CN116834731A - An obstacle avoidance control method suitable for wire-controlled four-wheel steering vehicles - Google Patents

Abstract

The invention discloses an obstacle avoidance control method suitable for a four-wheel steering-by-wire automobile, which comprises the following steps: step one, obtaining road surface attachment coefficient, gradient, vehicle speed, yaw rate and distance between the vehicle and a vehicle in front, a pedestrian or an obstacle through a sensor; step two, determining the environment complexity C of the current host vehicle _e And a comprehensive obstacle avoidance mode judgment index E _vd : and thirdly, controlling the vehicle to enter a corresponding obstacle avoidance mode according to the environment complexity and the comprehensive obstacle avoidance mode. The obstacle avoidance control method suitable for the four-wheel steering by wire automobile comprehensively considers the obstacle avoidance environment of the automobileAnd (3) carrying out a strategy obstacle avoidance mode, and carrying out corresponding automatic obstacle avoidance behaviors, and prompting the driver to intervene if necessary so as to improve the obstacle avoidance safety of the automobile.

Description

An obstacle avoidance control method suitable for wire-controlled four-wheel steering vehicles

Technical field

The invention belongs to the field of vehicle control technology, and particularly relates to an obstacle avoidance control method suitable for wire-controlled four-wheel steering vehicles.

Background technique

Four-wheel steering (4WS) is one of the technologies that improves the active safety of vehicles. Its main purpose is to improve the operational stability of the car when driving at high speeds or under the action of lateral wind, improve the maneuverability at low speeds, and reduce the Turning radius when parking at low speed. The redundant safety of the four-wheel steering system is higher. If the front-wheel steering system fails, the rear-wheel steering system can continue to work, ensuring that the vehicle has reliable steering capabilities.

Based on the rapid development of current smart driving vehicles, traditional mechanical steering mechanisms are no longer suitable for the rapid response needs of smart cars. At the same time, mechanical steering mechanisms are not suitable for intelligent chassis integration, so steer-by-wire (SBW) came into being. Steer-by-wire technology eliminates the mechanical connection between the steering wheel and the steering wheel, achieving decoupling between the person and the vehicle. It is considered a new generation steering system essential for achieving advanced autonomous driving.

The traditional car obstacle avoidance method is operated by the driver. With the rapid development of smart cars and wire-controlled chassis, the car's obstacle avoidance behavior can be controlled by the smart car itself, achieving driverless obstacle avoidance and reducing the driver's burden.

Contents of the invention

The invention provides an obstacle avoidance control method suitable for wire-controlled four-wheel steering vehicles. One purpose of the invention is to determine the vehicle's obstacle avoidance mode based on the vehicle's driving state and the driving environment in which the vehicle is located, which can improve the vehicle's ability to operate in complex environments. driving safety.

Another purpose of the present invention is to remind the driver to intervene when the environment in which the vehicle is located has a high risk factor, so as to improve the vehicle's obstacle avoidance safety.

The technical solution provided by the invention is:

An obstacle avoidance control method suitable for wire-controlled four-wheel steering vehicles, including:

Step 1: Obtain the road adhesion coefficient, slope, vehicle speed, yaw angular velocity, and distance between the vehicle and the vehicle in front, pedestrians or obstacles through sensors;

Step 2: Determine the current environmental complexity C _e and comprehensive obstacle avoidance mode judgment index E _vd of the vehicle:

Among them, kv is the weighting factor for the number of vehicles, kp is the weighting factor for the number of pedestrians, Nv is the number of vehicles, and Np is the number of pedestrians; λ ₁ is the vehicle speed influence factor, λ ₂ is the distance influence factor, and v is the vehicle speed;

Step 3: Control the vehicle to enter the corresponding obstacle avoidance mode according to the complexity of the environment and the comprehensive obstacle avoidance mode.

