A vehicle platoon variable parameter control system based on driving environment and vehicle state

Abstract

The application discloses a vehicle queue variable parameter control system based on driving environment and vehicle state, which comprises a vehicle state and driving environment sensing module, a vehicle decision control module and a brake driving execution module. The vehicle state and driving environment sensing module is used for calculating a driving environment scheduling coefficient and a vehicle state scheduling coefficient, and collecting information such as relative positions of vehicles in a queue in real time; the vehicle decision control module constructs a vehicle queue variable parameter control system according to the scheduling coefficients, calculates expected accelerations of the vehicles in the queue and sends the expected accelerations to the brake driving execution module; and the brake driving execution module corrects the expected accelerations according to a road adhesion coefficient, obtains expected tire forces and executes the expected tire forces, so as to realize vehicle queue control.

Description

Technical Field

[0001] This invention relates to the field of automotive technology, specifically a vehicle platooning variable parameter control system based on driving environment and vehicle status. Background Technology

[0002] With the continuous development of vehicle networking technology, vehicle queue control, as one of the application scenarios of vehicle-to-vehicle cooperative technology, has multiple advantages such as reducing fuel consumption, improving traffic efficiency and enhancing driving safety by controlling the following distance between vehicles.

[0003] Currently, there are many technologies related to vehicle platoon control. However, during vehicle operation, complex driving conditions and vehicle performance can severely affect the application of vehicle platoon control technology and significantly impact driving safety. To address this, this invention provides a variable parameter control system for vehicle platoon control based on driving environment and vehicle state. To solve the tracking data problem in following vehicle control, driving environment scheduling coefficients and vehicle state scheduling coefficients are designed as variable parameters characterizing driving conditions and vehicle driving performance. A variable parameter control system for following vehicle platoon control is designed and solved using robust stability theory to improve driving safety during vehicle platoon control. Summary of the Invention

[0004] The purpose of this invention is to provide a vehicle queuing variable parameter control system based on driving environment and vehicle status to solve the above-mentioned technical problems.

[0005] To solve the above-mentioned technical problems, the specific technical solution of the present invention is as follows:

[0006] A vehicle platooning variable parameter control system based on driving environment and vehicle status includes a vehicle status and driving environment perception module, a vehicle decision control module, and a braking drive execution module.

[0007] The vehicle status and driving environment perception module includes a scheduling coefficient calculation unit and a vehicle queue control status perception unit, wherein the scheduling coefficient calculation unit is used to calculate the driving environment scheduling coefficient ρ. e Vehicle status scheduling coefficient ρ v The driving environment scheduling coefficient ρ e Depends on road surface adhesion level μ v Air visibility level A v The vehicle state scheduling coefficient ρ v Depends on vehicle acceleration capability A a Vehicle deceleration capability A d The vehicle queue control status sensing unit is used to number the vehicles in the queue and collect the relative distance d between the vehicles in the queue in real time. iThe relative speed v of the vehicles in the queue i And the acceleration a of the vehicles in the queue Vi Information such as...

[0008] ρ e =ρ e (μ v A v ),

[0009] ρ v =ρ v (A a A d )

[0010] In the formula, μ v Indicates the road surface adhesion level, A v A represents the level of air visibility. a Indicates the vehicle's acceleration capability, A d This indicates the vehicle's deceleration capability.

[0011] The vehicle decision control module is based on the driving environment scheduling coefficient ρ. e With vehicle state scheduling coefficient ρ v A variable parameter control system for the vehicle queue is constructed, the desired acceleration of the vehicles in the queue is calculated and sent to the braking drive execution module.

[0012]

[0013] In the formula, x Vi u represents the state vector of the vehicle platoon variable parameter control system. Vi w represents the control input vector of the vehicle platoon variable parameter control system. Vi A represents the disturbance input vector of the vehicle platoon variable parameter control system. c B c d c These are the corresponding coefficient matrices, and the scheduling coefficients ρ following the driving environment. e With vehicle state scheduling coefficient ρ v It changes with the changes.

[0014] The braking drive execution module calculates and executes the desired tire force based on the vehicle's desired acceleration and the road surface adhesion coefficient, thereby achieving vehicle queuing control.

