WO2023024914A1

WO2023024914A1 - Vehicle avoidance method and apparatus, computer device, and storage medium

Info

Publication number: WO2023024914A1
Application number: PCT/CN2022/111675
Authority: WO
Inventors: 张家旭; 王洪雨; 刘洋; 许健
Original assignee: 中国第一汽车股份有限公司
Priority date: 2021-08-27
Filing date: 2022-08-11
Publication date: 2023-03-02
Also published as: CN113619574A

Abstract

A vehicle avoidance method and apparatus, a computer device, and a storage medium. The vehicle avoidance method comprises: detecting traveling data of a front vehicle in a same lane; detecting an avoidance type of a current vehicle according to the traveling data of the front vehicle; and under the condition that the avoidance type of the current vehicle is determined to be lane changing avoidance, determining a lane changing path of the current vehicle on the basis of constraint conditions of a lateral acceleration and a lateral acceleration change rate, and controlling the current vehicle to travel along the lane changing path.

Description

Vehicle avoidance method, device, computer equipment and storage medium

This application claims the priority of the Chinese patent application with application number 202110992796.3 submitted to the China Patent Office on August 27, 2021, the entire content of which is incorporated herein by reference.

technical field

The present application relates to intelligent driving technology, such as a vehicle avoidance method, device, computer equipment and storage medium.

Background technique

The car will encounter obstacles during driving and needs to avoid them.

In related technologies, the mass-produced models of the vehicle manufacturers are all equipped with an automatic emergency braking obstacle avoidance system to improve the active safety when the vehicle encounters an obstacle during driving.

However, avoiding by braking requires a relatively long initial distance between the vehicle and the obstacle in front. If the initial distance between the vehicle and the obstacle in front is not too far, a collision will still occur.

Contents of the invention

The present application provides a vehicle avoidance method, device, computer equipment and storage medium, so as to improve the safety of vehicle avoidance.

This application provides a vehicle avoidance method, including:

Detect the driving data of the vehicle ahead in the same lane;

According to the driving data of the vehicle in front, detect the current vehicle avoidance type;

When it is determined that the current vehicle avoidance type is lane change avoidance, based on the constraints of lateral acceleration and lateral acceleration change rate, determine the lane change path of the current vehicle, and control the current vehicle along the Change lanes.

The application also provides a vehicle avoidance device, comprising:

The driving data acquisition module of the preceding vehicle is configured to detect the driving data of the preceding vehicle in the same lane;

The avoidance type determination module is configured to detect the current vehicle avoidance type according to the driving data of the preceding vehicle;

Controlling the vehicle lane-changing module, configured to determine the lane-changing path of the current vehicle based on the constraints of lateral acceleration and lateral acceleration change rate when the type of avoidance of the current vehicle is determined to be lane-changing avoidance, and The current vehicle is controlled to travel along the lane changing path.

The present application also provides a kind of computer equipment, and described computer equipment comprises:

one or more processors;

a storage device configured to store one or more programs;

When the one or more programs are executed by the one or more processors, the one or more processors implement the above vehicle avoidance method.

The present application also provides a storage medium containing computer-executable instructions, and the computer-executable instructions are used to execute the above-mentioned vehicle avoidance method when executed by a computer processor.

Description of drawings

FIG. 1 is a flow chart of a vehicle avoidance method provided in Embodiment 1 of the present application;

Fig. 2 is a schematic diagram of a type of vehicle lane change and avoidance provided in Embodiment 1 of the present application;

3 is a flow chart of a vehicle avoidance method provided in Embodiment 2 of the present application;

FIG. 4 is a schematic diagram of a lane-changing avoidance route for a vehicle provided in Embodiment 2 of the present application;

5 is a flow chart of a vehicle avoidance method provided in Embodiment 3 of the present application;

FIG. 6 is a schematic diagram of a scene of vehicle avoidance path tracking control provided in Embodiment 3 of the present application;

FIG. 7 is a schematic diagram of a vehicle avoidance system provided in Embodiment 5 of the present application;

Fig. 8 is a schematic diagram of a vehicle avoidance device provided in Embodiment 5 of the present application;

FIG. 9 is a schematic structural diagram of a computer device provided in Embodiment 6 of the present application.

Detailed ways

The application will be described below in conjunction with the accompanying drawings and embodiments. The specific embodiments described herein are for illustration of the application only. For ease of description, only parts relevant to the present application are shown in the drawings.

Embodiment one

Figure 1 is a flow chart of a vehicle avoidance method provided in Embodiment 1 of the present application. This embodiment is applicable to the situation of avoiding vehicles, for example, the situation where the vehicle actively changes lanes to avoid the vehicle in front. This method can be avoided by the vehicle device, which can be implemented by software and/or hardware, and generally can be integrated into computer equipment, for example, a vehicle-mounted terminal. Including the following steps:

Step 110, detecting the driving data of the preceding vehicle in the same lane.

