Vehicle self-adaptive cruise control method with steering collision avoidance function

Abstract

The invention relates to a vehicle self-adaptive cruise control method with a steering collision avoidance function, which comprises the following steps of: constructing a collision avoidance critical longitudinal safety distance model for determining collision avoidance critical longitudinal safety distances corresponding to different front vehicle driving states; obtaining a current actual vehicle distance, comparing the actual vehicle distance with the collision avoidance critical longitudinal safety distance, and if the actual vehicle distance is smaller than the collision avoidance critical longitudinal safety distance, adopting a steering collision avoidance trajectory planning algorithm based on a quintic polynomial, and combining a steering collision avoidance trajectory tracking control method based on MPC to obtain a steering collision avoidance trajectory. Vehicle steering collision avoidance control is realized; and otherwise, realizing vehicle following control based on a vehicle following control method of adaptive adjustment of the performance index weight coefficient of the MPC. Compared with the prior art, the safety, the vehicle following performance, the comfort and the economical efficiency of the vehicle in the vehicle following mode can be dynamically coordinated, and when the vehicle following braking distance is insufficient, the vehicle can be switched to the steering collision avoidance mode in time so as to avoid collision accidents.

Description

Technical Field

[0001] The invention relates to the technical field of vehicle control, and in particular to a vehicle adaptive cruise control method with a steering collision avoidance function. Background Art

[0002] ACC (Adaptive Cruise Control) is an assisted driving system that can automatically adjust the speed of the vehicle according to the speed of the vehicle in front to ensure driving safety. It obtains the driving status of surrounding vehicles through sensors and controls the acceleration and deceleration of the vehicle to achieve following driving. At present, the ACC system is based on longitudinal following control in most cases to ensure the safe driving of the vehicle, which requires a sufficient safety distance between the front and rear vehicles. However, if there is an emergency such as a sudden stop of the front vehicle or a sudden exit of a vehicle at an intersection in a blind spot, the longitudinal following braking safety distance is insufficient, and it is difficult to avoid a collision accident through following control, but it is possible through steering collision avoidance control. Therefore, in order to expand the use scenarios of the ACC system and increase active safety, it is necessary to improve the ACC system so that it can perform steering collision avoidance driving when the longitudinal following braking safety distance is insufficient to ensure the safe driving of the vehicle.

[0003] In order to ensure that the vehicle can make accurate decisions on the following mode and the steering collision avoidance mode, the rationality of the establishment of the critical safety model is an important prerequisite. At present, the commonly used safety models are divided into safety models based on collision time and safety models based on relative vehicle distance. Compared with the safety model based on collision time, the safety model based on relative vehicle distance does not rely on a large amount of test data support. It is mainly analyzed through the braking process, vehicle distance and driving status of the two vehicles, which is the focus of current research. At present, in terms of the design of control strategies, the difficulty of the following mode lies in how to coordinate and optimize various performance indicators (safety, following, comfort and economy) so that it can adapt to complex and changeable driving environments, while the difficulty of the steering collision avoidance mode lies in how to plan a smooth and continuous steering collision avoidance trajectory in real time, and design a control method that takes into account both tracking accuracy and real-time performance. Summary of the invention

[0004] The purpose of the present invention is to overcome the defects of the above-mentioned prior art and to provide a vehicle adaptive cruise control method with a steering collision avoidance function, which can dynamically coordinate multiple performance indicators of the vehicle in the following mode. When the following vehicle braking distance is insufficient, it can switch to the steering collision avoidance mode in time to avoid the occurrence of collision accidents.

[0005] The object of the present invention can be achieved by the following technical solution: A vehicle adaptive cruise control method with a steering collision avoidance function comprises the following steps:

[0006] Construct a critical longitudinal safety distance model for collision avoidance to determine the critical longitudinal safety distance for collision avoidance corresponding to different driving states of the leading vehicle;

[0007] Obtain the current actual vehicle distance, compare the actual vehicle distance with the critical longitudinal safety distance for collision avoidance, and if the actual vehicle distance is less than the critical longitudinal safety distance for collision avoidance, adopt a steering collision avoidance trajectory planning algorithm based on a quintic polynomial, combined with a steering collision avoidance trajectory tracking control method based on MPC (Model predictive control) to achieve vehicle steering collision avoidance control;

[0008] Otherwise, the vehicle following control method based on the adaptive adjustment of the performance index weight coefficient of MPC is used to realize the vehicle following control.

