Control method and control system for collision avoidance of intelligent network connection vehicle formation

Abstract

The invention belongs to the technical field of vehicle automatic driving, and particularly relates to an intelligent network connection vehicle formation collision avoidance control method and system, and the method comprises the steps: carrying out the risk perception and processing in a vehicle driving process, and carrying out the path planning and tracking after the risk perception and processing; performing unified coordination according to path planning and tracking; collision avoidance in a formation driving process is realized, and two sets of optimization strategies based on model prediction control are implemented by performing two operations of merging and splitting on the formation to be respectively used for collision avoidance path planning and trajectory tracking in two modes of single vehicle cruising and multi-vehicle formation.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicle automatic driving, and in particular relates to a control method and a control system for collision avoidance of an intelligent networked vehicle formation. Background Art

[0002] Collaborative vehicle platooning, achieved through Internet of Vehicles and autonomous driving technologies, refers to vehicles traveling in series at short intervals to achieve virtual connection. Intelligent connected vehicle (CAV) platooning has significant energy-saving and emission-reduction potential. It greatly reduces the complexity of vehicle operation, thereby reducing the workload of drivers. Maintaining a stable vehicle platoon in actual traffic is challenging, especially when encountering obstacles in unstable traffic. In order to avoid obstacles that interfere with their intended path or motion state, vehicle platoons often need to perform split and merge operations.

[0003] In the related art, in order to solve the problem of how CAV formations can perform local path planning to achieve collision avoidance, some algorithms based on model predictive control (MPC) are used to incorporate safety constraints into a multi-objective optimization framework, which can effectively guide CAV formations to avoid obstacles while making minimal adjustments to their desired motion states. However, these algorithms have the problem of low algorithm solution efficiency. There are also studies that use controllers based on artificial potential fields (APFs) to describe the distribution of collision risks and guide CAV formations along the path with the lowest collision risk. However, existing APF-based controllers cannot accurately characterize the collision risk of moving obstacles, and they ignore the differences in risks in different directions. In addition, when applied to a large range, such controllers are prone to false alarms because they do not consider the expected trajectory of obstacles and the maneuverability of vehicles. At the same time, for the problem of collaborative collision avoidance of CAV formations, control strategies are usually regarded as relatively independent operations, including formation maintenance, obstacle avoidance, and formation reorganization. These controllers cannot fully realize the collaborative operation of CAV formations in collision avoidance. For example, when the formation needs to split to avoid obstacles, these controllers cannot simultaneously optimize single-vehicle cruising and multi-vehicle formations. These independent operations lack coordination mechanisms to ensure the safety and flexibility of CAV formations.

[0004] Therefore, based on the above technical problems, it is necessary to design a new control method and control system for intelligent connected vehicle platoon collision avoidance. Summary of the invention

[0005] The embodiments of the present disclosure at least provide a control method and control system for collision avoidance of an intelligent connected vehicle platoon.

[0006] In a first aspect, an embodiment of the present disclosure provides a method for controlling collision avoidance of an intelligent connected vehicle platoon, including:

[0007] Sense and handle risks while the vehicle is in motion;

[0008] Path planning and tracking after risk perception and processing;

[0009] Unified coordination based on path planning and tracking.

[0010] Furthermore, the risk perception and processing during vehicle driving includes:

[0011] Based on the dynamic window method, the problem of searching for the optimal path in geometric space is transformed into the problem of searching for the optimal speed set in speed space, and the feasible speed set of vehicles is obtained, that is,

[0012] Determine the set of achievable velocities:

[0013] If the current speed is v(t) = (v(t), θ(t)), in a limited time period Δt, the speed range that the main vehicle can achieve should be:

[0014]

[0015] Among them, RV represents the achievable speed set; and Respectively represent the maximum / minimum acceleration of linear velocity, and Respectively represent the maximum / minimum acceleration of angular velocity;

[0016] The speed that causes the main vehicle to leave the drivable area is:

[0017]

[0018] Among them, ROV represents the speed set that causes the main vehicle to leave the drivable area; a and b are the boundaries of the drivable area.