The vehicle ahead in the same lane refers to the vehicle driving in the same lane as the current vehicle and in front of the vehicle, and there are no other obstacles between the two vehicles. The driving data of the preceding vehicle is used to obtain the motion state of the preceding vehicle, including the distance between the preceding vehicle and the current vehicle, driving speed and acceleration, etc. Driving data can be obtained through sensor detection. Exemplarily, the sensor can include a front-view camera and a front-side millimeter-wave radar. The distance to the vehicle ahead, and the initial speed and deceleration for emergency braking. In addition, the sensor also includes a left rear millimeter-wave radar and a right rear millimeter-wave radar, wherein the left rear millimeter-wave radar and the right rear millimeter-wave radar can respectively detect the left lane behind the current vehicle and the right lane behind the current vehicle Is there a vehicle moving. The vehicle-mounted terminal may also include a network communication module configured to download maps, and a register configured to store downloaded maps.

Step 120 , according to the driving data of the vehicle in front, detect the type of avoidance of the current vehicle.

The avoidance type is used to determine the manner of avoiding the preceding vehicle. Exemplarily, the avoidance type may include braking avoidance and lane changing avoidance. According to the driving data of the preceding vehicle, detecting the type of avoidance of the current vehicle is: according to the detected driving data of the preceding vehicle, calculating the minimum deceleration at which the current vehicle adopts braking to avoid collision with the preceding vehicle. If the calculated minimum deceleration is greater than the preset maximum deceleration, the current vehicle will collide with the vehicle in front by braking to avoid collision, and it is determined to select the type of lane change avoidance; if the calculated minimum deceleration is less than or equal to the preset maximum deceleration, the current vehicle will not collide with the vehicle in front by braking to avoid, and the type of lane change avoidance or brake avoidance can be selected according to the actual situation. When it is detected that there are vehicles running in the left lane of the current vehicle and the right lane of the current vehicle, select the braking avoidance type; Lane change avoidance type. Wherein, the preset maximum deceleration may be the maximum deceleration of the current vehicle braking itself, or may be a user-defined maximum deceleration, for example, select the maximum deceleration that meets the requirements of ride comfort.

In one embodiment, the detecting the avoidance type of the current vehicle according to the driving data of the preceding vehicle includes: determining the time of collision and the vehicle distance between the preceding vehicle and the current vehicle according to the driving data of the preceding vehicle; Preset the maximum deceleration, the collision time and the vehicle distance, detect the braking prediction result of the current vehicle; determine the avoidance of the current vehicle according to the corresponding relationship between the braking prediction result and the type of avoidance type.

The collision time is when the vehicle ahead in the same lane is traveling at a constant speed at the current speed, and the vehicle in front decelerates at a preset maximum deceleration, and it happens to collide with the vehicle in front. The preset maximum deceleration refers to the maximum deceleration when the vehicle in front brakes. The vehicle distance between the preceding vehicle and the current vehicle refers to the distance between the current vehicle and the preceding vehicle, for example, the distance between the front of the current vehicle and the rear of the preceding vehicle. Exemplarily, the distance can be acquired through the millimeter-wave radar on the front side. The braking prediction result is used to indicate whether the current vehicle collides with the preceding vehicle when the current vehicle adopts the preset maximum deceleration braking. Braking prediction results include a result of no collision and a result of collision. The corresponding relationship between the braking prediction result and the avoidance type is used to select the avoidance type according to the braking prediction result. Exemplarily, the corresponding relationship is as follows, when the braking prediction result is that the current vehicle collides with the preceding vehicle, it corresponds to lane change avoidance; when the braking prediction result indicates that the preceding vehicle does not collide with the preceding vehicle, corresponding braking avoidance and lane change avoidance two types.

Exemplarily, through the millimeter-wave radar on the left rear side of the current vehicle and the millimeter-wave radar on the front side, it is detected whether there is a driving vehicle in the left lane of the current vehicle, and the current vehicle is detected through the millimeter-wave radar on the right rear side of the current vehicle and the millimeter-wave radar on the front side Whether there is a vehicle in the right lane. The millimeter-wave radar detection range is a preset value. When the distance between the vehicle on the right or left lane and the current vehicle in the direction of travel exceeds the preset value, the current vehicle will not change lanes with the vehicle on the right or left lane. Vehicles collide. When the braking prediction result is that no collision will occur, if it is detected that there is no vehicle in the left lane of the current vehicle and meets the conditions for the vehicle to change lanes to the left lane, you can choose to change lanes to the left lane to avoid; if it detects that the current vehicle is on the right If there is no vehicle in the side lane and the vehicle meets the conditions for changing lanes to the right lane, you can choose to change lanes to the right lane to avoid; Right lane change conditions, you can choose any lane to avoid; when it is detected that there are vehicles in the right lane of the vehicle and the left lane of the current vehicle, it does not meet the conditions for the vehicle to change lanes to the left or right lane. Select a deceleration braking avoidance with high riding comfort within the preset deceleration range. When the braking prediction result is that a collision will occur, the lane change avoidance is selected, and the lane change selection is the same as when the braking prediction result is that no collision will occur.

By detecting the braking prediction result of the current vehicle according to the preset maximum deceleration, collision time and vehicle distance, the avoidance method can be determined, and the avoidance method can be enriched under the condition that the driving can be guaranteed to avoid collision, and the driver can choose A more comfortable way to avoid.

