Hierarchical control method for safety redundant collision avoidance system based on differential steering

Abstract

The invention relates to a hierarchical control method for a safety redundant collision avoidance system based on differential steering, which comprises the following steps of: in upper-layer control, performing coordinated control on longitudinal and transverse movement of a vehicle under an MPC framework based on a steering collision avoidance trajectory planning algorithm of a quintic polynomial, and outputting an expected acceleration and a front wheel steering angle signal for tracking a steering obstacle avoidance trajectory; in the lower-layer control, differential steering control based on SMC is combined with longitudinal vehicle speed tracking control based on feedforward-feedback, and a differential driving torque signal used for adjusting left and right front wheels of the vehicle is output and obtained; and the differential driving torque signal is transmitted to a wheel hub motor of the vehicle so as to correspondingly control and change the running state of the vehicle. Compared with the prior art, under the condition that an original steering system of the vehicle fails, an additional actuator does not need to be added, an original driving / braking system of the vehicle can be utilized, the driving / braking force of the left wheel and the right wheel is independently controlled, differential torque is generated, and the steering obstacle avoidance function of the vehicle is effectively achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle control, and in particular to a hierarchical control method of a safety redundant collision avoidance system based on differential steering. Background Art

[0002] In order to ensure driving safety, the obstacle avoidance function in the intelligent assisted driving system has received widespread attention. There are two implementation plans for the obstacle avoidance function: emergency braking or emergency steering. When the vehicle speed is high and the lateral overlap with the obstacle is small, steering can usually complete the obstacle avoidance function more effectively than braking. Therefore, the normal operation of the steering system is closely related to driving safety. A single active steering technology cannot guarantee driving safety, especially for the wire control steering system. It is also necessary to design an additional redundant system to ensure the driving safety of the vehicle.

[0003] When faced with sudden obstacles, applying trajectory planning algorithms under emergency conditions to help the driver plan a feasible obstacle avoidance trajectory is a prerequisite for ensuring the effectiveness of the safety redundancy system. The steering obstacle avoidance trajectory planning algorithm belongs to a local planning algorithm, which can be mainly divided into algorithms based on geometric models, dynamic search algorithms, and optimization algorithms. Both dynamic search and optimization are prone to falling into local optimality, complex calculations, and complex parameters. The geometric model algorithm can generate continuous and smooth trajectories to improve driving stability. It has high flexibility in the face of complex scenes, can quickly plan collision-free trajectories to ensure safety, and has low computational complexity and high real-time performance.

[0004] After the trajectory planning algorithm outputs the desired obstacle avoidance trajectory, the trajectory tracking control algorithm needs to track the desired trajectory with high precision. At present, redundant obstacle avoidance systems mostly adopt a hierarchical control architecture. In the upper-level control, most studies design two different longitudinal and lateral control algorithms separately, and few consider the coordination of the longitudinal and lateral directions under the same control framework; in the lower-level control, most studies choose to add additional actuators, but this technology usually increases the complexity of the vehicle structure. Summary of the invention

[0005] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a hierarchical control method for a safe redundant collision avoidance system based on differential steering. In the event that the original steering system of the vehicle fails, there is no need to add additional actuators. The original driving / braking system of the vehicle can be used to individually control the driving / braking forces of the left and right wheels to generate a differential torque, thereby realizing the steering and obstacle avoidance function of the vehicle.

[0006] The purpose of the present invention can be achieved by the following technical solutions: a hierarchical control method for a safety redundant collision avoidance system based on differential steering, in which, in the upper control, a steering collision avoidance trajectory planning algorithm based on a quintic polynomial is used to coordinate the longitudinal and lateral motions of the vehicle under the MPC (Model Predictive Control) framework, and the actual lateral displacement, longitudinal displacement, heading angle, and vehicle speed of the vehicle are used as inputs, and the desired acceleration and front wheel angle signals for tracking the steering obstacle avoidance trajectory are output;

[0007] In the lower-level control, the differential steering control based on SMC (sliding mode control) and the longitudinal vehicle speed tracking control based on feedforward-feedback take the actual yaw rate, vehicle speed and acceleration of the vehicle as inputs, and the output is the differential drive torque signal for adjusting the left and right front wheels of the vehicle;

[0008] The differential drive torque signal is transmitted to the vehicle's wheel hub motor to control and change the vehicle's driving state accordingly.