[0009] Furthermore, the process of constructing the collision avoidance critical longitudinal safety distance model includes:

[0010] Considering the motion state of the front and rear vehicles, the actual vehicle distance, the road adhesion coefficient and the pressure building limit capacity of the braking system, the critical longitudinal safety distance models for collision avoidance under different driving states of the front vehicle are established respectively. The driving states of the front vehicle include the front vehicle being stationary, the front vehicle being at a constant speed and the front vehicle being in emergency braking.

[0011] Furthermore, when the leading vehicle is stationary, the self-vehicle follows the vehicle at the maximum deceleration and decelerates to 0. At this time, the corresponding collision avoidance critical longitudinal safety distance model is:

[0012]

[0013] Among them, τ′ b is the brake dead time, τ″ b is the deceleration response time, d min is the minimum safety distance, a bmax is the maximum braking deceleration, v e is the vehicle speed, when the actual vehicle distance d act Greater than S e_min When the vehicle does not need to make an emergency turn, it can make full use of d act Following the vehicle, the expected braking deceleration of the vehicle is:

[0014]

[0015] Among them, a des_s is the expected braking deceleration of the vehicle when the front vehicle is stationary.

[0016] Furthermore, when the front vehicle is at a constant speed, the vehicle adjusts its speed to the same as that of the front vehicle, and the distance traveled is less than the actual vehicle-to-vehicle distance. At this time, the corresponding critical longitudinal safety distance model for collision avoidance is:

[0017]

[0018] In this case, the ego vehicle uses the actual vehicle-to-vehicle distance to complete the following process. At this time, the expected braking deceleration of the ego vehicle is:

[0019]

[0020] Among them, v p is the speed of the preceding vehicle, a des_c is the expected braking deceleration of the vehicle when the front vehicle is at a constant speed.

[0021] Furthermore, when the preceding vehicle is in emergency braking state, the preceding vehicle brakes at the maximum deceleration a. pmax During emergency braking, when the vehicle decelerates to 0, the relative distance between the vehicle and the front vehicle is greater than 0. At this time, the distance traveled by the front vehicle is:

[0022]

[0023] The corresponding collision avoidance critical longitudinal safety distance model is:

[0024]

[0025] At this time, the expected braking deceleration corresponding to the actual vehicle-to-vehicle distance is:

[0026]

[0027] Among them, a des_e It is the expected braking deceleration of the vehicle when the front vehicle brakes in an emergency.

[0028] Furthermore, the steering collision avoidance trajectory planning algorithm based on the quintic polynomial is used to solve the desired steering collision avoidance trajectory taking into account lateral motion constraints, heading angle constraints, displacement constraints, and lateral acceleration constraints.

[0029] Furthermore, the desired trajectory of steering collision avoidance is specifically:

[0030]

[0031] Among them, y e is the lateral displacement corresponding to the collision avoidance end point, t e is the time required to complete the collision avoidance, and t is the time independent variable.

[0032] Furthermore, the MPC-based steering collision avoidance trajectory tracking control method is used to solve the optimal control quantity for tracking the desired trajectory of steering collision avoidance by rolling optimization, and the corresponding objective function and constraint conditions to be solved are:

[0033]

[0034] in, H is the control increment weight coefficient matrix, G1 is the control amount weight coefficient matrix, e k is the error between the prediction model and the reference value in the prediction time domain, y1(k+i) is the actual output of the system, Q1 and R1 are weight matrices, ρ is the weight coefficient of the relaxation factor, ε1 is the relaxation factor, u1 and Δu1 are the actual control quantity and actual control quantity error of the system, respectively.

[0035] Furthermore, the following vehicle control method with adaptive adjustment of performance index weight coefficient based on MPC is used to optimize the expected deceleration of the following vehicle in real time, and the corresponding objective function and constraint conditions to be solved are:

[0036]

[0037] in, U is the set of control variables, including the front wheel steering angle and the desired acceleration. ε is the vector relaxation factor. A H is the coefficient matrix of the constraint conditions, which are the upper and lower limits of the front wheel steering angle and the expected acceleration, b H is the constant term matrix of the constraints.