Step 130: When it is determined that the current vehicle avoidance type is lane change avoidance, based on the constraints of lateral acceleration and lateral acceleration change rate, determine the lane change path of the current vehicle, and control the current vehicle along the Follow the lane change path described.

Lateral acceleration and lateral acceleration change rate refer to the lateral acceleration and lateral acceleration change rate when the current vehicle changes lanes. Lateral refers to the direction perpendicular to the driving direction. Lateral acceleration and lateral acceleration change rate are used to limit the current Vehicle speed and acceleration. By selecting the appropriate lateral acceleration and the rate of change of lateral acceleration, when the vehicle is changing lanes, the human body can be prevented from rapidly shifting sideways and colliding with the inner wall of the vehicle, and the comfort of the human body can be improved when the vehicle is driving along the lane changing path.

The lane-changing path refers to the driving path of the current vehicle when changing lanes for avoidance, that is, when driving to a lane other than the lane where the vehicle in front is located. After the lane change path is determined, the current vehicle is controlled to change lanes according to the driving path. Exemplarily, the control method may be a closed-loop method, and the driving trajectory is corrected in real time through error feedback. FIG. 2 is a schematic diagram of a vehicle lane change avoidance type provided by Embodiment 1 of the present application. In Figure 2, the lane change to the left side of the vehicle direction is taken as an example. The X-axis in the figure is the direction of the lane, and the Y-axis is the direction perpendicular to the direction of the lane. The directions are the same, the Y axis is perpendicular to the driving direction of the current vehicle, and the direction perpendicular to the driving direction of the current vehicle is the lateral direction. The current vehicle 10 changes lanes from the current lane to the left lane to avoid the preceding vehicle 20 along the lane change path 30 , and the vehicle 11 after the lane change is the current vehicle after the lane change. Through lane change avoidance, when the distance between two vehicles is relatively close, it can effectively avoid the vehicle in front and ensure the safety of the vehicle.

In the embodiment of the present application, when the avoidance type is lane change avoidance, based on the constraints of lateral acceleration and lateral acceleration change rate, the current vehicle is controlled to drive along the lane change path, and the need for a relatively long initial distance for brake avoidance is solved. Distance, when the initial distance is relatively short, there will be a collision problem, so as to improve the efficiency of avoiding obstacles and improve the safety of automatic driving.

In one embodiment, the vehicle avoidance method further includes: in a case where it is determined that the current vehicle avoidance type is brake avoidance, controlling the current vehicle to decelerate.

When it is detected that the current vehicle braking avoidance will not collide with the preceding vehicle, control the vehicle braking avoidance. During the braking avoidance process, a deceleration with high ride comfort can be selected to control the vehicle braking avoidance.

When the avoidance type is brake avoidance, the vehicle in front is avoided by braking to ensure the driving safety of the vehicle.

Embodiment two

Fig. 3 is a flow chart of a vehicle avoidance method provided in Embodiment 2 of the present application. The technical solution of this embodiment is described on the basis of the above technical solution, and will be based on the constraints of lateral acceleration and lateral acceleration rate of change, Determining the lane-changing path of the current vehicle, which is refined into: based on the constraints of lateral acceleration and lateral acceleration rate of change, using a B-spline curve model to fit the lane-changing path of the current vehicle; determining the fitting curve is the lane-changing path of the current vehicle. The method includes:

Step 210, detecting the driving data of the vehicle ahead in the same lane.

Step 220 , according to the driving data of the vehicle in front, detect the type of avoidance of the current vehicle.

Step 230, when it is determined that the current vehicle avoidance type is lane change avoidance.

Step 240 , based on the constraint conditions of lateral acceleration and lateral acceleration change rate, adopting a B-spline curve model to fit the lane-changing path of the current vehicle.

The B-spline curve is a linear group of B-spline base curves, and the B-spline curve surface has the ability of local control. The B-spline curve can be expressed as,

In the formula, u is the node vector of the B-spline curve model, P is the control point of the B-spline curve model, N _i,k (u) is the B-spline basis function, and k represents the power of the B-spline basis function. Among them, N _i,k (u) can be expressed as:

Using B-spline Curve Coordinate Derivatives

and

Its curvature can be expressed as

Therefore, the B-spline curve curvature can be used to represent the current vehicle lateral acceleration constraints:

Among them, v _init and a _{y max} are the current vehicle initial velocity and maximum lateral acceleration respectively. If the third derivative of the coordinates of the B-spline curve is expressed as

Then the chain derivation method can be used to obtain the current vehicle lateral acceleration change rate:

Among them, j _{y max} is the maximum lateral acceleration change rate of the current vehicle.