[0009] Furthermore, the steering collision avoidance trajectory planning algorithm based on the quintic polynomial specifically uses the quintic polynomial curve as the basic curve of the steering obstacle avoidance trajectory, and constructs a steering obstacle avoidance trajectory model with time as the independent variable in the global coordinate system to output the vehicle's lateral and longitudinal trajectories.

[0010] Furthermore, the steering obstacle avoidance trajectory model is specifically:

[0011]

[0012] Among them, x(t) is the horizontal trajectory, y(x) is the vertical trajectory, t is the time variable, and a0~a5 and b0~b5 are all constant terms.

[0013] Furthermore, the longitudinal and transverse trajectories of the vehicle include transverse and longitudinal positions, heading angles, and curvatures, and the heading angles and curvatures are respectively:

[0014]

[0015] Among them, θ r (t) is the heading angle, k r (t) is the curvature.

[0016] Furthermore, the process of coordinating the longitudinal and lateral motion of the vehicle under the MPC framework includes:

[0017] According to the steering obstacle avoidance trajectory model, a linear prediction model of longitudinal and lateral coordinated control is established, and the linear prediction model is discretized using the first-order difference quotient. At the same time, an objective function that considers trajectory tracking accuracy and lateral stability for collision avoidance is established.

[0018] Set corresponding constraints for the system's control quantity, control increment and controlled output quantity;

[0019] In each control cycle, the objective function is optimized through quadratic programming to obtain the expected acceleration and front wheel angle for tracking the steering obstacle avoidance trajectory in real time.

[0020] Furthermore, the linear prediction model is specifically:

[0021]

[0022] in, u=[δ f a des ] is the control quantity, is the state quantity, e y is the distance deviation, is the distance deviation, is the vehicle reference heading angle, e v is the speed error, e s is the longitudinal position error, C f is the cornering stiffness of the front wheel, C r is the cornering stiffness of the rear wheel, m is the vehicle mass, v x is the longitudinal velocity of the vehicle, δ f is the front wheel turning angle, a des is the expected acceleration.

[0023] Furthermore, the objective function is specifically:

[0024]

[0025] The constraints are specifically:

[0026]

[0027] Among them, N p and N c are the prediction time domain and control time domain of the system, η(k+i) is the actual output of the system, η ref (k+i) is the reference output of the system, Q and R are both weight matrices, ρ is the weight coefficient of the relaxation factor, ε is the relaxation factor, u(k+i) is the control quantity of the system, and Δu(k+i) is the control increment of the system.

[0028] Furthermore, the SMC-based differential steering control specifically selects the difference between the ideal yaw rate of the reference model and the yaw rate of the differential steering vehicle as the tracking error to design a sliding mode controller for converting the desired front wheel steering angle into the front wheel differential torque, that is, obtaining the required front wheel differential torque.

[0029] Furthermore, the expression of the sliding mode controller is:

[0030]

[0031] Where c = [C a C b ], is the sliding mode parameter matrix, ζ=ω d -ω, is the ideal yaw rate ω of the reference model d The difference between the yaw rate ω and the differential steering car;

[0032] The expression of the front wheel differential torque is:

[0033]

[0034] Where R is the radius of the vehicle tire, α f is the front wheel slip angle, α r is the rear wheel slip angle, I z is the moment of inertia, k z is a constant, l s The tire contact width.

[0035] Furthermore, the longitudinal vehicle speed tracking control based on feedforward-feedback specifically uses the expected torque and expected braking pressure obtained from the vehicle reverse driving model and reverse braking model as feedforward quantities, and the error between the expected acceleration and the actual acceleration as feedback quantity, and obtains the differential driving torque through PI control calculation.