[0019] Furthermore, the main vehicle needs to leave the speed range where there is a risk of collision, and the driving risk needs to be quantified:

[0020] Vehicle virtual mass: M = m (1.566 × 10 -14 ·v 6.687 +0.3345);

[0021] Among them, m and v are the actual mass and speed of the obstacle vehicle respectively;

[0022] Vehicle Type:

[0023] Where s and k represent the size and type of the vehicle, respectively. * and k * are standard values, ω1 and ω2 are undetermined constants;

[0024]

[0025] Among them, (x s ,y s ) and (x o ,y o ) are the positions of the main vehicle and the obstacle vehicle, ε and ρ are undetermined coefficients;

[0026] Acceleration:

[0027] Among them, a is the acceleration vector of the obstacle vehicle, Represents the direction of a and the vector r from the main vehicle to the obstacle vehicle d The clockwise angle between them, K is an undetermined coefficient;

[0028] Road conditions:

[0029] Among them, ∈ is road visibility; μ is road adhesion coefficient; ρ is road curvature; τ is road slope; γ1, γ2, γ3, γ4 are all unknown constants, ∈ * is the standard road visibility, μ * is the standard road adhesion coefficient, ρ * is the standard road curvature, τ * is the standard road slope;

[0030] The driving risks caused by obstacles on the road are:

[0031]

[0032] Among them, G and ξ are undetermined coefficients;

[0033] The collision velocity set is determined by:

[0034] At some point in the future f , when the main vehicle and the obstacle vehicle appear at the same position at the same time, it is considered that a collision occurs, that is:

[0035]

[0036] Among them, P s (t f ) and P0(t f ) respectively represent the f The positions of the main vehicle and the obstacle vehicle at the moment; P s (t0) is the position of the main vehicle at time t0; v s is the speed of the host vehicle;

[0037] The location at the time of the collision is an area covered by the vehicle geometry, which contains all the points of the cumulative shape of the host vehicle and the obstacle vehicle, namely:

[0038]

[0039] in, represents Minkowski addition; ConfP(S, O) represents the position covered by the vehicle;

[0040] The main vehicle and the obstacle vehicle are regarded as two vehicles with radii of r s and r O ConfP(S, O) is a circle with a radius of r. s +r O The set of points covered by the extended circle of , from which we can get:

[0041] ConfP(S,O)={P|||PO||≤r s +r o};

[0042] Among them, P represents the possible collision point, and O is the center of mass of the obstacle vehicle;

[0043] In t f The speed set of the main vehicle that caused the collision at the moment:

[0044]

[0045] The set of collision velocities at any time t is the union of SVO, where t belongs to the interval (t0, ∞);

[0046]

[0047] The speeds in the set GVO are the set of speeds at which the main vehicle collides with the obstacle vehicle at a certain time in the future (t0,∞);

[0048] By adjusting the speed of the main vehicle, it will no longer be in the collision speed set after the driving risk reaches the collision warning threshold at a certain moment in the future. To avoid collision, the main vehicle cannot travel within the following speed range:

[0049]

[0050] in, Indicates the driving risk when the collision warning threshold is reached; Indicates a risk of collision The GVO area expands as the collision warning threshold increases, thereby obtaining a feasible speed set with a lower level of collision risk, and shrinks as the collision warning threshold decreases, thereby obtaining a feasible speed set with a higher level of collision risk.

[0051] The feasible speed set of the main vehicle considering vehicle dynamics constraints, driving rules requirements and collision avoidance requirements is obtained, which is expressed as:

[0052]

[0053] Further, the path planning and tracking after risk perception and processing includes:

[0054] The vehicle motion state is modeled using the dynamic equation. The vehicle motion model:

[0055]

[0056] The motion of the vehicle relative to the global coordinate system is:

[0057]

[0058] Where X and Y represent the lateral and longitudinal positions of the vehicle, respectively; θ represents the heading angle of the vehicle; v x and v y Respectively represent the lateral and longitudinal speeds of the vehicle; r represents the yaw angular velocity; I Z represents the moment of inertia; l f and l r Respectively represent the distance from the vehicle's center of gravity to the rear axle and the front axle; F yT Indicates the longitudinal force of the tire; F xf and F xr Represents the lateral forces on the front and rear wheels.