In one embodiment, said using a B-spline curve model to fit the lane-changing path of the current vehicle includes: defining the node vector U and control point P of the B-spline curve model based on the following formula, and fitting the current The lane change path of the vehicle:

U={0,0,0,0,0,0,0.5,1,1,1,1,1,1};

Among them, P _i is the i-th control point on the B-spline curve model, the coordinates of P _i are (P _xi , P _yi ), d _i is the same direction between control points P _i and P _i-1 distance. d ₁ ≥0, d ₂ ≥0, d ₃ ≥0, d ₄ ≥0 and

is the coefficient to be determined, L is the distance of the current vehicle in the direction perpendicular to the lane method before and after the lane change, φ is the last control point of the current vehicle driving on the lane before the lane change and the distance after the lane change The angle between the line connecting the first control point on the lane and the lane before the lane change, i is a positive integer greater than or equal to 0. FIG. 4 is a schematic diagram of a lane-changing avoidance path for vehicles provided in Embodiment 2 of the present application, wherein the node vector U and the control point P are determined according to the above definition, and the entire path is center-symmetric about the control point _P3 . The node vector U is the same as the node vector u in the aforementioned formula.

According to the setting of the node vector U and the control point P, the B-spline curve model is divided into 6 sections, which are respectively fitted with 6 different basis functions, and different lateral acceleration and lateral acceleration are selected among the 6 basis functions. The acceleration change rate can increase the smoothness of the B-spline curve, prevent the body from colliding with the inner wall of the vehicle due to excessive lateral speed changes during the lane change process, improve the comfort of the vehicle, and avoid using a large number of basis functions. The amount of calculation is too large to improve the fitting efficiency.

Step 250, determining the fitting curve as the lane-changing path of the current vehicle.

Based on the constraints in step 240, the nonlinear programming problem for establishing the current vehicle active lane change avoidance path is:

The sequential quadratic programming method is used to solve the above nonlinear programming problem to obtain the vehicle's active lane change avoidance path. Knowing the maximum lateral acceleration allowed by the vehicle, the greater the initial speed of the vehicle for lane change avoidance, the smaller the allowable maximum curvature of the vehicle lane change avoidance path. Taking the minimization of the lateral distance of the vehicle lane-changing avoidance path as the optimization goal, the planning problem of the path P ₀ P ₁ between the control point P ₀ and the control point P ₁ in the vehicle lane-changing avoidance path is transformed into a nonlinear programming problem, Solve to get the path P ₀ P ₁ . Based on the symmetric relationship between paths, the remaining paths are obtained by translating, flipping, and rotating the path P ₀ P ₁ , and ensuring that the six path curves are connected smoothly, that is, all paths are obtained.

After solving the path P ₀ P ₁ , the path P 1 _{P 2} between the control point P ₁ _and _the control point P ₂ is obtained by translating the path P ₀ P 1, and the path P ₁ P ₂ is rotated with P ₂ as the rotation center to obtain control The path P ₂ P ₃ between the point P ₂ and the control point P ₃ , the path P ₀ P ₃ is rotated with P ₃ as the rotation center, and the path P ₃ P ₄ and the path between the control point P ₃ and the control point P ₆ are obtained P ₄ P ₅ and the path P ₅ P ₆ , get the entire vehicle lane change avoidance path.

Step 260, controlling the current vehicle to travel along the lane changing path.

After the path planning route is determined, the vehicle is controlled to drive along the lane-changing path to realize vehicle lane-changing and avoidance.

In the technical solution of this embodiment, by using the B-spline curve model to fit the lane-changing path of the current vehicle, the B-spline curve has the characteristics of convexity and support, and by setting the basis function of the B-spline curve, the The fitted lane-changing path is smoother, avoiding sudden changes in the lane-changing path, causing discomfort to the passengers, and by constraining the lateral acceleration and lateral acceleration, the lateral acceleration and lateral acceleration change rate are prevented from being too large. It causes the human body to lean towards the inner wall of the vehicle and collide, improving the ride comfort.

Embodiment three

Fig. 5 is a flow chart of a vehicle avoidance method provided in Embodiment 3 of the present application. The technical solution of this embodiment is explained on the basis of the above technical solution. Controlling the current vehicle to travel along the lane-changing path includes: : according to the driving data and vehicle parameters of the current vehicle, construct a path tracking error model; determine the closed-loop system according to the path tracking error model, and calculate the tracking error control amount of the closed-loop system; according to the error control amount, control The current vehicle travels, and the current vehicle travels along the lane-changing path by closed-loop control. The method includes:

Step 310, detecting the driving data of the vehicle ahead in the same lane.

Step 320 , according to the driving data of the vehicle in front, detect the type of avoidance of the current vehicle.

Step 330 , if it is determined that the current vehicle avoidance type is lane change avoidance, determine the lane change path of the current vehicle based on the constraints of lateral acceleration and lateral acceleration change rate.

Step 340, constructing a path tracking error model according to the current vehicle driving data and vehicle parameters.

