial state information according to the measured yaw angular velocity, the first Kalman gain and the first measurement matrix, and acquiring the corrected first initial state information.

Optionally, the final state information obtaining module includes a final state information obtaining submodule, configured to update the lateral position according to the lateral speed, and determine second historical state information according to the updated lateral position, where the second historical state information includes the lateral position, the longitudinal position, the yaw angle, the longitudinal speed, and the yaw rate;

constructing an improved constant rate of rotation and speed model according to the second historical state information, and predicting the improved constant rate of rotation and speed model to obtain second initial state information;

and correcting the second initial state information according to the measured longitudinal speed and the measured yaw rate to acquire final state information.

Optionally, the final state information obtaining submodule is configured to obtain a second covariance matrix corresponding to the second initial state information, and a second measurement matrix and a second noise matrix corresponding to the measured longitudinal speed and the measured yaw rate;

calculating a second Kalman gain according to the second covariance matrix, the second measurement matrix and the second noise matrix;

and correcting the second initial state information according to the measured longitudinal speed and yaw angular speed, the second Kalman gain and the second measurement matrix to obtain final state information.

Optionally, the apparatus further includes a first obstacle obtaining sub-module, configured to determine whether a candidate obstacle exists in the sensing range through the fusion sensing module;

if the candidate obstacle is determined to exist, acquiring first associated information of the candidate obstacle, wherein the first associated information comprises the type of the obstacle, the transverse distance of the obstacle, the longitudinal distance of the obstacle, the transverse acceleration of the obstacle, the longitudinal acceleration of the obstacle and the width of the obstacle;

screening candidate obstacles according to the first correlation information to obtain an obstacle with the highest collision risk;

the obstacle with the highest collision risk is taken as the first obstacle.

Optionally, the steering obstacle avoidance track obtaining module is configured to obtain an operation parameter of the target vehicle, and calculate collision time according to the operation parameter and the first associated information, where the operation parameter includes a longitudinal speed and a longitudinal acceleration;

when the collision time is less than the preset time, determining that an obstacle avoidance risk exists between the target vehicle and the first obstacle;

acquiring a minimum braking obstacle avoidance distance and a minimum steering obstacle avoidance distance of the target vehicle;

acquiring second associated information of a second obstacle with the distance between the second obstacle and the target vehicle within a preset range;

and acquiring a steering obstacle avoidance track for the target vehicle when the steering obstacle avoidance condition is determined to be met according to the minimum braking obstacle avoidance distance, the minimum steering obstacle avoidance distance and the second associated information.

Optionally, the steering obstacle avoidance trajectory obtaining module is configured to determine that a steering obstacle avoidance space exists according to the second associated information;

and when the minimum steering obstacle avoidance distance is determined to be smaller than the minimum braking obstacle avoidance distance, acquiring a steering obstacle avoidance track of the target vehicle based on the steering obstacle avoidance space.

Optionally, the steering obstacle avoidance control module is configured to determine a target point position closest to a target vehicle position on the steering obstacle avoidance track;

determining a transverse deviation according to the position of the target point and the position of the target vehicle, and determining a course deviation according to the yaw angle of the target vehicle;

and carrying out steering obstacle avoidance control on the target vehicle according to the transverse deviation and the course deviation.

The device can execute the vehicle steering obstacle avoidance method provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method. For technical details not described in detail in this embodiment, reference may be made to the method provided in any embodiment of the present invention.

FIG. 5 illustrates a schematic diagram of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.

As shown in fig. 5, the electronic device 10 includes at least one processor 11, and a memory communicatively connected to the at least one processor 11, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, and the like, wherein the memory stores a computer program executable by the at least one processor, and the processor 11 can perform various suitable actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from a storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data necessary for the operation of the electronic apparatus 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other via a bus 14. An input/output (I/O) interface 15 is also connected to bus 14.

A number of components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, or the like; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.

The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, or the like. The processor 11 performs the various methods and processes described above, such as the vehicle steering avoidance method.

In some embodiments, the vehicle steering obstacle avoidance method may be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the vehicle steering obstacle avoidance method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the vehicle steering obstacle avoidance method by any other suitable means (e.g., by means of firmware).

Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.

A computer program for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be performed. A computer program can execute entirely on a machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.

The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the Internet.

The computing system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical host and VPS service are overcome.