[0059] Furthermore, if the vehicle has front-wheel steering, using the linear tire model, the lateral force is expressed as:

[0060]

[0061] Among them, α f and α r Represent the side slip angles of the front and rear wheels respectively; δ is the front wheel turning angle; C f and C f denote the cornering stiffness of the front and rear wheels respectively;

[0062] The system state vector is: x = [Y v y X v x θ r] T ;

[0063] The control input is: z = [F yT δ] T , the control output is: y = [X v y θ] T ;

[0064]

[0065] in:

[0066]

[0067]

[0068] In order to p Internal prediction control output, discretize the above model into an incremental prediction model:

[0069]

[0070] Among them, Δx(k)=x(k)-x(k-1), Δz(k)=z(k)-z(k-1);

[0071] Prediction time domain N p The intra-prediction output vector and the control time domain N c (N p >N c ) is the control input increment vector:

[0072]

[0073] Furthermore, for the free cruise mode, the motion planning based on model predictive control only seeks the path with the lowest risk to maintain the desired motion state. This mode corresponds to the cost function J defined as crus :

[0074] For the multi-vehicle platoon mode, the vehicle also needs to maintain unified motion with the vehicle in front of it. The cost function J corresponding to this mode is pltn :

[0075] Motion planning tasks of different modes can be uniformly described as a corresponding multi-constraint optimization problem:

[0076]

[0077]

[0078] st(k=1,…,N p )

[0079]

[0080] y s (k)≤y s_max +ε k

[0081] ε k ≥0

[0082]

[0083] a≤x(k)≤b

[0084] v min (k)≤v(k)≤vmax (k)

[0085] θ min (k)≤θ(k)≤θ max (k)

[0086]

[0087] F xT ≤T max / R eff

[0088] The multi-objective function includes collision risk, control output, control input, slack variables, and the longitudinal and lateral errors of the predicted trajectory between the host vehicle and its preceding vehicle, Q, R, P, W X and W Y is the weight factor of the above sub-goal; X pre and Y pre is the lateral and longitudinal position of the leading vehicle; d is the distance between two consecutive vehicles in the formation;

[0089] At each time step, no matter what mode the main vehicle is in, the MPC-based motion planning controller will continuously observe the real-time driving environment information of the main vehicle, predict the trajectory of the obstacle vehicle, and calculate the size and distribution of driving risks around the main vehicle within the prediction time range. Once the driving risk of the main vehicle at any time step exceeds the specified threshold, the collision avoidance path planning algorithm will be triggered, that is, the collision speed set GVO is calculated using the speed barrier method, the feasible speed set FV is determined using the dynamic window method, and then the cost function is solved to generate the optimal collision avoidance path and speed. Finally, the generated control scheme is output to the main vehicle for execution;

[0090] In a dynamic environment, if the driving environment around the main vehicle remains unchanged, or the driving risk intensity that changes in the future time step does not exceed the threshold, the main vehicle will continue to execute the current control plan; otherwise, the controller will update the driving risk intensity, GVO, FV and control plan according to environmental changes and trends.

[0091] Further, the unified coordination based on path planning and tracking includes:

[0092] An automaton architecture is used to realize the collaboration and operation of formation merging and splitting. The automaton architecture is defined as a tuple Q:

[0093] Q=<Manv,VehSta,ConInt,ConOut,Event,TranProc,SubSyst,SybCons,SubInit>

[0094] Among them, Manv = {Cruising; Platooning} is a finite set of operations, representing the control mode of the system;

[0095] VehSta=[Y,v y , Xv x , θ, r] is the state vector of the system related to the host vehicle and its neighboring vehicles;

[0096] ConInt=[F yT , δ T ] is the control input vector of the host vehicle, which is composed of the tire longitudinal force and the front wheel steering angle;

[0097] ConOut = [X, v y , θ] is the control output vector of the main vehicle, which consists of the lateral position, longitudinal velocity and heading angle;

[0098] Event = {Merge, Split} is a set of trigger events for system mode switching, including fleet merging and splitting;

[0099] SubSyst, SybCons, SubInit, and TranProc represent the continuously controlled vehicle subsystem, modal transition operation constraints, initial conditions, and transition process, respectively.

[0100] Furthermore, in order to accurately track the conversion operation of the system state, the motion states of the main vehicle and the obstacle vehicle are:

[0101] The motion status of the main vehicle:

[0102]

[0103] Among them, VehID is the main vehicle identifier; VehLop, VehLap, VehSpd, VehAng, VehAcc, VehAngAcc represent the longitudinal position, lateral position, speed, heading angle, acceleration and angular acceleration of the main vehicle respectively; PltnDunmy is the formation activation flag, which is 1 when the main vehicle is allowed to join the formation and 0 when the main vehicle is cruising alone; PltnID is the fleet identifier;

[0104] PltnNum, PltnHdwy, PltnSpd, and PrecedID represent the number of vehicles in the formation, the preset vehicle spacing, the preset speed, and the vehicle identifier in front of the main vehicle, respectively;

[0105] The motion state of the obstacle vehicle:

[0106]