The driving data of the vehicle refers to the motion parameters when the vehicle is running. For example, it may be the longitudinal speed and the target yaw rate, wherein the longitudinal direction is the same as the direction of the lane, the longitudinal speed is the speed in the direction of the lane, and the target yaw rate is the vehicle variable speed. The lateral angular velocity while on track, lateral refers to the direction perpendicular to the direction of travel of the vehicle. The vehicle parameters refer to the property parameters of the vehicle itself. Exemplarily, it may include at least one of the following: vehicle mass, yaw moment of inertia, equivalent cornering stiffness of the front wheels, equivalent cornering stiffness of the rear wheels, and distance from the center of mass of the vehicle to the front axle. distance and the distance from the center of mass of the car to the rear axle, etc. The path tracking error refers to the error between the vehicle's driving trajectory and the planned path. The path tracking error model is used to adjust the driving trajectory according to the tracking error so that the vehicle can drive along the planned path, including the longitudinal control and lateral control of the vehicle. The longitudinal control mainly It is the control of the driving speed, which corresponds to the longitudinal speed in the vehicle motion parameters; the lateral control is mainly the control of the front wheel angle of the vehicle, which corresponds to the target yaw rate in the vehicle motion parameters.

According to the lateral position deviation and azimuth angle deviation between the vehicle and the planned route and the rate of change of the two, the vehicle is controlled to travel along the planned route. Use m, I _Z , C _f , _Cr , _{If , I r} _, V _x and

Indicates vehicle mass, yaw moment of inertia, front wheel equivalent cornering stiffness, rear wheel equivalent cornering stiffness, distance from vehicle center of mass to front axle, distance from vehicle center of mass to rear axle, longitudinal velocity and target yaw rate, define The system state vector and control input vector are respectively

and u=δ _f , where e ₁ is the distance between the center of mass of the car and the target path,

is the derivative of e ₁ , and e ₂ is the deviation between the azimuth angle of the vehicle and the azimuth angle of the target,

is the derivative of e ₂ , and _δf is the front wheel angle to establish the path tracking error model as:

Considering the uncertainty of the equivalent cornering stiffness parameters of the front and rear wheels of the vehicle, it can be expressed as

Among them, C _f0 and C _r0 are the nominal values of the equivalent cornering stiffness of the front and rear wheels of the car respectively, and C _fe and C _re are the maximum perturbations of the equivalent cornering stiffness of the front and rear wheels of the car respectively. Therefore, The path tracking error model is modified as:

Among them, A, ΔA, B ₁ , ΔB ₁ , B ₂ , ΔB ₂ and C represent: system nominal matrix, A’s perturbation matrix, control nominal matrix, B ₁ ’s perturbation matrix, disturbance nominal matrix, B ₂ ’s Perturbation matrix and output matrix, A, ΔA, B ₁ , ΔB ₁ , B ₂ , ΔB ₂ and C can be expressed as:

[ΔA ΔB ₁ ΔB ₂ ]=WF[E ₁ E ₂ E ₃ ]

In the formula, W is the constant matrix of the perturbation structure of the system, F is the perturbation matrix satisfying F ^T F ≤ I, I is the moment of inertia, W, E ₁ , E ₂ and E ₃ can be expressed as:

FIG. 6 is a schematic diagram of a scene of vehicle avoidance path tracking control provided in Embodiment 3 of the present application. The X-axis is longitudinal, which is the same as the direction of the lane; the Y-axis is perpendicular to the direction of the lane, the x-axis is the same as the current driving direction of the vehicle, and the y-axis is perpendicular to the driving direction of the vehicle. The coordinate system formed by the X-axis and the Y-axis can be understood as a world coordinate system, and the coordinate system formed by the x-axis and the y-axis can be understood as the relative coordinate system of the current vehicle. The rectangle in the figure represents the current vehicle, e ₁ is the distance between the center of mass of the vehicle and the target path, e ₂ is the deviation between the vehicle azimuth and the target azimuth, Ψ is the yaw rate, and Ψ _des is the target yaw rate.

Step 350: Determine a closed-loop system according to the path tracking error model, and calculate a tracking error control quantity of the closed-loop system.

A closed-loop system refers to a control system with feedback information.

In one embodiment, the calculating the tracking error control quantity of the closed-loop system includes: calculating the tracking error control quantity of the closed-loop system according to the path tracking feedback model and the path tracking feedforward model.

Based on the modified path tracking error model, the "feedforward + feedback" path tracking control method is designed. Under the premise of closed loop stability, the dynamic performance index of the closed loop system can be flexibly configured. Feedforward is a lateral control method to control the front wheel angle. , the feedback is rear wheel feedback control. To this end, the modified path tracking error model is simplified as:

Based on the simplified path tracking error model, the feedback control law is designed as:

u ₁ =Ky=KCx

Among them, K is the gain.