Preferably, the obstacle avoidance mode includes: a first obstacle avoidance mode, a second obstacle avoidance mode, a third obstacle avoidance mode and a fourth obstacle avoidance mode;

If C _e ≤ C _e1 and E _vd < E _vdt , the vehicle is controlled to enter the first obstacle avoidance mode;

If C _e ≤ _{C e1} and E _vd ≥ E _vdt or C _e1 <C _e <C _e2 and E _vd <E _vdt , control the vehicle to enter the second obstacle avoidance mode;

If C _e1 <C _e <C _e2 and E _vd ≥E _vdt or C _e >C _e1 and E _vd <E _vdt , the vehicle is controlled to enter the third obstacle avoidance mode;

If C _e > C _e1 and E _vd ≥ E _vdt , the vehicle is controlled to enter the fourth obstacle avoidance mode;

Among them, C _e1 is the first threshold of environmental complexity, C _e2 is the second threshold of environmental complexity, and E _vdt is the comprehensive obstacle avoidance mode judgment index threshold.

Preferably, when the obstacle avoidance mode is the first obstacle avoidance mode:

Controlling the front wheel rotation angle is:

δ _f =δ _fd +λ _u ×cosu+λ _d ×exp(-d)+λ _v ×arctanv;

Among them, δ _f is the front wheel angle; δ _fd is the default value of the front wheel angle; λ _u is the road adhesion coefficient adjustment factor, λ _d is the distance adjustment factor, λ _v is the vehicle speed adjustment factor, and d is the distance between the vehicle and the vehicle or obstacle in front. distance of objects.

Preferably, when the obstacle avoidance mode is the second obstacle avoidance mode:

Controlling the front wheel rotation angle is:

δ _f =1.5×δ _fd +λ _u ×cosu+λ _d ×exp(-d)+λ _v ×arctanv;

The rear wheel braking force is controlled as:

Among them, F _brd is the default value of the rear wheel braking force; λ _m is the mass adjustment factor, λ _d is the distance adjustment factor, λ _u is the road adhesion coefficient adjustment factor, λ _v is the vehicle speed adjustment factor; F _br is the rear wheel braking force, m is the mass of the car.

Preferably, when the obstacle avoidance mode is the third obstacle avoidance mode:

Controlling the front wheel rotation angle is:

δ _f =1.5×δ _fd +λ _u ×cosu+λ _d ×exp(-d)+λ _v ×arctanv;

The rear wheel rotation angle is controlled as:

δ _r = G _d × δ _f + G _a × v × r

In the formula, δ _r is the rear wheel steering angle, G _d is the ratio of the front and rear wheel steering angles, δ _f is the front wheel steering angle; G _a is the feedback gain of the vehicle's lateral motion state; r is the vehicle yaw angular velocity, v is vehicle speed.

Preferably, when the obstacle avoidance mode is the fourth obstacle avoidance mode:

Controlling the front wheel rotation angle is:

δ _f =1.5×δ _fd +λ _u ×cosu+λ _d ×exp(-d)+λ _v ×arctanv;

The rear wheel rotation angle is controlled as:

δ _r = G _d × δ _f + G _a × v × r

The rear wheel braking force is controlled as:

The front wheel braking force is controlled as:

F _bf =G _b ×F _br ;

Among them, G _b is the ratio of the front wheel braking force to the rear wheel braking force; F _bf is the front wheel braking force.

Preferably, the comprehensive obstacle avoidance mode judgment index threshold is calculated by the following formula:

Among them, v _min is the minimum value of vehicle speed set by the user, d _max is the maximum value of distance set, λ ₁ is the vehicle speed influence factor, and λ ₂ is the distance influence factor.

Preferably, a fuzzy control method is used to determine the vehicle speed influence factor λ ₁ and the distance influence factor λ ₂ :

The vehicle speed v and the distance d between the vehicle and the vehicle in front, pedestrians or obstacles are used as inputs to the fuzzy control model. The fuzzy control subset is defined as v={ZE, PS, PM, PB}, d={ZE, PS, PM ,PB};

The output of the fuzzy control model is the vehicle speed influence factor λ ₁ ={ZE, PS, PM, PB}, and the distance influence factor λ ₂ ={ZE, PS, PM, PB}.