[0036] Compared with the prior art, the present invention has the following advantages:

[0037] The present invention is designed in the upper control layer, based on the steering collision avoidance trajectory planning algorithm of the fifth-order polynomial, to coordinate the longitudinal and lateral movements of the vehicle under the MPC framework, with the actual lateral displacement, longitudinal displacement, heading angle, and vehicle speed of the vehicle as input, and the output is the desired acceleration and front wheel angle signal for tracking the steering obstacle avoidance trajectory; in the lower control layer, the differential steering control based on SMC, combined with the longitudinal vehicle speed tracking fusion control based on feedforward-feedback, takes the actual yaw angular velocity, vehicle speed, and acceleration of the vehicle as input, and outputs the differential drive torque signal for adjusting the left and right front wheels of the vehicle. Therefore, when the original steering system of the vehicle fails, it can ensure that the vehicle can safely complete the high-speed emergency steering obstacle avoidance driving by adjusting the differential drive torque of the two front wheels without increasing the hardware cost and adding additional actuators, thereby reducing the occurrence of vehicle collision accidents.

[0038] In the present invention, a steering obstacle avoidance trajectory planning algorithm based on quintic polynomial is proposed, and a trajectory tracking model is established in a global coordinate system to ensure the real-time performance of high-speed emergency steering obstacle avoidance trajectory planning, which can improve the real-time performance of control and make the steering obstacle avoidance trajectory tracking more fitting.

[0039] In the present invention, a longitudinal and lateral coordinated optimization control method based on MPC is proposed. A linear prediction model of longitudinal and lateral coordinated control is established according to the steering obstacle avoidance trajectory model, and the model is discretized using the first-order difference quotient. At the same time, an objective function considering trajectory tracking accuracy and lateral stability of obstacle avoidance is established. The objective function is optimized and solved through quadratic programming, and the expected acceleration and front wheel angle for tracking the steering obstacle avoidance trajectory can be obtained in real time, thereby coordinating the front wheel angle and longitudinal speed of the vehicle during emergency steering obstacle avoidance.

[0040] The present invention combines the SMC-based differential steering control with the feedforward-feedback-based longitudinal vehicle speed tracking control. On the one hand, a front wheel differential steering SMC controller is designed to convert the desired front wheel steering angle into the front wheel differential torque, thereby realizing the steering control of the vehicle. On the other hand, the differential torque of the front wheels is obtained based on the sliding film control, and combined with the desired driving torque and braking pressure calculated by the PI control, the vehicle can stably track the obstacle avoidance lane change trajectory after the original steering system of the vehicle fails. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0042] Figure 2 is a schematic diagram of an application framework of an embodiment;

[0043] Figure 3 Schematic diagram of vehicle obstacle avoidance and lane changing in the embodiment;

[0044] Figure 4 A schematic diagram of the lower layer control of a vehicle in an embodiment;

[0045] Figure 5a to 5c A comparison diagram of the driving path and stability in a dual-lane speed change lane-changing obstacle avoidance working condition in the embodiment;

[0046] Figure 6a-6b 3 is a comparison diagram of the vehicle speed and lateral acceleration in a dual-lane speed change and lane-changing obstacle avoidance condition in the embodiment. DETAILED DESCRIPTION

[0047] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0048] Example

[0049] like Figure 1As shown, a hierarchical control method for a safety redundant collision avoidance system based on differential steering is provided. In the upper-level control, a steering collision avoidance trajectory planning algorithm based on a quintic polynomial is used to coordinate the longitudinal and lateral motions of the vehicle under the MPC framework. The actual lateral displacement, longitudinal displacement, heading angle, and vehicle speed of the vehicle are used as inputs, and the desired acceleration and front wheel steering angle signals for tracking the steering obstacle avoidance trajectory are output.