Thus, the path tracking closed-loop system is obtained as:

Among them, W is the constant matrix of the perturbation structure of the system, F is the perturbation matrix satisfying F ^T F ≤ I, I is the moment of inertia, A _c and E _c can be expressed as:

A _c ＝A+B ₁ KC

E _c =E ₁ +E ₂ KC

In order to realize the dynamic performance index of the closed-loop system can be flexibly configured under the premise of closed-loop stability, the pole configuration method is used to design the feedback gain, and the problem is converted into a linear matrix inequality method to solve the problem, even if the dynamic performance index of the closed-loop system can be flexibly configured The performance index is equivalent to the following linear matrix inequality:

The above linear matrix inequality can be equivalent to:

in,

The above linear matrix inequality can be equivalent to:

In order to decouple the above linear matrix inequalities, the intermediate transition variables X=QX _Q Q ^T +RX _R R ^T and KCX=Y=Y _R R ^T are defined, where Q is a full-rank matrix composed of matrix C kernel space basis vectors, R is the pseudo-inverse matrix of matrix C, which can be expressed as R＝C ^T (CC ^T ) ^-1 , X _Q and X _R are unknown symmetric positive definite matrices, and Y _R is an unknown arbitrary matrix, thus we can get:

Solving the above linear matrix inequality, the feedback gain coefficient can be obtained as:

in,

and

are the feasible solutions of X _R and Y _R respectively.

Since the core algorithm in the process of solving the above-mentioned feedback gain coefficient is the pole allocation algorithm, the dynamic performance index of the closed-loop system can be flexibly configured.

Through the vehicle lane change avoidance path tracking feedforward control, according to the vehicle lane change avoidance path tracking control law u=u ₁ +u ₂ =KCx+u ₂ , where u ₁ =Ky=KCx is the vehicle lane change avoidance path tracking feedback control law , K is the feedback control gain, u ₂ is the vehicle lane change avoidance path tracking feed-forward control law, using the Laplace transform final value theorem to design the feed-forward control law is:

Among them, f ₃ is the component of the matrix KC, and v _x is the longitudinal velocity of the vehicle.

Under the action of the feed-forward control law, the influence of the disturbance term on the path tracking accuracy is suppressed, and the disturbance term caused by the vehicle target yaw rate is ignored when designing the vehicle lane change and avoidance path tracking control law. The poles of the closed-loop system can be configured in The desired position, but the closed-loop system is actually affected by the disturbance term caused by the target yaw rate of the vehicle, which cannot achieve the expected dynamic and steady-state performance, so that the path tracking control can achieve zero stability error.

Through the path tracking feedback model and the path tracking feedforward model, the tracking error control quantity of the closed-loop system is calculated, which can track various paths, and is less affected by the interference of the path shape, and can reduce the lateral error and the vehicle driving. The longitudinal error improves the path tracking effect when the curvature changes significantly, so that the vehicle has better path tracking accuracy, stability and robustness.

Step 360 : Control the current vehicle to travel according to the error control amount, so as to control the current vehicle to travel along the lane-changing path in a closed-loop manner.

In the technical solution of this embodiment, the "feedforward + feedback" path tracking control is designed to control the vehicle to travel along the planned path, which can track multiple paths, reduce the lateral error and longitudinal error during vehicle driving, and improve the obvious curvature change. The real-time path tracking effect makes the vehicle have better path tracking accuracy, stability and robustness, and makes the vehicle travel in line with the path designed in the path planning.

Embodiment Four

FIG. 7 is a schematic diagram of a vehicle avoidance system provided in Embodiment 4 of the present application. The technical solution of this embodiment is an implementation of the above-mentioned technical solution. The method includes:

The vehicle avoidance system can be divided into three main parts, the information collection unit, the vehicle avoidance planning unit and the line control unit. The information collection unit communicates with the vehicle avoidance planning unit; the vehicle avoidance planning unit communicates with the wire control unit.

The information collection unit is configured to collect traffic environment information of the current vehicle environment, including a front-view camera 410 , a front millimeter-wave radar 420 , a rear millimeter-wave radar 430 and a high-definition map 440 . Wherein, the rear millimeter wave radar 430 may include a left rear millimeter wave radar and a right rear millimeter wave radar. The information acquisition unit obtains traffic environment information through multi-sensor fusion, so that the current vehicle active lane change avoidance process can comprehensively consider the traffic environment information of the current vehicle's own lane and adjacent lanes, and meet the vehicle functional safety requirements.

The vehicle avoidance planning unit is configured to plan vehicle avoidance routes, including an avoidance type determination module 450 , a lane change route fitting module 460 and a lane change route control module 470 . The vehicle avoidance planning unit is set to execute a vehicle dynamic lane change avoidance determination method, a vehicle lane change avoidance path planning method and a vehicle lane change avoidance path tracking control method running on the vehicle electronic control unit proposed by the application, so that the planned The path satisfies the vehicle kinematics and dynamics constraints, meets the requirements of trackability and ride comfort, and enables the path tracking control strategy to flexibly configure the poles of the closed-loop system according to the performance index and achieve the goal of zero stability error.

The control-by-wire unit is configured to implement the method of the vehicle avoidance planning unit, including a brake-by-wire module 480 and a lane-by-wire unit 490. The brake-by-wire module 480 is configured to execute a method for controlling vehicle braking and avoidance, and the lane-by-wire unit 490 is configured to Execute the vehicle lane change avoidance method. This application uses highly integrated and highly redundant brake-by-wire and steer-by-wire systems as the executive mechanism of the vehicle's active lane change avoidance system, making the vehicle intelligently controllable.