Preferably, the obstacle avoidance control method suitable for wire-controlled four-wheel steering vehicles also includes:

When the obstacle avoidance mode is the third obstacle avoidance mode or the fourth obstacle avoidance mode, compare the hazard characteristic coefficient D with the hazard characteristic coefficient threshold D _t :

If the hazard characteristic coefficient D ≥ D _t , a takeover prompt message will be sent to the driver;

in,

In the formula, K is the obstacle avoidance environment level value.

Preferably, the obstacle avoidance environment level value is calculated according to the following formula:

In the formula, k ₁ , k ₂ and k ₃ are the weighting coefficients of road slope, road adhesion coefficient and environmental visibility respectively, and k ₁ + k ₂ + k ₃ = 1; i is the road slope; V is the environmental visibility, u is Road adhesion coefficient.

The beneficial effects of the present invention are:

The obstacle avoidance control method provided by the present invention and suitable for wire-controlled four-wheel steering vehicles can determine the vehicle's obstacle avoidance mode according to the vehicle's driving state and the driving environment in which the vehicle is located, and improve the driving safety of the vehicle in complex environments.

The obstacle avoidance control method provided by the present invention and suitable for wire-controlled four-wheel steering vehicles can remind the driver to intervene when the environmental hazard coefficient of the vehicle is high, further improving the obstacle avoidance safety of the vehicle.

Description of the drawings

Figure 1 is a block diagram of an obstacle avoidance control system suitable for wire-controlled four-wheel steering vehicles according to the present invention.

Detailed ways

The present invention will be further described in detail below with reference to the accompanying drawings, so that those skilled in the art can implement it with reference to the text of the description.

The invention provides an obstacle avoidance control method suitable for a wire-controlled four-wheel steering automobile, which is implemented based on an obstacle avoidance control system suitable for a wire-controlled four-wheel steering automobile. As shown in Figure 1, the obstacle avoidance control system suitable for wire-controlled four-wheel steering vehicles includes: environment perception module, obstacle avoidance mode decision module, steering decision module, deceleration decision module, obstacle avoidance execution module and early warning module. The environment sensing module obtains the road adhesion coefficient, slope, vehicle speed, yaw angular velocity, the distance between the vehicle and the vehicle in front, pedestrians or obstacles through sensors, and determines the complexity of the environment. Afterwards, the environment perception module transmits the road adhesion coefficient, vehicle speed, yaw angular velocity, the distance between the vehicle and the vehicle in front, pedestrians or obstacles, and environmental complexity to the obstacle avoidance mode decision-making module. The obstacle avoidance mode decision-making module determines the obstacle avoidance mode based on the current environmental complexity, vehicle speed and the distance between the vehicle and the preceding vehicle, and transmits the determined obstacle avoidance mode information to the steering decision-making module and the deceleration decision-making module.

The steering decision-making module receives the obstacle avoidance mode information transmitted by the obstacle avoidance mode decision-making module and makes a decision on the steering mode; the steering mode includes a front-wheel steering mode and a four-wheel steering mode. The deceleration decision-making module receives the obstacle avoidance mode information transmitted by the obstacle avoidance mode decision-making module and makes a decision on the braking mode; the braking mode includes a rear wheel braking mode and a four-wheel braking mode.

The obstacle avoidance execution module is based on four-wheel steering by wire technology and distributed drive technology, and includes four wheel hub motors, a four-wheel steering by wire actuator, a brake-by-wire system, and a wheel hub motor controller. The wheel hub motor controller receives the information transmitted by the steering decision module and the braking decision module to control the four wheel hub motors to drive the wire-controlled four-wheel steering actuator and the wire-controlled brake system to perform corresponding steering and braking behaviors; The obstacle avoidance execution module also includes the steering wheel assembly sub-module, which specifically includes the steering wheel, reducer, torque sensor, angle sensor, road-sensing motor, and road-sensing motor controller.