[0050] In the lower-level control, the differential steering control based on SMC, combined with the longitudinal vehicle speed tracking control based on feedforward-feedback, takes the actual yaw rate, vehicle speed and acceleration of the vehicle as input, and outputs the differential drive torque signal used to adjust the left and right front wheels of the vehicle;

[0051] The differential drive torque signal is transmitted to the vehicle's wheel hub motor to control and change the vehicle's driving state accordingly.

[0052] This embodiment applies the above solution, such as Figure 2 As shown, the main contents are:

[0053] Step 1: In a two-lane scenario, in order to ensure the real-time performance of high-speed emergency steering obstacle avoidance trajectory planning, a steering obstacle avoidance trajectory planning algorithm based on quintic polynomial is proposed, and a trajectory tracking model is established in the global coordinate system to improve the real-time performance of control and make the steering obstacle avoidance trajectory tracking more fitting;

[0054] Step 2: In the upper-level control, a longitudinal and lateral collaborative optimization control method based on MPC is proposed to optimize in real time the desired acceleration and front wheel angle that can track the steering obstacle avoidance trajectory;

[0055] Step 3: In the lower-level control, a differential steering control based on SMC and a longitudinal vehicle speed tracking fusion control method based on feedforward plus feedback are proposed. In the event that the original steering system of the vehicle fails, the hardware cost will not be increased. The differential driving torque of the two front wheels is controlled to ensure that the vehicle can safely complete high-speed emergency steering and obstacle avoidance driving.

[0056] In step 1, considering the real-time nature of emergency steering trajectory planning, the curve interpolation method is selected as the trajectory planning method, and the quintic polynomial curve is used as the basic curve of the steering obstacle avoidance trajectory to ensure that the planned reference trajectory has the characteristics of continuous position, velocity and acceleration. In the global coordinate system, a steering obstacle avoidance trajectory model with time as the independent variable is constructed, such as Figure 3 shown.

[0057] The curve equation that defines the quintic polynomial is:

[0058]

[0059] According to the boundary conditions formed by the initial state of steering obstacle avoidance and the desired state, the correlation coefficient is solved. Considering that the steering obstacle avoidance trajectory model uses time t as the independent variable, the planned horizontal and vertical trajectories must be directly related to time t in order to track and control, so the following conversion is made:

[0060]

[0061] The heading angle and curvature of the reference trajectory can be obtained by the following formula:

[0062]

[0063] In the global coordinate system, the distance error and heading angle error between the vehicle position and the trajectory tracking target point are defined, and combined with the reference heading angle and curvature, a steering obstacle avoidance trajectory model is planned that includes lateral and longitudinal position, heading angle, curvature, longitudinal speed, and longitudinal acceleration constraints. Compared with the Frenet coordinate system, the global coordinate system can show strong adaptability to dynamic changes when planning trajectories, and can adjust trajectory planning in time according to changes in obstacles.

[0064] In step 2, in order to coordinate the front wheel angle and longitudinal speed of the vehicle during emergency steering to avoid obstacles, a longitudinal and lateral coordinated optimization control method based on MPC is proposed. The linear prediction model of longitudinal and lateral coordinated control is established according to the steering obstacle avoidance trajectory model. According to the longitudinal and lateral error diagram of the vehicle position, the linear prediction model of lateral and longitudinal integrated control under the MPC framework is obtained, and its state space equation is as follows:

[0065]

[0066] Control volume, is the state quantity, e y is the distance deviation, is the distance deviation, is the vehicle reference heading angle, e v is the speed error, e s is the longitudinal position error.

[0067] After that, the prediction model is linearized and discretized, and the relationship between the state quantity, control quantity and output quantity in the vehicle dynamics system can be expressed as:

[0068]

[0069] By linearizing the vehicle model established above, we can get the linear time-varying equation:

[0070]

[0071] In the formula,

[0072] By discretizing the above formula using the first-order difference quotient, we can get:

[0073] ξ(k+1)=A1(k)ξ(k)+B1(k)u(k)

[0074] Wherein, A1(k)=I+TA1(t), B1(k)=TB1(t), T is the sampling period, t is the sampling time, and I is the unit matrix.