[0015] The vehicle status and driving environment perception module includes the following:

[0016] S1.1, The scheduling coefficient calculation unit calculates the driving environment scheduling coefficient ρ. e ,

[0017] The driving environment scheduling coefficient ρ e Depends on road surface adhesion level μv Air visibility level A v Among them, the road surface adhesion level μ v The air visibility level A depends on the real-time road surface adhesion coefficient μ of the current driving surface. v The maximum viewing distance d of the vehicle camera depends on the current weather conditions. c The calculation formula is as follows:

[0018]

[0019]

[0020] In the formula, μ max μ represents the maximum value of the road surface adhesion level. min d represents the minimum level of road surface adhesion. c,max d represents the maximum level of air visibility. c,min The minimum value representing the level of air visibility;

[0021] Calculate the driving environment scheduling coefficient ρ using a fuzzy algorithm e Among them, the input variable is the road surface adhesion level μ. v With air visibility level A v The fuzzy subset representation is {NB, NM, NS, NO, PS, PM, PB}, with meanings of {extremely low, low, relatively low, moderate, relatively high, high, extremely high}, respectively. The input variable is the road surface adhesion level μ. v With air visibility level A v The universe of discourse is [0, 1]. A triangular membership function is chosen, and the output variable, the driving environment scheduling coefficient ρ, is set. e The universe of discourse is [0, 2, 1], and its fuzzy rules are defined as follows:

[0022]

[0023] S1.2, The scheduling coefficient calculation unit calculates the vehicle state scheduling coefficient ρ. v ,

[0024] The vehicle state scheduling coefficient ρ v Depends on vehicle acceleration capability A a Vehicle deceleration capability A d Among them, vehicle acceleration capability A a Depends on the reciprocal T of the current vehicle's 0-100km / h acceleration time a Vehicle deceleration capability A d The distance depends on the reciprocal of the braking distance d at the current vehicle speed of 100 km / h. d The calculation formula is as follows:

[0025]

[0026]

[0027] In the formula, T a,max T represents the maximum value of the reciprocal of the vehicle's acceleration time from 0 to 100 km / h. a,min d represents the minimum value of the reciprocal of the acceleration time from 0 to 100 km / h for a vehicle. d,max d represents the maximum value of the reciprocal of the braking distance of a vehicle from 100 km / h to a stopping point. d,min The minimum value representing the reciprocal of the vehicle's braking distance from 100 km / h;

[0028] Calculate the vehicle state scheduling coefficient ρ using a fuzzy algorithm v Among them, the input variable is the vehicle's acceleration capability A. a With vehicle deceleration capability A d The fuzzy subset representation is {NB, NM, NS, NO, PS, PM, PB}, with meanings of {extremely low, low, relatively low, moderate, relatively high, high, extremely high}, respectively. The input variable is the vehicle acceleration capability A. a With vehicle deceleration capability A d The universe of discourse is [0, 1]. A triangular membership function is chosen, and the output variable is the vehicle state scheduling coefficient ρ. v The universe of discourse is [0, 2, 1], and its fuzzy rules are defined as follows:

[0029]

[0030]

[0031] S1.3 The vehicle queue control status sensing unit numbers the vehicles in the queue. The lead vehicle is numbered V0, and the remaining following vehicles are numbered Vi according to their driving direction, i = 1, 2, 3...N, where N+1 is the number of vehicles in the queue.

[0032] The vehicle decision control module includes the following:

[0033] S2.1 Construct a vehicle platooning variable parameter control system.

[0034] Define the following vehicle Vi and calculate the expected distance value using a constant inter-vehicle distance model.

[0035] d=T ht v Vi +d0,

[0036] In the formula, d represents the desired vehicle spacing value, in meters (m), and T... ht The time distance between the front of the train is expressed in seconds (s). Vid0 represents the speed of the following vehicle Vi in m / s, and d0 represents the offset between vehicles in meters.

[0037] Based on the driving environment scheduling coefficient ρ e Vehicle status scheduling coefficient ρ v For desired acceleration The weighted processing is performed, and its input weight function W u As shown in the following formula:

[0038]

[0039] In the formula, T a Indicates the response delay of the vehicle's drive braking system. For the desired acceleration of vehicle Vi, The actual acceleration of vehicle Vi.

[0040] Define a vehicle platooning variable parameter control system for vehicle Vi following control.

[0041]

[0042] In the formula, x Vi U represents the state vector of the system. Vi w represents the control input vector of the system. Vi Let A represent the interference input vector of the system. c B c d c The corresponding coefficient matrices are shown below, and the correlation matrix is ​​as follows:

[0043] w Vi =[a Vi-1 ],

[0044] B c =[0 0 1 / T a ] T d c =[0 1 0] T ,

[0045] In the formula, d i This represents the error between the actual distance between vehicle Vi and vehicle Vi-1 and the desired vehicle spacing, v i a represents the speed difference between vehicle Vi and vehicle Vi-1. Vi-1 This represents the acceleration of vehicle Vi-1.

[0046] S2.2 Calculate the expected acceleration of vehicle Vi in the queue.