[0038] Furthermore, in the vehicle following control method based on adaptive adjustment of performance index weight coefficients of MPC, performance index adaptive fuzzy logic rules are designed according to the following four scenarios:

[0039] (1) When the ego vehicle speed is greater than the front vehicle and the actual inter-vehicle distance is less than the expected value, there is a risk of collision between the vehicles. The ego vehicle should be decelerated quickly. At this time, the weight coefficients of the relative speed and inter-vehicle distance error need to be increased, the restriction on the ego vehicle acceleration needs to be removed, and the weight coefficient of the ego vehicle acceleration needs to be reduced, thereby increasing the response speed of the following vehicle control.

[0040] (2) When the speed of the ego vehicle is greater than that of the front vehicle and the actual inter-vehicle distance is greater than the expected value, in order to ensure the following performance of the vehicle, the ego vehicle should first be controlled to decelerate and then accelerate to follow the front vehicle. As the relative speed of the two vehicles approaches 0, the weight coefficient of the relative speed should be gradually reduced, while the weight coefficient of the ego vehicle acceleration should be increased.

[0041] (3) When the speed of the ego vehicle is lower than that of the preceding vehicle and the actual inter-vehicle distance is lower than the expected value, the weight coefficient of the distance error should be increased first, and the weight coefficient of the ego vehicle acceleration should be increased at the same time. As the relative speed approaches zero, the weight coefficient of the relative speed should be gradually reduced.

[0042] (4) When the speed of the ego vehicle is lower than that of the front vehicle and the actual inter-vehicle distance is greater than the expected value, there is no risk of collision between the vehicles. The weight coefficient of the ego vehicle acceleration should be appropriately increased. In this scenario, the weight coefficients of the relative speed and distance error should be gradually reduced.

[0043] Compared with the prior art, the present invention has the following advantages:

[0044] The present invention comprehensively considers multiple factors such as the driving state of the leading vehicle, the road adhesion coefficient, the pressure building limit capacity of the braking system, etc., and establishes a critical longitudinal safety distance model for collision avoidance corresponding to different driving states of the leading vehicle. According to the model, the driving mode is divided into a following mode and a steering collision avoidance mode, so as to accurately decide the driving state that the vehicle should adopt; for the two driving states, a following control method with adaptive adjustment of performance indicator weight coefficients and a steering collision avoidance trajectory tracking control method are proposed under the MPC framework, which can dynamically coordinate the safety, following performance, comfort and economy of the vehicle in the following mode. When the following braking distance is insufficient, it can switch to the steering collision avoidance mode in time to avoid the occurrence of collision accidents, thereby improving the collision avoidance capability and safety in emergency situations, expanding the application scenarios of the ACC system, and being beneficial to improving the overall safety of road traffic.

[0045] The present invention is designed to enable the vehicle to steer for collision avoidance when the actual vehicle distance is less than the critical longitudinal safety distance for collision avoidance. A steering collision avoidance trajectory planning algorithm based on a quintic polynomial is proposed in combination with the lateral collision avoidance constraint conditions that take into account vehicle dynamics and road adhesion characteristics. On this basis, a steering collision avoidance trajectory tracking control method based on MPC is proposed. The objective function of the method comprehensively considers the trajectory tracking control accuracy, the smooth transition characteristics of the actuator, the collision avoidance output boundary limit and the actuator capacity, and can ensure real-time and accurate steering collision avoidance control.

[0046] The present invention is designed to follow the vehicle when the actual vehicle-to-vehicle distance is greater than the critical longitudinal safety distance for collision avoidance. The vehicle's followability, safety, comfort and economy are comprehensively considered, and a following control method with adaptive adjustment of performance index weight coefficients based on MPC is proposed to optimize the expected deceleration of following the vehicle in real time to meet the driver's following needs under different working conditions. In addition, performance index adaptive fuzzy logic rules are designed according to four scenarios taking into account the actual following driving process, thereby effectively improving the reliability of following control. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic diagram of the method flow of the present invention;

[0048] Figure 2 It is a schematic diagram of the application process of the embodiment;

[0049] Figure 3Schematic diagram of the critical longitudinal safety distance model for collision avoidance in the embodiment;