[0107] Among them, ObsID is the obstacle vehicle identifier; ObsLop, ObsLap, ObsSpd, ObsAng, ObsAcc, ObsAngAcc represent the obstacle vehicle's longitudinal position, lateral position, speed, heading angle, acceleration, and angular acceleration respectively;

[0108] ReltSpd, ReltAng, ReltDis, ObsCRPF, and ObsPrty represent the relative speed, relative heading angle, relative distance, collision risk intensity, and the priority of the main vehicle in dealing with the obstacle vehicle, respectively;

[0109] When the operation transition signal is activated, the control constraints and targets of the controlled master vehicle are adjusted through formation merging and splitting, and the master vehicle switches its operation mode from the current state to another operation mode according to the updated control instructions.

[0110] Furthermore, a single cruising vehicle or a platoon vehicle following behind a platoon can be regarded as a candidate vehicle for forming a new platoon. The main vehicle will continuously monitor the motion state of the candidate vehicle. If the following speed and vehicle spacing requirements are met, the merging signal will be activated to form a new platoon with the candidate vehicle. The main vehicle will modify its motion state to follow the candidate vehicle at an appropriate speed and vehicle spacing.

[0111] In a formation split event, the lead vehicle in a multi-vehicle formation will continuously monitor changes in the vehicle in front of it and the surrounding driving environment. If the driving conditions no longer meet the requirements of formation following driving, a split signal will be triggered to separate the lead vehicle from the current formation. The lead vehicle will modify its motion state to separate from the current formation.

[0112] On the other hand, the present invention further provides a control system using the above-mentioned intelligent connected vehicle platoon collision avoidance control method, comprising:

[0113] Risk perception and processing module, which performs risk perception and processing during vehicle driving;

[0114] Motion planning and tracking module, which performs path planning and tracking after risk perception and processing;

[0115] The system unification and coordination module performs unified coordination based on path planning and tracking.

[0116] The beneficial effect of the present invention is that the control method for collision avoidance of intelligent connected vehicle platoons includes: risk perception and processing during vehicle driving, and path planning and tracking after risk perception and processing; unified coordination based on path planning and tracking; achieving collision avoidance during platoon driving, by performing two operations of merging and splitting the formation, and implementing two sets of optimization strategies based on model predictive control for collision avoidance path planning and trajectory tracking in two modes of single-vehicle cruising and multi-vehicle platooning, respectively.

[0117] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention are realized and obtained by the structures particularly pointed out in the description, claims and drawings.

[0118] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, this article specifically cites preferred embodiments and provides detailed descriptions as follows in conjunction with the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0119] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0120] Figure 1 A principle block diagram of a method for avoiding collisions in a platoon of intelligent networked vehicles provided in an embodiment of the present disclosure;

[0121] Figure 2 A flowchart for determining a feasible speed set of a host vehicle provided by an embodiment of the present disclosure;

[0122] Figure 3 A schematic diagram of a vehicle motion model provided in an embodiment of the present disclosure;

[0123] Figure 4 A schematic diagram of the operation conversion process between different modes provided by an embodiment of the present disclosure;

[0124] Figure 5 A schematic diagram of a vehicle code according to a state change rule after a Merge event is triggered provided in an embodiment of the present disclosure;

[0125] Figure 6 A schematic diagram of a vehicle code showing a state change rule after a Split event is triggered provided in an embodiment of the present disclosure;

[0126] Figure 7 A schematic diagram of a formation collaborative collision avoidance scenario provided by an embodiment of the present disclosure;

[0127] Figure 8 A driving risk value and a collision risk threshold map perceived by each host vehicle provided in an embodiment of the present disclosure;

[0128] Fig. 9 A diagram showing changes in the longitudinal forces of the tires of each main vehicle provided in an embodiment of the present disclosure;

[0129] Fig.10A diagram showing the change in the front wheel angle of each main vehicle provided in the embodiment of the present disclosure;

[0130] Fig.11 A diagram showing changes in the longitudinal speed of each vehicle provided in an embodiment of the present disclosure;

[0131] Fig.12 A diagram showing changes in the lateral positions of each vehicle provided in an embodiment of the present disclosure;

[0132] Fig.13 A diagram showing changes in lateral acceleration of each main vehicle provided in an embodiment of the present disclosure;

[0133] Fig.14 A diagram showing changes in the inter-vehicle distance between each main vehicle and the vehicle ahead provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0134] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0135] As used herein, the phrases "in one embodiment," "according to one embodiment," "in some embodiments," and the like generally refer to the fact that the particular feature, structure, or characteristic following the phrase may be included in at least one embodiment of the present disclosure. Therefore, a particular feature, structure, or characteristic may be included in more than one embodiment of the present disclosure, so that these phrases do not necessarily refer to the same embodiment. As used herein, the terms "example," "exemplary," and the like are used to "serve as an example, instance, or illustration." Any implementation, aspect, or design described herein as "example" or "exemplary" is not necessarily to be construed as preferred or superior to other implementations, aspects, or designs. On the contrary, the use of the terms "example," "exemplary," and the like is intended to present concepts in a concrete manner.