[0047] Define the system auxiliary output vector y Vi ,

[0048] y Vi =Q 1 / 2 C c x Vi +R 1 / 2 D c u Vi +Q 1 / 2 d yc w Vi ,

[0049] In the formula, Q and R are weighting coefficients, and C c D c d yc The corresponding coefficient matrix and correlation matrix are as follows:

[0050] D c =[1], d yc =[0],

[0051] Introducing a state feedback matrix K ensures the robust stability of the vehicle platooning variable parameter control system for vehicle Vi following control.

[0052] u Vi =Kx Vi ,

[0053] The vehicle platooning variable parameter control system for vehicle Vi following control is represented as:

[0054]

[0055] The final closed-loop system for vehicle Vi following control is obtained as follows:

[0056]

[0057] In the formula, A(ρ) e ,ρ v ) = A c +B c K, B x =d c C = Q 1 / 2 C c +R 1 / 2 D c K, B x =Q 1 / 2 d yc ,A(ρ e ,ρ v ) indicates that matrix A contains a variable parameter ρ. e and ρ v ,

[0058] To ensure interference w ViUnder the influence of interference, it is necessary to ensure that the closed-loop system of vehicle Vi following control is protected from interference. Vi To output y Vi transfer function In the formula, γ is an arbitrarily small positive number, and the necessary and sufficient condition is that the following Riccati equation exists:

[0059]

[0060] In the formula, X is a Lyapunov function. The positive definite solution,

[0061] B y =0, therefore the Riccati equation is equivalent to:

[0062]

[0063] According to Schur's complement theorem, the following inequality is obtained:

[0064]

[0065] Based on the driving environment scheduling coefficient ρ e and vehicle state scheduling coefficient ρ v The maximum and minimum values ​​of ρ form the four vertices M, N, O, and P of the polytope, where M = (ρ e,min , ρ v,min ), N=(ρ e,min , ρ v,max ), O=(ρ e,max , ρ v,min ), P=(ρ e,max , ρ v,max ),

[0066] Then the set of matrices It can be represented as:

[0067]

[0068] In the formula,

[0069]

[0070]

[0071]

[0072]

[0073] Then, multiply both sides of the inequality by the matrix (X). -1 ,I,I), get:

[0074]

[0075] In the formula, i,j∈{1,2,3,4}, P=X -1 W j =K j P, solve the above inequality to obtain the solution X of the Lyapunov function and the feedback gain K. j ,

[0076] Desired acceleration of vehicle Vi

[0077] The braking drive execution module calculates the desired tire force, including the following:

[0078] S3.1 Calculate the target tire force.

[0079]

[0080] In the formula, F xfl F xfr F xrl F xrr S3.2 represents the target tire force for the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively. Based on the road surface adhesion coefficient, the desired tire force is obtained after correction.

[0081] The desired tire force can be calculated using the following formula:

[0082]

[0083]

[0084] In the formula, These represent the target tire forces for the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively.

[0085] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0086] 1. This invention constructs a vehicle queuing variable parameter control system based on driving environment scheduling coefficient and vehicle state scheduling coefficient. The designed variable parameter control system considers road surface adhesion level, air visibility level, vehicle acceleration and deceleration capabilities when calculating the expected acceleration of vehicles in the queuing, resulting in a more reasonable expected acceleration and contributing to safe vehicle operation.

[0087] 2. This invention corrects the final calculated expected tire force using the road adhesion coefficient, thereby improving driving safety during vehicle platoon control. Attached Figure Description

[0088] The present invention will be further described below with reference to the accompanying drawings:

[0089] Figure 1This is a system framework diagram of a vehicle queuing variable parameter control system based on driving environment and vehicle status according to the present invention. Detailed Implementation

[0090] The present invention will be further described in detail below with reference to the accompanying drawings. The following embodiments are only used to illustrate the technical solution of the present invention more clearly, and are therefore only examples and should not be used to limit the scope of protection of the present invention.

[0091] It should be noted that, unless otherwise stated, the technical or scientific terms used in this application should have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0092] like Figure 1 As shown, this embodiment of the invention provides a vehicle platooning variable parameter control system based on driving environment and vehicle status, including the following modules:

[0093] Vehicle status and driving environment perception module, vehicle decision control module, and braking drive execution module.

[0094] The vehicle status and driving environment perception module includes a scheduling coefficient calculation unit and a vehicle queue control status perception unit, wherein the scheduling coefficient calculation unit is used to calculate the driving environment scheduling coefficient ρ. e Vehicle status scheduling coefficient ρ v The driving environment scheduling coefficient ρ e Depends on road surface adhesion level μ v Air visibility level A v The vehicle state scheduling coefficient ρ v Depends on vehicle acceleration capability A a Vehicle deceleration capability A d The vehicle queue control status sensing unit is used to number the vehicles in the queue and collect the relative distance d between the vehicles in the queue in real time. i The relative speed v of the vehicles in the queue i And the acceleration a of the vehicles in the queue Vi Information such as...