[0050] Figures 4a to 4d A schematic diagram showing the comparison of the simulation results of the following vehicle control in the embodiment;

[0051] Figures 5a to 5f Schematic diagram of comparison of steering collision avoidance control simulation results in the embodiment. DETAILED DESCRIPTION

[0052] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0053] Example

[0054] like Figure 1 As shown, a vehicle adaptive cruise control method with a steering collision avoidance function comprises the following steps:

[0055] Construct a critical longitudinal safety distance model for collision avoidance to determine the critical longitudinal safety distance for collision avoidance corresponding to different driving states of the leading vehicle;

[0056] Obtain the current actual vehicle distance and compare it with the critical longitudinal safety distance for collision avoidance. If the actual vehicle distance is less than the critical longitudinal safety distance for collision avoidance, adopt a steering collision avoidance trajectory planning algorithm based on quintic polynomials and combine it with a steering collision avoidance trajectory tracking control method based on MPC to achieve vehicle steering collision avoidance control.

[0057] Otherwise, the vehicle following control method based on the adaptive adjustment of the performance index weight coefficient of MPC is used to realize the vehicle following control.

[0058] This embodiment applies the above solution, such as Figure 2 As shown, the main contents are:

[0059] Step 1: Comprehensively consider the motion state of the front and rear vehicles, the actual vehicle distance, the road adhesion coefficient and the pressure building limit capacity of the braking system, and establish three different collision avoidance critical longitudinal safety distance models according to the different driving states of the front vehicle;

[0060] Step 2: When the actual vehicle-to-vehicle distance is less than the critical longitudinal safety distance for collision avoidance, the vehicle turns to avoid collision. A steering collision avoidance trajectory planning algorithm based on quintic polynomials is proposed, combining the lateral collision avoidance constraints that consider vehicle dynamics and road adhesion characteristics. On this basis, a steering collision avoidance trajectory tracking control method based on MPC is designed. In designing the objective function, the trajectory tracking control accuracy, actuator smooth transition characteristics, collision avoidance output boundary restrictions, and actuator capabilities are comprehensively considered.

[0061] Step 3: When the actual vehicle distance is greater than the critical longitudinal safety distance for collision avoidance, the vehicle follows the vehicle. Taking into account the vehicle's followability, safety, comfort and economy, a following control method with adaptive adjustment of the performance index weight coefficient based on MPC is proposed to optimize the expected deceleration of the following vehicle in real time.

[0062] In step 1, the motion state of the front and rear vehicles, the actual vehicle distance, the road adhesion coefficient and the braking system pressure limit capacity are comprehensively considered. Assuming that the driving state of the front vehicle remains unchanged, three situations are considered: the front vehicle is stationary, the front vehicle is moving at a constant speed, and the front vehicle is braking urgently. Under the three different driving states of the front vehicle, the corresponding collision avoidance critical longitudinal safety distance model (such as Figure 3 As shown) as follows:

[0063] (1) The vehicle ahead is stationary

[0064] When the leading vehicle is stationary, the self-vehicle follows the vehicle at the maximum deceleration and decelerates to 0. At this time, the critical longitudinal safety distance model for collision avoidance is:

[0065]

[0066] Among them, τ′ b is the brake dead time, τ″ b is the deceleration response time, when the actual vehicle distance d act Greater than S e_min When the vehicle does not need to make an emergency turn, it can make full use of d act Follow the vehicle. The expected braking deceleration of the vehicle is a des_s :

[0067]

[0068] (2) The vehicle ahead has a constant speed

[0069] When the front vehicle is traveling at a constant speed, the self-vehicle only needs to adjust its speed to the same as that of the front vehicle and the distance traveled is less than the actual vehicle-to-vehicle distance to avoid a collision. At this time, the collision avoidance critical longitudinal safety distance model is:

[0070]

[0071] In this case, the ego vehicle can fully utilize the actual vehicle-to-vehicle distance to complete the following vehicle. At this time, the expected braking deceleration a of the ego vehicle is des_c :

[0072]

[0073] (3) Emergency braking of the vehicle ahead

[0074] The current vehicle is braking at maximum deceleration a pmaxDuring emergency braking, when the vehicle decelerates to 0, the relative distance between the vehicle in front is greater than 0, which is the critical condition for collision avoidance in this case. At this time, the distance traveled by the vehicle in front is:

[0075]

[0076] The corresponding collision avoidance critical longitudinal safety distance model is:

[0077]

[0078] At this time, the vehicle fully utilizes the expected braking deceleration a corresponding to the actual vehicle-to-vehicle distance. dese for:

[0079]

[0080] According to the above model, decisions and judgments are made on the current driving state of the vehicle, and the driving state is divided into two types: when the actual vehicle distance is less than the critical longitudinal safety distance for collision avoidance, the vehicle turns to avoid collision; when the actual vehicle distance is greater than the critical longitudinal safety distance for collision avoidance, the vehicle follows the vehicle.

[0081] In step 2, when the actual vehicle distance is less than the critical longitudinal safety distance for collision avoidance, the vehicle turns to avoid collision. A steering collision avoidance trajectory planning algorithm based on quintic polynomial is proposed, taking into account the steering collision avoidance constraints of vehicle dynamics characteristics and road adhesion coefficient.

[0082] Define the steering collision avoidance trajectory based on the quintic polynomial:

[0083] y(x)=b0+b1x+b2x 2 +b3x 3 +b4x 4 +b5x 5

[0084] Among them, b i is the unknown constant, x is the longitudinal displacement, and y is the lateral displacement.

[0085] By taking the first-order and second-order derivatives of the above formula, we can get the lateral velocity and lateral acceleration of the vehicle:

[0086]

[0087] According to the road constraints, the fifth-order polynomial collision avoidance trajectory coefficients b0=b1=b2=0 can be obtained, and b3, b4, b5 satisfy the following relationship:

[0088]

[0089] Among them, x eis the longitudinal displacement corresponding to the collision avoidance end point, y e is the lateral displacement corresponding to the collision avoidance termination point. At the same time, we can get

[0090] The quintic polynomial collision avoidance trajectory can be organized as:

[0091]

[0092] Assume that the vehicle maintains a constant speed during the entire collision avoidance process, that is, x = v e t, where t is the collision avoidance time. When the vehicle avoids an obstacle, x e =v e t e , where t e is the time required to complete the collision avoidance. Therefore, the above trajectory formula is rewritten as a collision avoidance trajectory with time t as the independent variable:

[0093]

[0094] Since the heading angle of the vehicle is small during the collision avoidance process, the lateral acceleration of the vehicle is approximately equal to the lateral acceleration. Therefore, the lateral acceleration constraint condition is set as:

[0095] |a ey (t)|≤a ey_max

[0096] Among them, a ey (t) and a ey_max They are the lateral acceleration of the vehicle and the maximum lateral acceleration. The lateral acceleration generally does not exceed 0.35g. Considering the grading of lateral acceleration, the maximum lateral acceleration of the vehicle is set to 0.5μg.

[0097] By solving the value of t, we can get the lateral acceleration a of the vehicle: ey (t) is:

[0098]

[0099] The time for steering to avoid collision is calculated by the lateral acceleration that satisfies the stability constraint. The time needs to meet the following conditions:

[0100]

[0101] On this basis, a steering collision avoidance trajectory tracking control method based on MPC is designed, which comprehensively considers the trajectory tracking control accuracy, actuator smooth transition characteristics, collision avoidance output boundary limit and actuator capability. According to the emergency steering obstacle avoidance trajectory model and vehicle dynamics model, the state space equation of the control system is established:

[0102]

[0103] in, is the system state variable, u1=[δ f a des ] is the system control variable, e y is the lateral distance deviation, e θ is the heading angle deviation, θ des is the vehicle reference heading angle, e v is the speed error, e x is the longitudinal position error.

[0104] The nonlinear system model is linearized, and the continuous system needs to be further converted into a discrete system to facilitate sampling and calculation of trajectory deviation. The optimization objective function is established in the following form:

[0105]

[0106] Where N p1 and N c1 are the prediction time domain and control time domain of the system respectively, y1(k+i) is the actual output of the system, y 1_ref (k+i) is the reference output of the system, Q1 and R1 are weight matrices, ρ is the weight coefficient of the relaxation factor, and ε1 is the relaxation factor. In the formula, the first term represents the tracking accuracy of the target trajectory, the second term represents whether the vehicle can stably steer to avoid collision, and the third term represents the degree of relaxation of the solution.