[0136] Some embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0137] like Figures 1 to 14As shown, an embodiment of the present disclosure provides a control method for collision avoidance of an intelligent connected vehicle platoon, including: risk perception and processing during vehicle driving; path planning and tracking after risk perception and processing; unified coordination based on path planning and tracking; collision avoidance during platoon driving is achieved by performing two operations, merging and splitting the platoon, and implementing two sets of optimization strategies based on model predictive control for collision avoidance path planning and trajectory tracking in two modes, namely, single-vehicle cruising and multi-vehicle platooning.

[0138] It integrates DSRC, 5G / V2X and other intelligent network connection technologies, and can realize real-time transmission and control of information. In order to achieve collision avoidance during platoon driving, two operations are performed on the formation, and two sets of optimization strategies based on model predictive control are implemented for collision avoidance path planning and trajectory tracking in the two modes of vehicle single-vehicle cruising and multi-vehicle platooning. According to the triggering events of the formation splitting and merging, it flexibly switches between the vehicle single-vehicle cruising and multi-vehicle platooning modes to achieve the avoidance of collision risks during the coordinated driving of the formation. It is mainly composed of three hierarchical modules, an upper layer for system unification and coordination, a middle layer for risk perception and processing, and a lower layer for motion planning and tracking.

[0139] In one embodiment, the risk perception and processing during vehicle driving includes: based on the dynamic window method, the problem of searching for the optimal path in the geometric space is converted into the problem of searching for the optimal speed set in the speed space, and the feasible speed set of the vehicle is obtained, that is, the achievable speed set is determined: the speed of the vehicle during driving is not globally achievable, and when the actual movement restrictions of the vehicle are taken into account, a speed range can be obtained, which is the speed that the main vehicle can achieve;

[0140] If the current speed is v(t) = (v(t), θ(t)), considering the maximum acceleration limit of the main vehicle, in a limited time period Δt, the speed range that the main vehicle can achieve should be:

[0141]

[0142] Among them, RV represents the achievable speed set; and Respectively represent the maximum / minimum acceleration of linear velocity, and Respectively represent the maximum / minimum acceleration of angular velocity;

[0143] Due to the restrictions of driving rules, the main vehicle should drive on the road or in the designated lane. Therefore, there is a speed range that the main vehicle cannot reach. That is, the speed that causes the main vehicle to leave the drivable area is:

[0144]

[0145] Among them, ROV represents the speed set that causes the main vehicle to leave the drivable area; a and b are the boundaries of the drivable area.

[0146] In one embodiment, considering the driving safety of the main vehicle, the main vehicle needs to leave the speed range where there is a risk of driving collision. Driving risk quantification: For moving obstacles in different directions and distances, the driving collision risks faced by the main vehicle are different. Therefore, it is impossible to accurately measure the collision risks faced by the vehicle during driving only by the geometric shape of the obstacle. The driving risk caused by the obstacle vehicle to the main vehicle is related to its physical properties, motion state, road conditions and the vector distance between vehicles; vehicle virtual mass: it is a virtual parameter that combines the actual mass and speed of the obstacle vehicle, and is used to evaluate its impact on the risk of driving collision;

[0147] Vehicle virtual mass: M = m (1.566 × 10 -14 ·v 6.687 +0.3345)(3);

[0148] Where M is the virtual mass of the vehicle; m and v are the actual mass and speed of the obstacle vehicle respectively; vehicle type:

[0149] Vehicle type: mainly considers the size, weight, speed performance of different types of vehicles and the degree of damage they may cause in a collision, so as to more accurately assess the impact of different types of vehicles on the risk of traffic collision;

[0150] Vehicle Type:

[0151] Where T is the vehicle type attribute indicator; s and k represent the size and type of the vehicle respectively, s * and k * are standard values, ω1 and ω2 are undetermined constants;

[0152] Pseudo-distance: It takes into account not only the actual physical distance between vehicles, but also their relative speed and direction, so that even if the actual distance between two vehicles is the same, if they are moving in opposite directions or at a higher relative speed, then the pseudo-distance between them will be shorter, indicating a higher risk of collision;

[0153]

[0154] Among them, (x s ,y s ) and (x o ,y o ) are the positions of the main vehicle and the obstacle vehicle, ε and ρ are undetermined coefficients;

[0155] Acceleration: The risk of collision is greater in the direction of acceleration than in other directions; it takes into account the acceleration of the vehicle in its current direction of motion. The greater the acceleration, the greater the risk of collision.