The technical solution of this embodiment is a system that realizes the vehicle avoidance method of the present application through the construction of the vehicle avoidance system, and demonstrates the vehicle dynamic lane change avoidance determination method, the vehicle lane change avoidance path planning method and the vehicle lane change avoidance path tracking proposed in this application. The implementation process of the control method solves the problem of a relatively long initial distance and reduced ride comfort through braking avoidance, and achieves the effect of improving the effectiveness of avoiding obstacles and ride comfort.

Embodiment five

FIG. 8 is a schematic structural diagram of a vehicle avoidance device provided in Embodiment 5 of the present application. Embodiment 5 is a corresponding device for implementing the vehicle avoidance method provided in the above embodiments of the present application. The device can be implemented in the form of software and/or hardware, and can generally be integrated into computer equipment. Vehicle avoidance devices include:

The driving data acquisition module 510 of the vehicle in front is configured to detect the driving data of the vehicle ahead in the same lane; the avoidance type determination 520 is configured to detect the current vehicle avoidance type according to the driving data of the vehicle in front; the vehicle lane change control module 530 is configured to When it is determined that the avoidance type of the current vehicle is lane change avoidance, based on the constraints of lateral acceleration and lateral acceleration change rate, determine the lane change path of the current vehicle, and control the current vehicle along the lane change path. Drive on the road.

In the technical solution of this embodiment, when the type of avoidance is lane change avoidance, based on the constraints of lateral acceleration and lateral acceleration change rate, the current vehicle is controlled to drive along the lane change path, solving the need for braking avoidance. If the initial distance is long, collisions will occur when the initial distance is relatively short, so as to improve the efficiency of avoiding obstacles and improve the safety of automatic driving.

Vehicle avoidance devices, also including:

The deceleration control module is configured to control the current vehicle to decelerate when it is determined that the type of avoidance of the current vehicle is braking avoidance.

The module for controlling the vehicle to change lanes includes:

The lane-changing path fitting unit is set to be based on the constraints of lateral acceleration and lateral acceleration rate of change, and adopts the B-spline curve model to fit the lane-changing path of the current vehicle; the lane-changing path determination unit is set to The joining curve is determined as the lane-changing path of the current vehicle.

The lane changing path fitting unit includes:

The B-spline curve definition subunit is configured to define the node vector U and the control point P of the B-spline curve model based on the following formula, and fit the lane-changing path of the current vehicle:

U={0,0,0,0,0,0,0.5,1,1,1,1,1,1};

Among them, P _i is the i-th control point on the B-spline curve model, the coordinates of P _i are (P _xi , P _yi ), d _i is the same direction between control points P _i and P _i-1 Distance, L is the distance of the current vehicle in the direction perpendicular to the lane approach before and after the lane change,

is the angle between the last control point of the current vehicle on the lane before the lane change and the first control point on the lane after the lane change and the lane before the lane change, i is greater than or equal to A positive integer of 0.

The lane changing path fitting unit includes:

The tracking error model construction subunit is configured to construct a path tracking error model according to the current vehicle driving data and vehicle parameters; the tracking error control amount calculation subunit is configured to determine the closed-loop system according to the path tracking error model and calculate The tracking error control amount of the closed-loop system; the closed-loop control subunit is configured to control the running of the current vehicle according to the error control amount, so as to control the running of the current vehicle along the lane-changing path in a closed-loop manner.

The calculation of the tracking error control amount of the closed-loop system includes:

According to the path tracking feedback model and the path tracking feedforward model, the tracking error control quantity of the closed-loop system is calculated.

The avoidance type determination module includes:

The vehicle distance determining unit is configured to determine the collision time and the vehicle distance between the preceding vehicle and the current vehicle according to the driving data of the preceding vehicle; the braking prediction unit is configured to determine the collision time according to the preset maximum deceleration, the collision The time and the vehicle distance are used to detect the braking prediction result of the current vehicle; the avoidance type determination unit is configured to determine the avoidance type of the current vehicle according to the corresponding relationship between the braking prediction result and the avoidance type.

The above-mentioned device can execute the vehicle avoidance method provided in the embodiment of the present application, and has corresponding functional modules and effects for executing the vehicle avoidance method.

Embodiment six

FIG. 9 is a schematic structural diagram of a computer device provided in Embodiment 6 of the present application. As shown in FIG. 9, the computer device includes a processor 601, a memory 602, an input device 603, and an output device 604; The quantity can be one or more, and a processor 60 is taken as an example in FIG. Take the bus connection as an example.

The memory 602, as a computer-readable storage medium, can be set to store software programs, computer-executable programs and modules, such as program instructions/modules corresponding to the vehicle avoidance method in the embodiment of the present application (for example, the front vehicle travel data acquisition module 510, avoidance type determination module 520 and control vehicle lane change travel module 530). The processor 601 executes various functional applications and data processing of the computer equipment by running the software programs, instructions and modules stored in the memory 602 , that is, realizes the above-mentioned vehicle avoidance method.

The memory 602 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system and at least one application required by a function; the data storage area may store data created according to the use of the terminal, and the like. In addition, the memory 602 may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid-state storage devices. In some examples, memory 602 may include memory located remotely from processor 601, and such remote memory may be connected to the computer device via a network. Examples of the aforementioned networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

The input device 603 can be configured to receive input numbers or character information, and generate key signal input related to user settings and function control of the computer equipment. The output device 604 may include a display device such as a display screen.