The obstacle avoidance mode decision-making module of the present invention also includes a voice warning sub-module and an information storage sub-module; the voice warning sub-module is used to warn people in the vehicle that there are obstacles ahead, and the vehicle will perform automatic obstacle avoidance behavior; The information storage module is used to store vehicle information, including mass, wheelbase, distance from the center of mass to the front axle, distance from the center of mass to the rear axle, front wheel cornering stiffness and rear wheel cornering stiffness.

In one embodiment, the environmental complexity refers to a ₁ with the center of mass of the vehicle as the geometric center, the longitudinal axis and the horizontal axis of the vehicle as the major axis and the minor axis of the ellipse respectively, and the length of the long axis as the length of the vehicle body. times and the weighted sum of the number of all vehicles and pedestrians covered within the ellipse whose short axis length is a ₂ times the width of the vehicle body. The parameters a ₁ and a ₂ described in the present invention depend on the vehicle body length, width, mass, road adhesion coefficient and the distance between the vehicle and the vehicle or obstacle in front. The specific calculation formula is as follows:

In the formula, k _L is the influence coefficient of the vehicle body length, k _u is the influence coefficient of road adhesion, k _d is the influence coefficient of the distance between the vehicle and the vehicle in front, pedestrians or obstacles, B is the length of the vehicle body in meters, and W is the width of the vehicle body. , the unit is meters, m is the mass of the car, the unit is kilograms, u is the road adhesion coefficient, which is a dimensionless quantity, d is the distance between the vehicle and the vehicle or obstacle in front, the unit is meters; k _L , k _u and k _d Set by the driver;

The specific calculation formula for the environmental complexity described is as follows:

In the formula, C _e is the environmental complexity, k _v is the weighting factor for the number of vehicles, k _p is the weighting factor for the number of pedestrians, N _v is the number of vehicles, and N _p is the number of pedestrians; k _v and k _p are set by the driver;

The specific steps for the obstacle avoidance mode decision-making module to decide the obstacle avoidance mode based on the current environmental complexity, vehicle speed and the distance between the vehicle and the vehicle in front are as follows:

S1, set the environmental complexity threshold, which is set by the user. Specifically, it includes the first environmental complexity threshold C _e1 and the second environmental complexity threshold C _e2 . The relationship between the two is as follows:

C _e1 =0.5×C _e2 ;

S2, calculates the comprehensive obstacle avoidance mode judgment index of the vehicle speed and the distance between the vehicle and the vehicle in front, hereinafter referred to as the comprehensive obstacle avoidance mode judgment index, represented by E _vd . The specific calculation formula is as follows:

In the formula, λ ₁ is the vehicle speed influence factor, λ ₂ is the distance influence factor, v is the vehicle speed, the unit is km/h;

At the same time, the threshold of E _vd is calculated, represented by E _vdt . The specific expression is as follows:

In the formula, v _min is the minimum speed value set by the user, which can be selected from 10, 20, and 30; d _max is the maximum distance value set by the user, and can be selected from 30, 40, and 50;

S3, the vehicle speed influence factor λ ₁ and the distance influence factor λ ₂ are determined using fuzzy control. The specific implementation method is as follows: The input of the fuzzy control method is the vehicle speed v and the distance d between the vehicle and the vehicle in front, pedestrians or obstacles. The fuzzy control method The control subset is defined as v={ZE, PS, PM, PB}, that is, {zero, positive small, positive middle, positive large}, d=={ZE, PS, PM, PB}, that is, {zero, positive small, positive middle , positive large}; the output is the vehicle speed influence factor λ ₁ ={ZE, PS, PM, PB}, that is, {zero, positive small, middle, positive large}, the distance influence factor λ ₂ ={ZE, PS, PM, PB}, That is {zero, positive small, positive middle, positive large};