[0075] In order to estimate the output of the system in the prediction domain, it is assumed that Then the new state space equation is:

[0076]

[0077] In the formula, Δu(k) is the control increment of the system at time k, Nx and Nu are the number of state variables and control variables respectively.

[0078] Then, an objective function considering trajectory tracking accuracy and lateral stability for collision avoidance is established, and constraints are set on the control quantity, control increment, and controlled output of the system to establish an optimization objective function in the following form:

[0079]

[0080] Where N p and N c are the prediction time domain and control time domain of the system, η(k+i) is the actual output of the system, η ref (k+i) is the reference output of the system, Q and R are weight matrices, ρ is the weight coefficient of the relaxation factor, and ε is the relaxation factor.

[0081] In order to ensure the safety and stability of the vehicle, constraints are set on the control quantity, control increment and controlled output of the system:

[0082]

[0083] Finally, the quadratic programming is solved to transform the optimal control increment problem in the control time domain into a quadratic programming problem with constraints:

[0084]

[0085] In the formula, e k is the error between the prediction model and the reference value in the prediction domain.

[0086] In each control cycle, the objective function is optimized through quadratic programming to obtain a series of control increments and relaxation factors in the prediction time domain.

[0087]

[0088] The first item in the sequence is used as the actual control increment to act on the system, and the actual control amount at the current moment can be expressed as:

[0089] u(k)=u(k-1)+Δu * (k)

[0090] The above steps are repeated in subsequent control cycles to repeatedly solve the optimal control quantity to achieve rolling optimization, and the expected acceleration and front wheel angle for tracking the steering obstacle avoidance trajectory are obtained in real time.

[0091] In step 3, the trajectory tracking lower layer control is as follows Figure 4 As shown, the differential torque of the front wheels is obtained through sliding film control, and combined with the desired driving torque and braking pressure calculated by PI control, the vehicle can stably track the obstacle avoidance lane change trajectory after the original steering system of the vehicle fails.

[0092] This scheme designs a front wheel differential steering SMC controller to convert the desired front wheel angle into the front wheel differential torque, thereby realizing the steering control of the vehicle. The difference between the ideal yaw rate of the reference model and the yaw rate of the differential steering vehicle is selected as the tracking error of the system to design a sliding mode controller to obtain the required differential torque.

[0093] Define the difference in yaw angular velocity between the two as ζ, then ζ=ω d -ω. The sliding mode controller is designed based on the linear switching function, and the following switching function is constructed:

[0094]

[0095] In the formula, c = [C a C b ] is the sliding mode parameter matrix. In order to reduce the jitter when the control system switches, the exponential reaching law is introduced:

[0096]

[0097] In the formula, k z is a constant.

[0098] Under the premise of reachability, the sliding mode controller should be designed to meet You can get:

[0099]

[0100] The front wheel differential torque required to achieve the same desired front wheel steering angle can be obtained, and its expression is as follows:

[0101]

[0102] This scheme is designed based on longitudinal vehicle speed tracking control with feedforward plus feedback, in which the function of the vehicle drive model is to achieve the desired acceleration by solving the driving torque. Assuming that the road the vehicle is traveling on is a straight road with a slope angle of α = 0, according to Newton's second law, the vehicle's driving balance equation can be expressed as:

[0103] F t =F f +F i +F w +F a

[0104] In the formula, F t is the vehicle driving force, F f is the rolling resistance, F i is the slope resistance, F w is the air resistance, F a is the acceleration resistance.