[0095] ρ e =ρ e (μ v A v ),

[0096] ρ v =ρ v (A a A d )

[0097] In the formula, μ v Indicates the road surface adhesion level, A v A represents the level of air visibility.a Indicates the vehicle's acceleration capability, A d This indicates the vehicle's deceleration capability.

[0098] The vehicle decision control module is based on the driving environment scheduling coefficient ρ. e With vehicle state scheduling coefficient ρ v A vehicle platoon variable parameter control system is constructed to calculate the desired acceleration of vehicles in the platoon and send it to the braking drive execution module.

[0099]

[0100] In the formula, x Vi u represents the state vector of the vehicle platoon variable parameter control system. Vi w represents the control input vector of the vehicle platoon variable parameter control system. Vi A represents the disturbance input vector of the vehicle platoon variable parameter control system. c B c d c These are the corresponding coefficient matrices, and the scheduling coefficients ρ following the driving environment. e With vehicle state scheduling coefficient ρ v It changes with the changes.

[0101] The braking drive execution module calculates and executes the desired tire force based on the vehicle's desired acceleration and the road surface adhesion coefficient, thereby achieving vehicle queuing control.

[0102] The vehicle status and driving environment perception module includes the following:

[0103] S1.1, The scheduling coefficient calculation unit calculates the driving environment scheduling coefficient ρ. e ,

[0104] The driving environment scheduling coefficient ρ e Depends on road surface adhesion level μ v Air visibility level A v Among them, the road surface adhesion level μ v The air visibility level A depends on the real-time road surface adhesion coefficient μ of the current driving surface. v The maximum viewing distance d of the vehicle camera depends on the current weather conditions. c The calculation formula is as follows:

[0105]

[0106]

[0107] In the formula, μ max μ represents the maximum value of the road surface adhesion level. min d represents the minimum level of road surface adhesion.c,max d represents the maximum level of air visibility. c,min The minimum value representing the level of air visibility;

[0108] Calculate the driving environment scheduling coefficient ρ using a fuzzy algorithm e Among them, the input variable is the road surface adhesion level μ. v With air visibility level A v The fuzzy subset representation is {NB, NM, NS, NO, PS, PM, PB}, with meanings of {extremely low, low, relatively low, moderate, relatively high, high, extremely high}, respectively. The input variable is the road surface adhesion level μ. v With air visibility level A v The universe of discourse is [0, 1]. A triangular membership function is chosen, and the output variable, the driving environment scheduling coefficient ρ, is set. e The universe of discourse is [0, 2, 1], and its fuzzy rules are defined as follows:

[0109]

[0110] S1.2, The scheduling coefficient calculation unit calculates the vehicle state scheduling coefficient ρ. v ,

[0111] The vehicle state scheduling coefficient ρ v Depends on vehicle acceleration capability A a Vehicle deceleration capability A d Among them, vehicle acceleration capability A a Depends on the reciprocal T of the current vehicle's 0-100km / h acceleration time a Vehicle deceleration capability A d The distance depends on the reciprocal of the braking distance d at the current vehicle speed of 100 km / h. d The calculation formula is as follows:

[0112]

[0113]

[0114] In the formula, T a,max T represents the maximum value of the reciprocal of the vehicle's acceleration time from 0 to 100 km / h. a,min d represents the minimum value of the reciprocal of the acceleration time from 0 to 100 km / h for a vehicle. d,max d represents the maximum value of the reciprocal of the braking distance of a vehicle from 100 km / h to a stopping point. d,min The minimum value representing the reciprocal of the vehicle's braking distance from 100 km / h;

[0115] Calculate the vehicle state scheduling coefficient ρ using a fuzzy algorithm v Among them, the input variable is the vehicle's acceleration capability A.a With vehicle deceleration capability A d The fuzzy subset representation is {NB, NM, NS, NO, PS, PM, PB}, with meanings of {extremely low, low, relatively low, moderate, relatively high, high, extremely high}, respectively. The input variable is the vehicle acceleration capability A. a With vehicle deceleration capability A d The universe of discourse is [0, 1]. A triangular membership function is chosen, and the output variable is the vehicle state scheduling coefficient ρ. v The universe of discourse is [0, 2, 1], and its fuzzy rules are defined as follows:

[0116]

[0117]

[0118] S1.3 The vehicle queue control status sensing unit numbers the vehicles in the queue. The lead vehicle is numbered V0, and the remaining following vehicles are numbered Vi according to their driving direction, i = 1, 2, 3...N, where N+1 is the number of vehicles in the queue.

[0119] The vehicle decision control module includes the following:

[0120] S2.1 Construct a vehicle platooning variable parameter control system.