Embodiment seven

Embodiment 7 of the present application also provides a storage medium containing computer-executable instructions, the computer-executable instructions are used to execute a vehicle avoidance method when executed by a computer processor, and the method includes: detecting the vehicle in front of the same lane Driving data; according to the driving data of the vehicle in front, detect the current vehicle avoidance type; when it is determined that the current vehicle avoidance type is lane change avoidance, based on the constraints of lateral acceleration and lateral acceleration change rate, determine the current vehicle avoidance type the lane-changing path of the vehicle, and control the current vehicle to travel along the lane-changing path.

From the above description about the implementation manners, it is understood that the present application can be implemented by means of software and necessary general-purpose hardware, or can be implemented by means of hardware. The technical solution of the present application can be embodied in the form of software products in essence, and the computer software products can be stored in computer-readable storage media, such as computer floppy disks, read-only memory (Read-Only Memory, ROM), random access Memory (Random Access Memory, RAM), flash memory (FLASH), hard disk or optical disc, etc., including a plurality of instructions to make a computer device (which can be a personal computer, server, or network device, etc.) execute the described embodiment of the present application. Methods.

In the above embodiment of the search device, the multiple units and modules included are only divided according to the functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the names of the multiple functional units It is only for the convenience of distinguishing each other, and is not used to limit the protection scope of the present application.

Claims

A vehicle avoidance method, comprising:

Detect the driving data of the vehicle ahead in the same lane;

Detecting the current vehicle avoidance type according to the driving data of the preceding vehicle;

When it is determined that the current vehicle avoidance type is lane change avoidance, based on the constraints of lateral acceleration and lateral acceleration change rate, determine the lane change path of the current vehicle, and control the current vehicle along the Change lanes.
The method according to claim 1, wherein said determining the lane-changing path of the current vehicle based on the constraints of lateral acceleration and lateral acceleration rate comprises:

Based on the constraints of the lateral acceleration and the rate of change of the lateral acceleration, a B-spline curve model is used to fit the lane-changing path of the current vehicle;

The fitting curve is determined as the lane-changing path of the current vehicle.
The method according to claim 2, wherein said adopting a B-spline curve model to fit the lane-changing path of the current vehicle comprises:

The node vector U and the control point P of the B-spline curve model are defined based on the following formula, and the lane-changing path of the current vehicle is fitted:

U={0,0,0,0,0,0,0.5,1,1,1,1,1,1};

Among them, P i is the i-th control point on the B-spline curve model, the coordinates of P i are (P xi , P yi ), d i is the same direction between control points P i and P i-1 Distance, L is the distance of the current vehicle in the direction perpendicular to the lane approach before and after the lane change,
is the angle between the last control point of the current vehicle on the lane before the lane change and the first control point on the lane after the lane change and the lane before the lane change, i is greater than or equal to A positive integer of 0.
The method according to claim 1, wherein the controlling the current vehicle to travel along the lane change path comprises:

Constructing a path tracking error model according to the driving data and vehicle parameters of the current vehicle;

Determine the closed-loop system according to the path tracking error model, and calculate the tracking error control quantity of the closed-loop system;

According to the error control amount, the current vehicle is controlled to travel along the lane-changing path in a closed-loop control.
The method according to claim 4, wherein said calculating the tracking error control amount of said closed-loop system comprises:

According to the path tracking feedback model and the path tracking feedforward model, the tracking error control quantity of the closed-loop system is calculated.
The method according to claim 1, wherein said detecting the current vehicle avoidance type according to the driving data of the preceding vehicle comprises:

determining the collision time and the vehicle distance between the preceding vehicle and the current vehicle according to the driving data of the preceding vehicle;

Detecting the braking prediction result of the current vehicle according to the preset maximum deceleration, the collision time and the vehicle distance;

The avoidance type of the current vehicle is determined according to the corresponding relationship between the braking prediction result and the avoidance type.
The method according to claim 1, further comprising:

If it is determined that the current vehicle avoidance type is brake avoidance, the current vehicle is controlled to decelerate.
A vehicle avoidance device, comprising:

The driving data acquisition module of the preceding vehicle is configured to detect the driving data of the preceding vehicle in the same lane;

The avoidance type determination module is configured to detect the current vehicle avoidance type according to the driving data of the preceding vehicle;

Controlling the vehicle lane-changing module, configured to determine the lane-changing path of the current vehicle based on the constraints of lateral acceleration and lateral acceleration change rate when it is determined that the current vehicle avoidance type is lane-changing avoidance, and The current vehicle is controlled to travel along the lane changing path.
A computer device, comprising a memory, a processor, and a computer program stored on the memory and operable on the processor, wherein, when the processor executes the program, any of claims 1-7 is implemented. A vehicle avoidance method described above.
A computer-readable storage medium storing a computer program, wherein when the program is executed by a processor, the vehicle avoidance method according to any one of claims 1-7 is realized.