[0156] Acceleration:

[0157] in, is the acceleration index; a is the acceleration vector of the obstacle vehicle, Represents the direction of a and the vector r from the main vehicle to the obstacle vehicle d The clockwise angle between them, K is an undetermined coefficient;

[0158] Road conditions: It takes into account factors such as road smoothness, visibility, and slipperiness;

[0159] Road conditions:

[0160] Among them, R is the road condition index; ∈ is the road visibility; μ is the road adhesion coefficient; ρ is the road curvature; τ is the road slope; γ1, γ2, γ3, γ4 are all unknown constants, ∈ * is the standard road visibility, μ * is the standard road adhesion coefficient, ρ * is the standard road curvature, τ * is the standard road slope;

[0161] Based on the above influencing factors, the driving risks caused by obstacles on the road are:

[0162]

[0163] Among them, G and ξ are undetermined coefficients;

[0164] Determination of collision speed set: Based on the idea of ​​speed barrier method, at a certain time t in the future f , when the main vehicle and the obstacle vehicle appear at the same position at the same time, it is considered that a collision occurs, that is:

[0165]

[0166] Among them, P s (t f ) and P0(t f ) respectively represent the f The positions of the main vehicle and the obstacle vehicle at the moment; P s (t0) is the position of the main vehicle at time t0; v s is the speed of the host vehicle;

[0167] Since the vehicle is an entity with a shape, the location when a collision occurs is not just a point, but an area covered by the vehicle geometry. This area contains all the points of the cumulative shape of the main vehicle and the obstacle vehicle, that is:

[0168]

[0169] in, represents Minkowski addition; ConfP(S, O) represents the position covered by the vehicle;

[0170] The main vehicle and the obstacle vehicle are regarded as two vehicles with radii of r s and r O ConfP(S, O) is a circle with a radius of r. s +r O The set of points covered by the extended circle of , from which we can get:

[0171] ConfP(S,O)={P|||PO||≤r s +r O}(11);

[0172] Among them, P represents the possible collision point, and O is the center of mass of the obstacle vehicle;

[0173] Solution about v s Equation (10) shows that at t f The speed set of the main vehicle that caused the collision at the moment:

[0174]

[0175] The set of collision velocities at any time t is the union of SVO, where t belongs to the interval (t0, ∞);

[0176]

[0177] The speeds in the set GVO are the set of speeds at which the main vehicle collides with the obstacle vehicle at a certain time in the future (t0,∞);

[0178] Normally, after the main vehicle detects a collision risk, it will not take anti-collision measures immediately. Instead, the vehicle will continue to maintain its current motion state until the driving risk reaches the collision warning threshold. Therefore, it is necessary to adjust the speed of the main vehicle so that it will not be in the collision speed set after the driving risk reaches the collision warning threshold at a certain moment in the future. To avoid collisions. To ensure driving safety, the main vehicle cannot travel within the following speed ranges:

[0179]

[0180] in, Indicates the driving risk when the collision warning threshold is reached; Indicates a risk of collision The GVO area expands as the collision warning threshold increases, thereby obtaining a feasible speed set with a lower level of collision risk, and shrinks as the collision warning threshold decreases, thereby obtaining a feasible speed set with a higher level of collision risk.

[0181] In summary, the feasible speed set of the main vehicle considering vehicle dynamics constraints, driving rules requirements and collision avoidance requirements can be obtained, which is expressed as:

[0182]

[0183] In one embodiment, the path planning and tracking after risk perception and processing includes: motion modeling based on a vehicle dynamics model, using dynamic equations to model the vehicle motion state, and the vehicle motion model is shown in the following figure:

[0184]

[0185] The motion of the vehicle relative to the global coordinate system is:

[0186]

[0187] Where X and Y represent the lateral and longitudinal positions of the vehicle, respectively; θ represents the heading angle of the vehicle; v x and v y Respectively represent the lateral and longitudinal speeds of the vehicle; r represents the yaw angular velocity; I Z represents the moment of inertia; l f and l r Respectively represent the distance from the vehicle's center of gravity to the rear axle and the front axle; F yT Indicates the longitudinal force of the tire; F xf and F xr Represents the lateral forces on the front and rear wheels.