[0121] Define the following vehicle Vi and calculate the expected distance value using a constant inter-vehicle distance model.

[0122] d = T ht v Vi +d0,

[0123] In the formula, d represents the desired vehicle spacing value, in meters (m), and T... ht The time distance between the front of the train is expressed in seconds (s). Vi d0 represents the speed of the following vehicle Vi in m / s, and d0 represents the offset between vehicles in meters.

[0124] Based on the driving environment scheduling coefficient ρ e Vehicle status scheduling coefficient ρ v For desired acceleration The weighted processing is performed, and its input weight function W u As shown in the following formula:

[0125]

[0126] In the formula, T a Indicates the response delay of the vehicle's drive braking system. For the desired acceleration of vehicle Vi, The actual acceleration of vehicle Vi.

[0127] Define a vehicle platooning variable parameter control system for vehicle Vi following control.

[0128]

[0129] In the formula, x Vi U represents the state vector of the system. Vi w represents the control input vector of the system. Vi Let A represent the interference input vector of the system. c B c d c The corresponding coefficient matrices are shown below, and the correlation matrix is ​​as follows:

[0130] w Vi =[a Vi-1 ],

[0131] B c =[0 0 1 / T a ] T d c =[0 1 0] T ,

[0132] In the formula, d i This represents the error between the actual distance between vehicle Vi and vehicle Vi-1 and the desired vehicle spacing, v i a represents the speed difference between vehicle Vi and vehicle Vi-1. Vi-1 This represents the acceleration of vehicle Vi-1.

[0133] S2.2 Calculate the expected acceleration of vehicle Vi in the queue.

[0134] Define the system auxiliary output vector y Vi ,

[0135] y Vi =Q 1 / 2 C c x Vi +R 1 / 2 D c u Vi +Q 1 / 2 d yc w Vi ,

[0136] In the formula, Q and R are weighting coefficients, and C c D c d yc The corresponding coefficient matrix and correlation matrix are as follows:

[0137] D c =[1], dyc =[0],

[0138] Introducing a state feedback matrix K ensures the robust stability of the vehicle platooning variable parameter control system for vehicle Vi following control.

[0139] u Vi =Kx Vi ,

[0140] The vehicle platooning variable parameter control system for vehicle Vi following control is represented as:

[0141]

[0142] The final closed-loop system for vehicle Vi following control is obtained as follows:

[0143]

[0144] In the formula, A(ρ) e ,ρ v ) = A c +B c K, B x =d c C = Q 1 / 2 C c +R 1 / 2 D c K, B x =Q 1 / 2 d yc ,A(ρ e ,ρ v ) indicates that matrix A contains a variable parameter ρ. e and ρ v ,

[0145] To ensure interference w Vi Under the influence of interference, it is necessary to ensure that the closed-loop system of vehicle Vi following control is protected from interference. Vi To output y Vi transfer function In the formula, γ is an arbitrarily small positive number, and the necessary and sufficient condition is that the following Riccati equation exists:

[0146]

[0147] In the formula, X is a Lyapunov function. The positive definite solution,

[0148] B y =0, therefore the Riccati equation is equivalent to:

[0149]

[0150] According to Schur's complement theorem, the following inequality is obtained:

[0151]

[0152] Based on the driving environment scheduling coefficient ρ e and vehicle state scheduling coefficient ρ v The maximum and minimum values ​​of ρ form the four vertices M, N, O, and P of the polytope, where M = (ρ e,min , ρ v,min ), N=(ρ e,min , ρ v,max ), O=(ρ e,max , ρ v,min ), P=(ρ e,max , ρ v,max ),

[0153] Then the set of matrices It can be represented as:

[0154]

[0155] In the formula,

[0156]

[0157]

[0158]

[0159]

[0160] Then, multiply both sides of the inequality by the matrix (X). -1 ,I,I), get:

[0161]

[0162] In the formula, i,j∈{1,2,3,4}, P=X -1 W j =K j P, solve the above inequality to obtain the solution X of the Lyapunov function and the feedback gain K. j ,

[0163] Desired acceleration of vehicle Vi

[0164] The braking drive execution module calculates the desired tire force, including the following:

[0165] S3.1 Calculate the target tire force.

[0166]

[0167] In the formula, F xfl F xfr F xrl F xrr S3.2 represents the target tire force for the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively. Based on the road surface adhesion coefficient, the desired tire force is obtained after correction.

[0168] The desired tire force can be calculated using the following formula:

[0169]

[0170]

[0171] In the formula, These represent the target tire forces for the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively.