S4, the obstacle avoidance mode is decided based on the environmental complexity and the comprehensive obstacle avoidance mode judgment index. It is specifically divided into three situations: S41, S42 and S43:

In case S41, if the environmental complexity of the vehicle is less than or equal to the first threshold of environmental complexity, it is further subdivided into the following situation S411 and situation S412 to determine the obstacle avoidance mode according to the comprehensive obstacle avoidance mode judgment index:

In case S411, if the comprehensive obstacle avoidance mode judgment index E _vd of the vehicle is less than the threshold E _vdt , the decision-making obstacle avoidance mode is type I obstacle avoidance mode (first obstacle avoidance mode);

In case S412, if the comprehensive obstacle avoidance mode judgment index E _vd of the vehicle is greater than or equal to the threshold E _vdt , the decision-making obstacle avoidance mode is the Type II obstacle avoidance mode (second obstacle avoidance mode);

In case S42, if the environmental complexity of the vehicle is greater than the first threshold of environmental complexity and less than the second threshold of environmental complexity, the decision-making obstacle avoidance mode is further subdivided into the following situation S421 and situation S422 based on the comprehensive obstacle avoidance mode judgment index:

In case S421, if the comprehensive obstacle avoidance mode judgment index E _vd of the vehicle is less than the threshold E _vdt , the decision-making obstacle avoidance mode is the Type II obstacle avoidance mode;

In case S422, if the comprehensive obstacle avoidance mode judgment index E _vd of the vehicle is greater than or equal to the threshold E _vdt , the decision-making obstacle avoidance mode is type III obstacle avoidance mode (third obstacle avoidance mode);

In case S43, if the environmental complexity of the vehicle is greater than or equal to the second threshold of environmental complexity, the decision-making obstacle avoidance mode is further subdivided into the following situation S431 and situation S432 based on the comprehensive obstacle avoidance mode judgment index:

In case S431, if the comprehensive obstacle avoidance mode judgment index E _vd of the vehicle is less than the threshold E _vdt , the decision-making obstacle avoidance mode is type III obstacle avoidance mode;

In case S432, if the vehicle's comprehensive obstacle avoidance mode judgment index E _vd is greater than or equal to the threshold E _vdt , the decision-making obstacle avoidance mode is type IV obstacle avoidance mode (the fourth obstacle avoidance mode).

The steering decision-making module and deceleration decision-making module of the present invention receive the obstacle avoidance mode signal sent by the obstacle avoidance mode decision-making module and make corresponding steering decisions and deceleration decisions. The specific steps are as follows:

S1, when the obstacle avoidance mode is type I obstacle avoidance mode, the deceleration decision-making module is silent, the steering decision-making module decides that the steering mode is the front wheel steering mode and sends the front wheel angle signal to the hub motor controller; the front wheel of the present invention The specific values of the rotation angle are as follows:

δ _f =δ _fd +λ _u ×cosu+λ _d ×exp(-d)+λ _v ×arctanv;

In the formula, δ _f is the front wheel angle; δ _fd is the default value of the front wheel angle, which is set by the user; λ _u is the road adhesion coefficient adjustment factor, λ _d is the distance adjustment factor, and λ _v is the vehicle speed adjustment factor, both Set by the user;

S2, when the obstacle avoidance mode is Type II obstacle avoidance mode, the steering decision module decides the steering mode to be the front wheel steering mode and sends the front wheel angle signal to the wheel hub motor controller, and the deceleration decision module decides the braking mode to be rear wheel braking. mode and send the braking force signal to the hub motor controller;

The specific values of the front wheel rotation angle are as follows:

δ _f =1.5×δ _fd +λ _u ×cosu+λ _d ×exp(-d)+λ _v ×arctanv;

The specific values of rear wheel braking force are as follows:

In the formula, F _brd is the default value of rear wheel braking force, which is set by the user; λ _m is the mass adjustment factor, λ _d is the distance adjustment factor, λ _u is the road adhesion coefficient adjustment factor, and λ _v is the vehicle speed adjustment factor. All are set by the user; F _br is the rear wheel braking force;

S3, when the obstacle avoidance mode is type III obstacle avoidance mode, the deceleration decision-making module is silent, and the steering decision-making module decides that the steering mode is four-wheel steering mode and sends the front wheel angle and rear wheel angle signals to the hub motor controller;

The specific values of the front wheel rotation angle are as follows:

δ _f =1.5×δ _fd +λ _u ×cosu+λ _d ×exp(-d)+λ _v ×arctanv;

The specific numerical values of the rear wheel rotation angle according to the present invention are as follows:

δ _r = G _d × δ _f + G _a × v × r;

In the formula, δ _r is the rear wheel turning angle, G _d is the ratio of the front and rear wheel steering angles, δ _f is the front wheel turning angle; G _a is the feedback gain of the vehicle's lateral motion state, which is taken as 0.001; r is the vehicle yaw. Angular velocity;

The specific formula of the ratio G _d of the front and rear wheel steering angles according to the present invention is as follows:

In the formula, a is the distance from the center of mass of the vehicle to the front axle, b is the distance from the center of mass of the vehicle to the rear axle, L is the wheelbase, C _f is the cornering stiffness of the front wheel, and C _r is the cornering stiffness of the rear wheel;

S4, when the obstacle avoidance mode is the IV obstacle avoidance mode, the steering decision module decides that the steering mode is the four-wheel steering mode and sends the front wheel angle and rear wheel angle signals to the wheel hub motor controller; the front wheel angle according to the present invention The specific values of the front wheel braking force and rear wheel angle are the same as those in S3; the deceleration decision module decides that the braking mode is four-wheel braking mode and sends the braking force signal to the wheel hub motor controller, and the specific values of the front wheel braking force and rear wheel braking force are as follows:

F _bf =G _b ×F _br ;

In the formula, G _b is the ratio of the front wheel braking force to the rear wheel braking force. The specific value can be trained by the neural network to obtain the appropriate value; F _bf is the front wheel braking force.

The early warning module of the present invention issues corresponding early warning information based on the obstacle avoidance mode determined by the obstacle avoidance mode decision-making module and reminds the driver to take over the vehicle when necessary. The specific steps are as follows:

S1, when the obstacle avoidance mode is Type I obstacle avoidance mode or Type II obstacle avoidance mode, the early warning module issues a normal voice prompt "Please keep the driver and passengers in a normal sitting posture. The obstacle avoidance behavior ahead can be operated independently by the vehicle, and the driver does not need to take over." ";

S2, when the obstacle avoidance mode is Type III obstacle avoidance mode or Type IV obstacle avoidance mode, the early warning module issues different voice prompts based on the hazard characteristic coefficient D. The specific steps are as follows:

S21, when the hazard characteristic coefficient D is less than the hazard characteristic coefficient threshold D _t , the early warning module issues a normal voice prompt "Please keep the driver and passengers in a normal sitting posture. The obstacle avoidance behavior ahead can be operated independently by the vehicle, and the driver does not need to take over";

S22, when the hazard characteristic coefficient D is greater than or equal to the hazard characteristic coefficient threshold D _t , the early warning module issues an emergency voice prompt "If the obstacle avoidance behavior ahead is operated autonomously by the vehicle, there may be a risk of collision. Please keep the driver alert and be ready to take over at any time."vehicle".