[0188] In one embodiment, if the vehicle has front-wheel steering, using a linear tire model, the lateral force is expressed as:

[0189]

[0190] Among them, α f and α r Represent the side slip angles of the front and rear wheels respectively; δ is the front wheel turning angle; C f and C f denote the cornering stiffness of the front and rear wheels respectively;

[0191] By linearizing equations (16)-(20), we can obtain the linear dynamic equations;

[0192] The system state vector is: x = [Y v y X v x θ r] T ;

[0193] The control input is: z = [F yT δ] T , the control output is: y = [X v y θ] T ;

[0194]

[0195] y = Cx (24);

[0196] in:

[0197]

[0198]

[0199] In order to p Internal prediction control output, discretize the above model into an incremental prediction model:

[0200] Δx(k+1)=A k Δx(k)+B k Δz(k) (25)

[0201] y(k)=C k Δx(k)+y(k-1) (26);

[0202] Among them, Δx(k)=x(k)-x(k-1), Δz(k)=z(k)-z(k-1);

[0203] Prediction time domain N p The intra-prediction output vector and the control time domain N c (N p >N c ) is the control input increment vector:

[0204]

[0205] In one embodiment, the motion planning task is considered as a predictive tracking control problem with multiple constraints. The action sequence of the desired control input and output in equations (25)-(28) is generated and updated by repeatedly solving the optimal control problem in a finite time in a predictive manner in a model predictive control scheme. At each time step, an open-loop optimal problem with a cost function and operating constraints is solved in a finite time domain. For the free cruise mode, only the path with the lowest risk is sought to maintain the desired motion state. The cost function J corresponding to this mode is defined as crus As shown in 29a. For the multi-vehicle platooning mode, the vehicle also needs to maintain unified motion with the vehicle in front of it. The cost function J corresponding to this mode is pltn As shown in 29b, motion planning tasks of different modes can be uniformly described as a corresponding multi-constraint optimization problem:

[0206]

[0207]

[0208] st(k=1,…,N p )

[0209]

[0210] y s (k)≤y s_max +ε k (33);

[0211] ε k ≥0(34);

[0212]

[0213] a≤x(k)≤b(37);

[0214] v min (k)≤v(k)≤v max (k)(38);

[0215] θ min (k)≤θ(k)≤θ max (k)(39);

[0216]

[0217] F xT ≤T max / R eff (45);

[0218] The multi-objective function includes collision risk, control output, control input, slack variables, and the longitudinal and lateral errors of the predicted trajectory between the host vehicle and its preceding vehicle. X and W Y is the weight factor of the above sub-goal; X pre and Y pre is the lateral and longitudinal position of the leading vehicle; d is the distance between two consecutive vehicles in the formation; Equations (30)-(31) predict the vehicle's motion state and tracking output respectively. Among them, A, B, C and D are the state, input, output and feedforward matrices respectively. Constraint variables In Eq. (32), linearized as a function of state and input, C s and D s is the output and feedforward matrix. Equations (33)-(34) give the vehicle actuator and motion speed constraints. Equations (35) and (36) guide the main vehicle to select a collision-free speed. Equation (37) constrains the main vehicle to travel within the drivable area. Equations (38)-(41) limit the achievable speed and angle, where v min and v max and θ min and θ max are the lower and upper limits of speed and steering angle, respectively. Formula (42) limits the number of control inputs to save computational cost. Formulas (43)-(45) are the lateral and longitudinal force constraints of the tire. yf0_max and F yr0_max is the maximum lateral force on the front and rear wheels; F xT_max is the maximum longitudinal force of the tire; W is the weight of the vehicle; h is the height of the center of gravity of the vehicle from the ground; F xT is the tire longitudinal force; T max is the maximum thrust torque, R eff is the wheel radius;

[0219] At each time step, no matter what mode the main vehicle is in, the MPC-based motion planning controller will continuously observe the real-time driving environment information of the main vehicle, predict the trajectory of the obstacle vehicle, and calculate the size and distribution of driving risks around the main vehicle within the prediction time range. Once the driving risk of the main vehicle at any time step exceeds the specified threshold, the collision avoidance path planning algorithm will be triggered, that is, the collision speed set GVO is calculated using the speed barrier method, the feasible speed set FV is determined using the dynamic window method, and then the cost function is solved to generate the optimal collision avoidance path and speed. Finally, the generated control scheme is output to the main vehicle for execution;

[0220] In a dynamic environment, if the driving environment around the main vehicle remains unchanged, or the driving risk intensity that changes in the future time step does not exceed the threshold, the main vehicle will continue to execute the current control plan; otherwise, the controller will update the driving risk intensity, GVO, FV and control plan according to environmental changes and trends.