[0105] Electric vehicles driven by wheel hub motors do not have a transmission, and the transmission system is omitted, simplifying the chassis structure. Therefore, the expressions of the vehicle's driving force and remaining resistance are:

[0106]

[0107] Where, T t is the expected driving torque of the hub motor, g is the acceleration of gravity, f is the rolling resistance coefficient, C D is the air resistance coefficient, A is the frontal area, δ is the conversion coefficient of the vehicle's rotating mass, and a des is the expected acceleration of driving.

[0108] The expected driving torque expression of the hub motor is:

[0109]

[0110] The function of the braking model is to achieve the desired acceleration by solving the braking pressure. When the vehicle brakes, it is modeled according to the longitudinal dynamics model of the vehicle:

[0111] F b +F f +F w =-ma des

[0112] In the formula, F b The expected braking force.

[0113] Introduction of brake force and brake pressure P b The proportionality factor K b , then:

[0114] F b =K b ×P b

[0115] Where P b is the desired brake pressure.

[0116] The expected brake pressure is:

[0117]

[0118] The expected torque and expected braking pressure obtained by the reverse driving model and the reverse braking model are used as feedforward quantities, and the error between the expected acceleration and the actual acceleration is used as feedback quantity. Through PI control, it is made to approach zero quickly, thereby improving the response speed of the lower-level actuator and the overall stability of the system.

[0119] Define the error between the expected acceleration and the actual acceleration as e a =a des -a act , then the PI control model is expressed as:

[0120] u a =K p ·e a +K I ·∫e a dt

[0121] In order to verify the effectiveness of the hierarchical control method of the safety redundant obstacle avoidance system based on differential steering proposed in this scheme, this embodiment conducted relevant simulation experiments and compared the effects of the currently commonly used SMC-based lateral and PID-based longitudinal joint control. Under the dual-lane steering obstacle avoidance condition, the sensor detected that an obstacle suddenly appeared 100m in front of the vehicle, and the vehicle was traveling at a speed of 54km / h, requiring it to complete the steering obstacle avoidance operation within 100m.

[0122] in, Figure 5a to 5c The comparison diagram of obstacle avoidance trajectory and vehicle stability in dual-lane steering obstacle avoidance conditions; Figure 6a-6b The figure is a comparison chart of vehicle speed and lateral acceleration under the dual-lane acceleration steering obstacle avoidance condition.

[0123] In summary, this solution can use differential steering to control the vehicle to smoothly complete the acceleration and obstacle avoidance lane change when the original steering system of the vehicle fails. No additional actuator is added. The original driving / braking system of the vehicle is used to control the driving / braking force of the left and right wheels separately to generate differential torque to achieve the steering and obstacle avoidance function of the vehicle. When the original steering system of the vehicle fails, the trajectory tracking error can be controlled within a reasonable range, and the longitudinal speed can be followed. At the same time, the lateral stability change of the vehicle during the steering and obstacle avoidance process is also within a reasonable range, which improves the safety of the obstacle avoidance system and effectively reduces the occurrence of vehicle collision accidents.

Claims

1. A hierarchical control method for a safety redundant collision avoidance system based on differential steering, characterized in that: In the upper-level control, the steering collision avoidance trajectory planning algorithm based on the fifth-order polynomial is used to coordinate the longitudinal and lateral motion of the vehicle under the MPC framework. The actual lateral displacement, longitudinal displacement, heading angle, and vehicle speed of the vehicle are used as inputs, and the desired acceleration and front wheel angle signals for tracking the steering collision avoidance trajectory are output; In the lower-level control, the differential steering control based on SMC, combined with the longitudinal vehicle speed tracking control based on feedforward-feedback, takes the actual yaw rate, vehicle speed and acceleration of the vehicle as input, and outputs the differential drive torque signal used to adjust the left and right front wheels of the vehicle; The differential drive torque signal is transmitted to the vehicle's wheel hub motor to control and change the vehicle's driving state accordingly.

2. The hierarchical control method of a safety redundant collision avoidance system based on differential steering according to claim 1, characterized in that: The steering collision avoidance trajectory planning algorithm based on quintic polynomial specifically uses the quintic polynomial curve as the basic curve of the steering obstacle avoidance trajectory, and constructs a steering obstacle avoidance trajectory model with time as the independent variable in the global coordinate system to output the vehicle's lateral and longitudinal trajectories.