The hazard characteristic coefficient D described in the present invention is used to evaluate the degree of danger of the current obstacle avoidance environment. It is positively correlated with the degree of danger of the obstacle avoidance environment. The specific formula is as follows:

In the formula, D is the hazard characteristic coefficient; K is the obstacle avoidance environment level value, which is determined based on the road slope, road adhesion coefficient and environmental visibility. The specific determination method is as follows:

In the formula, k ₁ , k ₂ and k ₃ are the weighting coefficients of road slope, road adhesion coefficient and environmental visibility respectively, which are set by the user, and k ₁ + k ₂ + k ₃ = 1; i is the road slope; V is the environmental visibility, the unit is m;

The risk characteristic coefficient threshold D _t described in the present invention is set by the user.

This provided obstacle avoidance control method suitable for wire-controlled four-wheel steering vehicles comprehensively considers the vehicle's obstacle avoidance environment, parameters and various indicators to determine the obstacle avoidance mode, and performs corresponding automatic obstacle avoidance behaviors. The driver can intervene when necessary to improve It improves the car's obstacle avoidance safety and reduces the driver's burden.

Although the embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the description and embodiments. They can be applied to various fields suitable for the present invention. For those familiar with the art, they can easily Additional modifications may be made, and the invention is therefore not limited to the specific details and illustrations shown and described herein without departing from the general concept defined by the claims and equivalent scope.

Claims (10)