It should be understood that various forms of the flows shown above, reordering, adding or deleting steps, may be used. For example, the steps described in the present invention may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solution of the present invention can be achieved.

The above-described embodiments should not be construed as limiting the scope of the invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A vehicle steering obstacle avoidance method is characterized by comprising the following steps:

when determining that a collision risk exists between a target vehicle and a first obstacle and the target vehicle and the first obstacle meets a steering obstacle avoidance condition, acquiring a steering obstacle avoidance track for the target vehicle;

2. The method of claim 1, wherein the obtaining the lateral velocity of the target vehicle comprises:

acquiring first historical state information, wherein the first historical state information comprises a lateral speed and a yaw rate;

and correcting the first initial state information according to the measured yaw velocity, and acquiring corrected first initial state information, wherein the corrected first initial state information comprises the lateral velocity.

3. The method of claim 2, wherein said correcting said first initial state information based on a measured yaw rate to obtain corrected first initial state information comprises:

acquiring a first covariance matrix corresponding to the first initial state information, and a first measurement matrix and a first noise matrix corresponding to the measured yaw rate;

and correcting the first initial state information according to the measured yaw rate, the first Kalman gain and the first measurement matrix to obtain the corrected first initial state information.

4. The method of claim 2, wherein constructing an improved constant rate of rotation and velocity model according to the lateral velocity, and estimating the improved constant rate of rotation and velocity model to obtain final state information comprises:

updating the transverse position according to the lateral speed, and determining second historical state information according to the updated transverse position, wherein the second historical state information comprises the transverse position, the longitudinal position, a yaw angle, a longitudinal speed and a yaw angular speed;

constructing the improved constant conversion rate and speed model according to the second historical state information, and predicting the improved constant conversion rate and speed model to obtain second initial state information;

5. The method of claim 4, wherein said correcting said second initial state information based on measured longitudinal and yaw rates to obtain said final state information comprises:

acquiring a second covariance matrix corresponding to the second initial state information, and a second measurement matrix and a second noise matrix corresponding to the measured longitudinal velocity and the measured yaw velocity;

and correcting the second initial state information according to the measured longitudinal speed and yaw angular speed, the second Kalman gain and the second measurement matrix to acquire the final state information.

6. The method of claim 1, wherein prior to obtaining the steering avoidance trajectory for the target vehicle upon determining that there is a risk of collision between the target vehicle and the first obstacle and that the steering avoidance condition is met, further comprising:

determining whether candidate obstacles exist in a sensing range through a fusion sensing module;

if the candidate obstacle is determined to exist, acquiring first associated information of the candidate obstacle, wherein the first associated information comprises an obstacle type, an obstacle transverse distance, an obstacle longitudinal distance, an obstacle transverse acceleration, an obstacle longitudinal acceleration and an obstacle width;

screening the candidate obstacles according to the first correlation information to obtain an obstacle with the highest collision risk;

and taking the obstacle with the highest collision risk as the first obstacle.

7. The method of claim 6, wherein obtaining a steering obstacle avoidance trajectory for the target vehicle when it is determined that there is a collision risk between the target vehicle and the first obstacle and the steering obstacle avoidance condition is met, comprises:

acquiring operation parameters of a target vehicle, and calculating collision time according to the operation parameters and the first associated information, wherein the operation parameters comprise longitudinal speed and longitudinal acceleration;

8. The method according to claim 7, wherein the obtaining of the steering obstacle avoidance trajectory for the target vehicle when it is determined that the steering obstacle avoidance condition is met according to the minimum braking obstacle avoidance distance, the minimum steering obstacle avoidance distance, and the second correlation information includes:

determining that a steering obstacle avoidance space exists according to the second associated information;

and when the minimum steering obstacle avoidance distance is smaller than the minimum braking obstacle avoidance distance, acquiring a steering obstacle avoidance track of the target vehicle based on the steering obstacle avoidance space.

9. The method as claimed in claim 1, wherein the performing of steering and obstacle avoidance control on the target vehicle according to the steering and obstacle avoidance trajectory and the final state information comprises:

determining a target point position closest to the target vehicle position on the steering obstacle avoidance track;

10. An electronic device, characterized in that the electronic device comprises:

one or more processors;

a storage device for storing one or more programs,

when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-9.

11. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-9.