Claims

1. A vehicle platooning variable parameter control system based on driving environment and vehicle status, characterized in that, Includes the following: The vehicle platooning variable parameter control system based on driving environment and vehicle status includes a vehicle status and driving environment perception module, a vehicle decision control module, and a braking drive execution module. The vehicle status and driving environment perception module includes a scheduling coefficient calculation unit and a vehicle queue control status perception unit, wherein the scheduling coefficient calculation unit is used to calculate the driving environment scheduling coefficient. Vehicle status scheduling coefficient The driving environment scheduling coefficient Depends on road surface adhesion level Air visibility level The calculation formula is as follows: ， ， In the formula, This indicates the maximum value of the road surface adhesion level. This represents the minimum value of the road surface adhesion level. This represents the maximum value of the air visibility level. The minimum value representing the level of air visibility; The vehicle status scheduling coefficient Depends on vehicle acceleration Vehicle deceleration capability The calculation formula is as follows: ， ， In the formula, This represents the maximum value of the reciprocal of the vehicle's acceleration time from 0 to 100 km / h. This represents the minimum value of the reciprocal of the vehicle's acceleration time from 0 to 100 km / h. This represents the maximum value of the reciprocal of the vehicle's braking distance from 100 km / h. The minimum value representing the reciprocal of the vehicle's braking distance from 100 km / h; The vehicle queue control status sensing unit is used to number the vehicles in the queue and collect the relative distances between vehicles in the queue in real time. The relative speed of vehicles in the queue and the acceleration of vehicles in the queue Information such as; ， In the formula, Indicates the road surface adhesion level. Indicates the level of air visibility. Indicates the vehicle's acceleration capability. Indicates the vehicle's deceleration capability; The vehicle decision control module is based on the designed driving environment scheduling coefficient. With vehicle state scheduling coefficient A vehicle platoon variable parameter control system is constructed to calculate the desired acceleration of vehicles in the platoon and send it to the braking drive execution module; In the formula, This represents the state vector of the vehicle platoon variable parameter control system. This represents the control input vector of the vehicle platoon variable parameter control system. This represents the disturbance input vector of the vehicle platoon variable parameter control system. , , These are the corresponding coefficient matrices, and the scheduling coefficients follow the driving environment. With vehicle state scheduling coefficient It changes with the changes; The braking drive execution module calculates and executes the desired tire force based on the vehicle's desired acceleration and the road surface adhesion coefficient, thereby achieving vehicle queuing control.