3. The hierarchical control method of a safety redundant collision avoidance system based on differential steering according to claim 2, characterized in that: The steering obstacle avoidance trajectory model is specifically: Among them, x(t) is the horizontal trajectory, y(x) is the vertical trajectory, t is the time variable, and a0~a5 and b0~b5 are all constant terms.

4. The hierarchical control method of a safety redundant collision avoidance system based on differential steering according to claim 3, characterized in that: The longitudinal and transverse trajectories of the vehicle include transverse and longitudinal positions, heading angles, and curvatures, wherein the heading angles and curvatures are respectively: Among them, θ r (t) is the heading angle, k r (t) is the curvature.

5. The hierarchical control method of a safety redundant collision avoidance system based on differential steering according to claim 3, characterized in that: The process of coordinated control of the longitudinal and lateral motion of the vehicle under the MPC framework includes: According to the steering obstacle avoidance trajectory model, a linear prediction model of longitudinal and lateral coordinated control is established, and the linear prediction model is discretized using the first-order difference quotient. At the same time, an objective function that considers trajectory tracking accuracy and lateral stability for collision avoidance is established. Set corresponding constraints for the system's control quantity, control increment and controlled output quantity; In each control cycle, the objective function is optimized through quadratic programming to obtain the expected acceleration and front wheel angle for tracking the steering obstacle avoidance trajectory in real time.

6. The hierarchical control method of a safety redundant collision avoidance system based on differential steering according to claim 5, characterized in that: The linear prediction model is specifically: Control volume, is the state quantity, e y is the distance deviation, is the distance deviation, is the vehicle reference heading angle, e v is the speed error, e s is the longitudinal position error, C f is the cornering stiffness of the front wheel, C r is the cornering stiffness of the rear wheel, m is the vehicle mass, v x is the longitudinal velocity of the vehicle, δ f is the front wheel turning angle, a des is the expected acceleration.

7. The hierarchical control method of a safety redundant collision avoidance system based on differential steering according to claim 6, characterized in that: The objective function is specifically: The constraints are specifically: Among them, N p and N c are the prediction time domain and control time domain of the system, η(k+i) is the actual output of the system, η ref (k+i) is the reference output of the system, Q and R are both weight matrices, ρ is the weight coefficient of the relaxation factor, ε is the relaxation factor, u(k+i) is the control quantity of the system, and Δu(k+i) is the control increment of the system.

8. The hierarchical control method of a safety redundant collision avoidance system based on differential steering according to claim 7, characterized in that: The SMC-based differential steering control specifically selects the difference between the ideal yaw rate of the reference model and the yaw rate of the differential steering vehicle as the tracking error to design a sliding mode controller for converting the desired front wheel steering angle into the front wheel differential torque, that is, obtaining the required front wheel differential torque.

9. The hierarchical control method of a safety redundant collision avoidance system based on differential steering according to claim 8, characterized in that: The expression of the sliding mode controller is: Where c = [C a C b ], is the sliding mode parameter matrix, ζ=< d -ω, is the ideal yaw rate ω of the reference model d The difference between the yaw rate ω and the differential steering car; The expression of the front wheel differential torque is: Where R is the radius of the vehicle tire, α f is the front wheel slip angle, α r is the rear wheel slip angle, I z is the moment of inertia, k z is a constant, l s The tire contact width.

10. The hierarchical control method of a safety redundant collision avoidance system based on differential steering according to claim 8, characterized in that: The longitudinal vehicle speed tracking control based on feedforward-feedback specifically uses the expected torque and expected braking pressure obtained from the vehicle reverse driving model and reverse braking model as feedforward quantities, uses the error between the expected acceleration and the actual acceleration as feedback quantity, and obtains the differential driving torque through PI control calculation.

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