1. An obstacle avoidance control method suitable for wire-controlled four-wheel steering vehicles, which is characterized by including the following steps: Step 1: Obtain the road adhesion coefficient, slope, vehicle speed, yaw angular velocity, and distance between the vehicle and the vehicle in front, pedestrians or obstacles through sensors; Step 2: Determine the current environmental complexity C _e and comprehensive obstacle avoidance mode judgment index E _vd of the vehicle: Among them, kv is the weighting factor for the number of vehicles, kp is the weighting factor for the number of pedestrians, Nv is the number of vehicles, and Np is the number of pedestrians; λ ₁ is the influence factor of vehicle speed, λ ₂ is the influence factor of distance, v is the vehicle speed, and d is the distance between the vehicle and the front distance to vehicles or obstacles; Step 3: Control the vehicle to enter the corresponding obstacle avoidance mode according to the complexity of the environment and the comprehensive obstacle avoidance mode. 2. The obstacle avoidance control method suitable for wire-controlled four-wheel steering vehicles according to claim 1, characterized in that the obstacle avoidance mode includes: a first obstacle avoidance mode, a second obstacle avoidance mode, and a third obstacle avoidance mode. mode and the fourth obstacle avoidance mode; If C _e ≤ C _e1 and E _vd < E _vdt , the vehicle is controlled to enter the first obstacle avoidance mode; If C _e ≤ _{C e1} and E _vd ≥ E _vdt or C _e1 <C _e <C _e2 and E _vd <E _vdt , control the vehicle to enter the second obstacle avoidance mode; If C _e1 <C _e <C _e2 and E _vd ≥E _vdt or C _e >C _e1 and E _vd <E _vdt , the vehicle is controlled to enter the third obstacle avoidance mode; If C _e > C _e1 and E _vd ≥ E _vdt , the vehicle is controlled to enter the fourth obstacle avoidance mode; Among them, C _e1 is the first threshold of environmental complexity, C _e2 is the second threshold of environmental complexity, and E _vdt is the comprehensive obstacle avoidance mode judgment index threshold. 3. The obstacle avoidance control method suitable for wire-controlled four-wheel steering vehicles according to claim 2, characterized in that when the obstacle avoidance mode is the first obstacle avoidance mode: Controlling the front wheel rotation angle is: δ _f =δ _fd +λ _u ×cosu+λ _d ×exp(-d)+λ _v ×arctanv; Among them, δ _f is the front wheel angle; δ _fd is the default value of the front wheel angle; λ _u is the road adhesion coefficient adjustment factor, λ _d is the distance adjustment factor, λ _v is the vehicle speed adjustment factor, and d is the distance between the vehicle and the vehicle or obstacle in front. distance of objects. 4. The obstacle avoidance control method suitable for wire-controlled four-wheel steering vehicles according to claim 3, characterized in that when the obstacle avoidance mode is the second obstacle avoidance mode: Controlling the front wheel rotation angle is: δ _f =1.5×δ _fd +λ _u ×cosu+λ _d ×exp(-d)+λ _v ×arctanv; The rear wheel braking force is controlled as: Among them, F _brd is the default value of the rear wheel braking force; λ _m is the mass adjustment factor, λ _d is the distance adjustment factor, λ _u is the road adhesion coefficient adjustment factor, λ _v is the vehicle speed adjustment factor; F _br is the rear wheel braking force, m is the mass of the car. 5. The obstacle avoidance control method suitable for wire-controlled four-wheel steering vehicles according to claim 3 or 4, characterized in that when the obstacle avoidance mode is the third obstacle avoidance mode: Controlling the front wheel rotation angle is: δ _f =1.5×δ _fd +λ _u ×cosu+λ _d ×exp(-d)+λ _v ×arctanv; The rear wheel rotation angle is controlled as: δ _r = G _d × δ _f + G _a × v × r In the formula, δ _r is the rear wheel steering angle, G _d is the ratio of the front and rear wheel steering angles, δ _f is the front wheel steering angle; G _a is the feedback gain of the vehicle's lateral motion state; r is the vehicle yaw angular velocity, v is vehicle speed. 6. The obstacle avoidance control method suitable for wire-controlled four-wheel steering vehicles according to claim 5, characterized in that when the obstacle avoidance mode is the fourth obstacle avoidance mode: Controlling the front wheel rotation angle is: δ _f =1.5×δ _fd +λ _u ×cosu+λ _d ×exp(-d)+λ _v ×arctanv; The rear wheel rotation angle is controlled as: δ _r = G _d × δ _f + G _a × v × r The rear wheel braking force is controlled as: The front wheel braking force is controlled as: F _bf =G _b ×F _br ; Among them, G _b is the ratio of the front wheel braking force to the rear wheel braking force; F _bf is the front wheel braking force. 7. The obstacle avoidance control method suitable for wire-controlled four-wheel steering vehicles according to claim 6, characterized in that the comprehensive obstacle avoidance mode judgment index threshold is calculated by the following formula: Among them, v _min is the minimum value of vehicle speed set by the user, d _max is the maximum value of distance set, λ ₁ is the vehicle speed influence factor, and λ ₂ is the distance influence factor. 8. The obstacle avoidance control method suitable for wire-controlled four-wheel steering vehicles according to claim 7, characterized in that a fuzzy control method is used to determine the vehicle speed influence factor λ ₁ and the distance influence factor λ ₂ : The vehicle speed v and the distance d between the vehicle and the vehicle in front, pedestrians or obstacles are used as inputs to the fuzzy control model. The fuzzy control subset is defined as v={ZE, PS, PM, PB}, d={ZE, PS, PM ,PB}; The output of the fuzzy control model is the vehicle speed influence factor λ ₁ ={ZE, PS, PM, PB} and the distance influence factor λ ₂ ={ZE, PS, PM, PB}. 9. The obstacle avoidance control method suitable for wire-controlled four-wheel steering vehicles according to claim 8, characterized in that it further includes: When the obstacle avoidance mode is the third obstacle avoidance mode or the fourth obstacle avoidance mode, compare the hazard characteristic coefficient D with the hazard characteristic coefficient threshold D _t : If the hazard characteristic coefficient D ≥ D _t , a takeover prompt message is sent to the driver; in, In the formula, K is the obstacle avoidance environment level value. 10. The obstacle avoidance control method suitable for wire-controlled four-wheel steering vehicles according to claim 9, characterized in that the obstacle avoidance environment level value is calculated according to the following formula: In the formula, k ₁ , k ₂ and k ₃ are the weighting coefficients of road slope, road adhesion coefficient and environmental visibility respectively, and k ₁ + k ₂ + k ₃ = 1; i is the road slope; V is the environmental visibility, u is Road adhesion coefficient.