2. The vehicle platooning variable parameter control system based on driving environment and vehicle status according to claim 1, characterized in that: The vehicle status and driving environment perception module includes the following: S2.1 The scheduling coefficient calculation unit calculates the driving environment scheduling coefficient. , The driving environment scheduling coefficient Depends on road surface adhesion level Air visibility level Among them, road surface adhesion level Depends on the real-time road surface adhesion coefficient of the current driving surface Air visibility level The maximum field of view of the vehicle camera depends on the current weather conditions. ; Using fuzzy algorithms to calculate driving environment scheduling coefficients Among them, the input variable is the road surface adhesion level. With air visibility level The fuzzy subset representation is {NB, NM, NS, NO, PS, PM, PB}, with meanings of {extremely low, low, relatively low, moderate, relatively high, high, extremely high}, respectively. The input variable is the road surface adhesion level. With air visibility level The universe of discourse is [0, 1]. A triangular membership function is selected, and the output variable, the driving environment scheduling coefficient, is set. The domain of discourse is [0, 2, 1]. The fuzzy rules are described using the If…and…then statement, totaling 49 rules, defined as follows: (1) If ( is NB) and ( is NB) then ( is NB), (2) If ( is NB) and ( is NM) then ( is NB), (3) If ( is NB) and ( is NS) then ( is NB), (4) If ( is NB) and ( is NO) then ( is NB), (5) If ( is NB) and ( is PS) then ( is NB), (6) If ( is NB) and ( is PM) then ( is NB), (7) If ( is NB) and ( is PB) then ( is NB), (8) If ( is NM) and ( is NB) then ( is NB), (9) If ( is NM) and ( is NM) then ( is NM), (10) If ( is NM) and ( is NS) then ( is NM), (11) If ( is NM) and ( is NO) then ( is NM), (12) If ( is NM) and ( is PS) then ( is NM), (13) If ( is NM) and ( is PM) then ( is NM), (14) If ( is NM) and ( is PB) then ( is NM), (15) If ( is NS) and ( is NB) then ( is NB), (16) If ( is NS) and ( is NM) then ( is NM), (17) If ( is NS) and ( is NS) then ( is NS), (18) If ( is NS) and ( is NO) then ( is NS), (19) If ( is NS) and ( is PS) then ( is NS), (20) If ( is NS) and ( is PM) then ( is NS), (21) If ( is NS) and ( is PB) then ( is NS), (22) If ( is NO) and ( is NB) then ( is NB), (23) If ( is NO) and ( is NM) then ( is NM), (24) If ( is NO) and ( is NS) then ( is NS), (25) If ( is NO) and ( is NO) then ( is NO), (26) If ( is NO) and ( is PS) then ( is NO), (27) If ( is NO) and ( is PM) then ( is PS), (28) If ( is NO) and ( is PB) then ( is PS), (29) If ( is PS) and ( is NB) then ( is NB), (30) If ( is PS) and ( is NM) then ( is NM), (31) If ( is PS) and ( is NS) then ( is NS), (32) If ( is PS) and ( is NO) then ( is PS), (33) If ( is PS) and ( is PS) then ( is PS), (34) If ( is PS) and ( is PM) then ( is PM), (35) If ( is PS) and ( is PB) then ( is PM), (36) If ( is PM) and ( is NB) then ( is NB), (37) If ( is PM) and ( is NM) then ( is NM), (38) If ( is PM) and ( is NS) then ( is NS), (39) If ( is PM) and ( is NO) then ( is PS), (40) If ( is PM) and ( is PS) then ( is PM), (41) If ( is PM) and ( is PM) then ( is PM), (42) If ( is PM) and ( is PB) then ( is PB), (43) If ( is PB) and ( is NB) then ( is NB), (44) If ( is PB) and ( is NM) then ( is NM), (45) If ( is PB) and ( is NS) then ( is NS), (46) If ( is PB) and ( is NO) then ( is PM), (47) If ( is PB) and ( is PS) then ( is PM), (48) If ( is PB) and ( is PM) then ( is PB), (49) If ( is PB) and ( is PB) then ( is PB), S2.2 The scheduling coefficient calculation unit calculates the vehicle state scheduling coefficient. , The vehicle status scheduling coefficient Depends on vehicle acceleration Vehicle deceleration capability Among them, vehicle acceleration capability Depends on the reciprocal of the current vehicle's 0-100km / h acceleration time Vehicle deceleration capability Depends on the reciprocal of the braking distance at the current vehicle speed of 100km / h ; Calculating vehicle state scheduling coefficients using fuzzy algorithms Among them, the input variable is the vehicle's acceleration capability. With vehicle deceleration ability The fuzzy subset representation is {NB, NM, NS, NO, PS, PM, PB}, with meanings of {extremely low, low, relatively low, moderate, relatively high, high, extremely high}, respectively. The input variable is vehicle acceleration capability. With vehicle deceleration ability The universe of discourse is [0, 1]. A triangular membership function is selected, and the output variable is the vehicle state scheduling coefficient. The domain of discourse is [0, 2, 1]. The fuzzy rules are described using the If…and…then statement, totaling 49 rules, defined as follows: (1) If ( is NB) and ( is NB) then ( is NB), (2) If ( is NB) and ( is NM) then ( is NB), (3) If ( is NB) and ( is NS) then ( is NB), (4) If ( is NB) and ( is NO) then ( is NB), (5) If ( is NB) and ( is PS) then ( is NB), (6) If ( is NB) and ( is PM) then ( is NB), (7) If ( is NB) and ( is PB) then ( is NB), (8) If ( is NM) and ( is NB) then ( is NB), (9) If ( is NM) and ( is NM) then ( is NM), (10) If ( is NM) and ( is NS) then ( is NM), (11) If ( is NM) and ( is NO) then ( is NM), (12) If ( is NM) and ( is PS) then ( is NM), (13) If ( is NM) and ( is PM) then ( is NM), (14) If ( is NM) and ( is PB) then ( is NM), (15) If ( is NS) and ( is NB) then ( is NB), (16) If ( is NS) and ( is NM) then ( is NM), (17) If ( is NS) and ( is NS) then ( is NS), (18) If ( is NS) and ( is NO) then ( is NS), (19) If ( is NS) and ( is PS) then ( is NS), (20) If ( is NS) and ( is PM) then ( is NS), (21) If ( is NS) and ( is PB) then ( is NS), (22) If ( is NO) and ( is NB) then ( is NB), (23) If ( is NO) and ( is NM) then ( is NM), (24) If ( is NO) and ( is NS) then ( is NS), (25) If ( is NO) and ( is NO) then ( is NO), (26) If ( is NO) and ( is PS) then ( is PS), (27) If ( is NO) and ( is PM) then ( is PS), (28) If ( is NO) and ( is PB) then ( is PM), (29) If ( is PS) and ( is NB) then ( is NB), (30) If ( is PS) and ( is NM) then ( is NM), (31) If ( is PS) and ( is NS) then ( is NS), (32) If ( is PS) and ( is NO) then ( is NO), (33) If ( is PS) and ( is PS) then ( is PS), (34) If ( is PS) and ( is PM) then ( is PM), (35) If ( is PS) and ( is PB) then ( is PM), (36) If ( is PM) and ( is NB) then ( is NB), (37) If ( is PM) and ( is NM) then ( is NM), (38) If ( is PM) and ( is NS) then ( is NS), (39) If ( is PM) and ( is NO) then ( is PS), (40) If ( is PM) and ( is PS) then ( is PM), (41) If ( is PM) and ( is PM) then ( is PM), (42) If ( is PM) and ( is PB) then ( is PB), (43) If ( is PB) and ( is NB) then ( is NB), (44) If ( is PB) and ( is NM) then ( is NM), (45) If ( is PB) and ( is NS) then ( is NS), (46) If ( is PB) and ( is NO) then ( is PS), (47) If ( is PB) and ( is PS) then ( is PM), (48) If ( is PB) and ( is PM) then ( is PB), (49) If ( is PB) and ( is PB) then ( is PB), S2.3 The vehicle queue control status sensing unit numbers the vehicles in the queue. The lead vehicle is numbered V0, and the remaining following vehicles are numbered Vi according to their driving direction, i=1, 2, 3...N, where N+1 is the number of vehicles in the queue.