[0107] The optimal control increment problem in the control domain is transformed into a constrained quadratic programming problem:

[0108]

[0109] In the formula, e k is the error between the prediction model and the reference value in the prediction time domain.

[0110] In each control cycle, the optimization solution is completed through quadratic programming to obtain a series of control increments and relaxation factors in the prediction time domain.

[0111]

[0112] 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:

[0113] u1(k)=u1(k-1)+Δu1 * (k)

[0114] Repeat the above steps in subsequent control cycles to repeatedly solve the optimal control quantity to achieve rolling optimization.

[0115] In step 3, when the actual vehicle distance is greater than the critical longitudinal safety distance for collision avoidance, the vehicle follows the vehicle. Taking into account the vehicle's followability, safety, comfort and economy, a following control method with adaptive adjustment of the performance index weight coefficient based on MPC is proposed to optimize the expected deceleration of the following vehicle in real time.

[0116] By analyzing the system's optimization objectives, we design the objective function to be optimized:

[0117]

[0118] Among them, y p (k+i|k) is the predicted output value of the system at time k for the future i-th step, y ref (k+i|k) is the expected value of the system at time k for the future i-th step, u(k+i) is the control amount of the system at time k for the i-th step, are the weight factors of vehicle distance error, relative vehicle speed, ego vehicle speed and ego vehicle acceleration respectively.

[0119] According to the designed objective function, performance indicators and related constraints, the multi-objective optimization control of the system is transformed into a quadratic programming problem, namely:

[0120]

[0121] in, U is the set of control variables, ε is the vector relaxation factor, A H is the coefficient matrix of the constraint conditions, b H is the constant term matrix of the constraints.

[0122] In the process of rolling optimization solution, if d, v, a, jerk, and u in the input system exceed the upper and lower limits of the hard constraints, the vector relaxation factor will gradually increase, and the upper and lower limits of the input and output constraints will be relaxed to further expand the feasible domain of the solution system and ensure that u(k+i|k) has an optimal solution. The first component of the control system input is used as the actual input of the system at the next moment and rolling optimization and prediction are performed.

[0123] Considering the actual vehicle-following driving process, the performance index adaptive fuzzy logic rules are designed according to the following four scenarios:

[0124] (1) When the ego vehicle speed is greater than the front vehicle and the actual inter-vehicle distance is less than the expected value, there is a risk of collision between the vehicles. The ego vehicle should be decelerated quickly. At this time, the weight coefficients of the relative speed and inter-vehicle distance error need to be increased, the restriction on the ego vehicle acceleration needs to be removed, and the weight coefficient of the ego vehicle acceleration needs to be reduced, thereby increasing the response speed of the following vehicle control.

[0125] (2) When the speed of the ego vehicle is greater than that of the front vehicle and the actual inter-vehicle distance is greater than the expected value, in order to ensure the following performance of the vehicle, the ego vehicle should first be controlled to decelerate and then accelerate to follow the front vehicle. As the relative speed of the two vehicles approaches 0, the weight coefficient of the relative speed should be gradually reduced, while the weight coefficient of the ego vehicle acceleration should be increased.

[0126] (3) When the speed of the ego vehicle is lower than that of the preceding vehicle and the actual inter-vehicle distance is lower than the expected value, the weight coefficient of the distance error should be increased first, and the weight coefficient of the ego vehicle acceleration should be increased at the same time. As the relative speed approaches zero, the weight coefficient of the relative speed should be gradually reduced.

[0127] (4) When the speed of the ego vehicle is lower than that of the front vehicle and the actual inter-vehicle distance is greater than the expected value, there is no risk of collision between the vehicles. The weight coefficient of the ego vehicle acceleration should be appropriately increased. In this scenario, the weight coefficients of the relative speed and distance error should be gradually reduced.

[0128] In order to prove the effectiveness of this solution, this embodiment verifies the effectiveness of the following vehicle control and steering collision avoidance control methods under two typical working conditions. Figures 4a to 4d The simulation results of the following car control are shown in Figure 2. Figures 5a to 5f The simulation results of steering collision avoidance control.

[0129] 1. Following vehicle control verification conditions

[0130] A vehicle suddenly cuts in at 30m ahead, traveling at a constant speed of 60km / h. The initial speed of the vehicle is 80km / h. After calculation, the actual distance between the vehicles is greater than the critical longitudinal safety distance for collision avoidance. At this time, the following vehicle control is adopted, and the control effect is as follows: Figures 4a to 4d As shown in the figure, during the whole process of following the vehicle, as the vehicle distance error decreases, its weight also gradually decreases; the speed and acceleration of the vehicle can change smoothly under the condition of satisfying multi-objective constraints.

[0131] 2. Steering Collision Avoidance Control Verification Conditions

[0132] The vehicle is traveling at a constant speed of 120 km / h and finds that a vehicle traveling at a constant speed of 30 km / h suddenly cuts in 40 meters ahead. After calculation, the critical longitudinal safety distance for collision avoidance is 46.08 meters. The actual distance between the two vehicles is less than this safety distance. The vehicle turns to avoid collision. The control effect is as follows: Figures 5a to 5f As shown in the figure, the lateral displacement tracking control effect of the vehicle during the steering collision avoidance process is good, and within the road width range, the range of change of the center of mass sideslip angle and the front wheel turning angle is not large, and the body posture is good.

[0133] In summary, this scheme improves the ACC system and proposes a vehicle adaptive cruise control method with steering collision avoidance function, which can dynamically coordinate the safety, following performance, comfort and economy of the vehicle in the following mode. When the following braking distance is insufficient, it can switch to the steering collision avoidance mode in time to avoid the occurrence of collision accidents.

Claims

1. A vehicle adaptive cruise control method with a steering collision avoidance function, characterized in that: The following steps are involved: Construct a critical longitudinal safety distance model for collision avoidance to determine the critical longitudinal safety distance for collision avoidance corresponding to different driving states of the leading vehicle; Obtain the current actual vehicle distance and compare it with the critical longitudinal safety distance for collision avoidance. If the actual vehicle distance is less than the critical longitudinal safety distance for collision avoidance, adopt a steering collision avoidance trajectory planning algorithm based on quintic polynomials and combine it with a steering collision avoidance trajectory tracking control method based on MPC to achieve vehicle steering collision avoidance control. Otherwise, the vehicle following control method based on the adaptive adjustment of the performance index weight coefficient of MPC is used to realize the vehicle following control.

2. The vehicle adaptive cruise control method with steering collision avoidance function according to claim 1, characterized in that: The process of constructing the collision avoidance critical longitudinal safety distance model includes: Considering the motion state of the front and rear vehicles, the actual vehicle distance, the road adhesion coefficient and the pressure building limit capacity of the braking system, the critical longitudinal safety distance models for collision avoidance under different driving states of the front vehicle are established respectively. The driving states of the front vehicle include the front vehicle being stationary, the front vehicle being at a constant speed and the front vehicle being in emergency braking.

3. The vehicle adaptive cruise control method with steering collision avoidance function according to claim 2, characterized in that: When the front vehicle is stationary, the self-vehicle follows the vehicle at the maximum deceleration and decelerates to 0. At this time, the corresponding critical longitudinal safety distance model for collision avoidance is: Among them, τ′ b is the brake dead time, τ″ b is the deceleration response time, d min is the minimum safety distance, a bmax is the maximum braking deceleration, v e is the vehicle speed, when the actual vehicle distance d act Greater than S e_min When the vehicle does not need to make an emergency turn, it can make full use of d act Following the vehicle, the expected braking deceleration of the vehicle is: Among them, a des_s is the expected braking deceleration of the vehicle when the front vehicle is stationary.

4. The vehicle adaptive cruise control method with steering collision avoidance function according to claim 3, characterized in that: When the front vehicle is at a constant speed, the vehicle adjusts its speed to the same as that of the front vehicle, and the distance traveled is less than the actual vehicle-to-vehicle distance. At this time, the corresponding critical longitudinal safety distance model for collision avoidance is: In this case, the ego vehicle uses the actual vehicle-to-vehicle distance to complete the following process. At this time, the expected braking deceleration of the ego vehicle is: Among them, v p is the speed of the preceding vehicle, a des_c is the expected braking deceleration of the vehicle when the front vehicle is at a constant speed.