3. The vehicle platooning variable parameter control system based on driving environment and vehicle status according to claim 1, characterized in that: The vehicle decision control module includes the following: S3.1 Construct a vehicle platooning variable parameter control system. Define the following vehicle Vi and calculate the expected distance value using a constant inter-vehicle distance model. ， In the formula, This represents the desired vehicle spacing value, in meters (m). The time distance between the front and rear of the train is expressed in seconds (s). This indicates the speed of the following vehicle Vi, in m / s. This indicates the offset between vehicles, in meters (m). Based on driving environment scheduling coefficient Vehicle status scheduling coefficient For desired acceleration The weighted processing is performed, and its input weight function is... As shown in the following formula: ， In the formula, Indicates the response delay of the vehicle's drive braking system. For the desired acceleration of vehicle Vi, The actual acceleration of vehicle Vi. Define a vehicle platooning variable parameter control system for vehicle Vi following control. ， In the formula, This represents the state vector of the system. This represents the control input vector of the system. This represents the interference input vector of the system. , , The corresponding coefficient matrices are shown below, and the correlation matrix is ​​as follows: ， ， ， ， ， ， In the formula, This represents the error between the actual distance between vehicle Vi and vehicle Vi-1 and the desired vehicle spacing. This represents the speed difference between vehicle Vi and vehicle Vi-1. This represents the acceleration of vehicle Vi-1. S3.2 Calculate the expected acceleration of vehicle Vi in the queue. Define the system auxiliary output vector , ， In the formula, , These are the weighting coefficients. , , The corresponding coefficient matrix and correlation matrix are as follows: ， ， ， Introducing a state feedback matrix To ensure the robust stability of the vehicle platooning variable parameter control system for vehicle Vi following control, ， The vehicle platooning variable parameter control system for vehicle Vi following control is represented as: ， The final closed-loop system for vehicle Vi following control is obtained as follows: ， In the formula, , , , , Representation matrix There are variable parameters and , To ensure interference Under the influence of interference, it is necessary to ensure that the closed-loop system of vehicle Vi following control is protected from disturbances. To output transfer function In the formula For any arbitrarily small positive number, the necessary and sufficient condition is that the following Riccati equation exists: ， In the formula, Lyapunov function The positive definite solution, , Therefore, the Riccati equation is equivalent to: ， According to Schur's complement theorem, the following inequality is obtained: ， Based on driving environment scheduling coefficient and vehicle state scheduling coefficient The maximum and minimum values ​​of form the four vertices M, N, O, and P of the polytope, where M = ( , ), N=( , ), O=( , ), P=( , ), Then the set of matrices It can be represented as: ， In the formula, ， ， ， ， ， ， ， ， ， ， ， ， Then, multiply both sides of the inequality by a matrix. ,get: ， In the formula, , , Solving the above inequality yields the solution for the Lyapunov function. and feedback gain , Desired acceleration of vehicle Vi .

4. The vehicle platooning variable parameter control system based on driving environment and vehicle status according to claim 1, characterized in that: The braking drive execution module calculates the desired tire force, including the following: S4.1 Calculate the target tire force. ， In the formula, , , , These represent the target tire forces for the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively. S4.

2. Based on the road surface adhesion coefficient, the desired tire force is obtained through correction. The desired tire force can be calculated using the following formula: ， ， ， ， In the formula, , , , These represent the target tire forces for the left front wheel, right front wheel, left rear wheel, and right rear wheel, respectively.

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