5. The vehicle adaptive cruise control method with steering collision avoidance function according to claim 4, characterized in that: When the front vehicle is in emergency braking state, the front vehicle brakes at the maximum deceleration a pmax During emergency braking, when the vehicle decelerates to 0, the relative distance between the vehicle and the front vehicle is greater than 0. At this time, the distance traveled by the front vehicle is: The corresponding collision avoidance critical longitudinal safety distance model is: At this time, the expected braking deceleration corresponding to the actual vehicle-to-vehicle distance is: Among them, a des_e It is the expected braking deceleration of the vehicle when the front vehicle brakes in an emergency.

6. The vehicle adaptive cruise control method with steering collision avoidance function according to claim 1, characterized in that: The steering collision avoidance trajectory planning algorithm based on quintic polynomial is used to solve the desired steering collision avoidance trajectory taking into account lateral motion constraints, heading angle constraints, displacement constraints, and lateral acceleration constraints.

7. The vehicle adaptive cruise control method with steering collision avoidance function according to claim 6, characterized in that: The desired trajectory of steering collision avoidance is specifically: Among them, y e is the lateral displacement corresponding to the collision avoidance end point, t e is the time required to complete the collision avoidance, and t is the time independent variable.

8. The vehicle adaptive cruise control method with steering collision avoidance function according to claim 1, characterized in that: The MPC-based steering collision avoidance trajectory tracking control method is used to solve the optimal control quantity for tracking the desired trajectory of steering collision avoidance by rolling optimization. The corresponding objective function and constraint conditions to be solved are: in, H is the control increment weight coefficient matrix, G1 is the control amount weight coefficient matrix, e k is the error between the prediction model and the reference value in the prediction time domain, y1(k+i) is the actual output of the system, Q1 and R1 are weight matrices, ρ is the weight coefficient of the relaxation factor, ε1 is the relaxation factor, u1 and Δu1 are the actual control quantity and actual control quantity error of the system, respectively.

9. The vehicle adaptive cruise control method with steering collision avoidance function according to claim 1, characterized in that: The following vehicle control method with adaptive adjustment of performance index weight coefficient based on MPC is used to optimize the expected deceleration of the following vehicle in real time, and the corresponding objective function and constraint conditions to be solved are: in, U is the set of control variables, including the front wheel steering angle and the desired acceleration. ε is the vector relaxation factor. A H is the coefficient matrix of the constraint conditions, which are the upper and lower limits of the front wheel steering angle and the expected acceleration, d H is the constant term matrix of the constraints.

10. The vehicle adaptive cruise control method with steering collision avoidance function according to claim 9, characterized in that: In the vehicle following control method with adaptive adjustment of performance index weight coefficient based on MPC, performance index adaptive fuzzy logic rules are designed according to the following four scenarios: (1) When the ego vehicle speed is greater than the front vehicle and the actual inter-vehicle distance is less than the expected value, there is a risk of collision between the vehicles. The ego vehicle should be decelerated quickly. At this time, the weight coefficients of the relative speed and inter-vehicle distance error need to be increased, the restriction on the ego vehicle acceleration needs to be removed, and the weight coefficient of the ego vehicle acceleration needs to be reduced, thereby increasing the response speed of the following vehicle control. (2) When the speed of the ego vehicle is greater than that of the front vehicle and the actual inter-vehicle distance is greater than the expected value, in order to ensure the following performance of the vehicle, the ego vehicle should first be controlled to decelerate and then accelerate to follow the front vehicle. As the relative speed of the two vehicles approaches 0, the weight coefficient of the relative speed should be gradually reduced, while the weight coefficient of the ego vehicle acceleration should be increased. (3) When the speed of the ego vehicle is lower than that of the preceding vehicle and the actual inter-vehicle distance is lower than the expected value, the weight coefficient of the distance error should be increased first, and the weight coefficient of the ego vehicle acceleration should be increased at the same time. As the relative speed approaches zero, the weight coefficient of the relative speed should be gradually reduced. (4) When the speed of the ego vehicle is lower than that of the front vehicle and the actual inter-vehicle distance is greater than the expected value, there is no risk of collision between the vehicles. The weight coefficient of the ego vehicle acceleration should be appropriately increased. In this scenario, the weight coefficients of the relative speed and distance error should be gradually reduced.

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