[0221] In one embodiment, the unified coordination based on path planning and tracking includes: adopting an automaton architecture to realize the collaboration and operation of formation merging and splitting, and defining the automaton architecture as a tuple Q, as shown in formula (46):

[0222] Q=<Manv,VehSta,ConInt,ConOut,Event,TranProc,SubSyst,SybCons,SubInit> (46);

[0223] Among them, manv = {Cruising; Platooning} is a finite set of operations, representing the control mode of the system; VehSta = [Y, v y ,X,v x , θ, r] is the state vector of the system related to the host vehicle and its neighboring vehicles; ConInt = [F yT , δ T ] is the control input vector of the main vehicle, which is composed of the tire longitudinal force and the front wheel angle; ConOut = [X, v y , θ] is the control output vector of the main vehicle, which consists of the lateral position, longitudinal velocity and heading angle; Event = {Merge, Split} is the trigger event set of the system mode switching, including formation merging and splitting; SubSyst, SybCons, SubInit, TranProc represent the continuously controlled vehicle subsystem, mode transition operation constraints, initial conditions and transition process, respectively.

[0224] In one embodiment, the system state set, in order to accurately track the conversion operation of the system state, the motion states of the main vehicle and the obstacle vehicle are:

[0225] The motion status of the main vehicle:

[0226]

[0227] Among them, VehID is the main vehicle identifier; VehLop, VehLap, VehSpd, VehAng, VehAcc, VehAngAcc represent the longitudinal position, lateral position, speed, heading angle, acceleration and angular acceleration of the main vehicle respectively; PltnDumy is the formation activation flag, which is 1 when the main vehicle is allowed to join the formation and 0 when the main vehicle is cruising alone; PltnID is the fleet identifier;

[0228] PitnNum, PltnHdwy, PltnSpd, and PrecedID represent the number of vehicles in the formation, the preset vehicle spacing, the preset speed, and the identifier of the vehicle in front of the main vehicle, respectively.

[0229] The motion state of the obstacle vehicle:

[0230]

[0231] Among them, ObsID is the obstacle vehicle identifier; ObsLop, ObsLap, ObsSpd, ObsAng, ObsAcc, ObsAngAcc represent the obstacle vehicle's longitudinal position, lateral position, speed, heading angle, acceleration, and angular acceleration respectively;

[0232] ReltSpd, ReltAng, RsltDis, ObsCRPF, and ObsPrty represent the relative speed, relative heading angle, relative distance, collision risk intensity, and the priority of the main vehicle in dealing with the obstacle vehicle, respectively;

[0233] The operation transition process between different modes is shown in the figure below. Under the initial conditions, the main vehicle is either operating in the single-vehicle cruising mode or in the multi-vehicle formation mode. The cost function indicators defined in 29a and 29b guide the execution behavior of the vehicle system. When the operation transition signal is activated, the control constraints and targets of the controlled main vehicle are adjusted by merging and splitting the formation, and the main vehicle switches its operation mode from the current state to another operation mode according to the updated control instructions.

[0234] In one embodiment, formation merging (Merge) and splitting (Split) are two important events in the collaborative formation control system. A single cruising vehicle or a formation vehicle following the queue can be regarded as a candidate vehicle for forming a new formation. The main vehicle will continuously monitor the motion state of the candidate vehicle. If the requirements of the following speed and vehicle spacing are met, the merging signal will be activated to form a new formation with the candidate vehicle. The main vehicle will modify its motion state and follow the candidate vehicle at an appropriate speed and vehicle spacing; the state change rules of the vehicle before and after the merger are shown in Table 1 below. In Table 1, L1 is the code of the leading vehicle added to the front team, and F1 is the code of the following vehicle at the rear of the front team. If the front is a single vehicle, the state quantities of F1 are equal to the parameters of L1, and the corresponding change rules remain unchanged. The state change rules of other following vehicles in the front team are the same as F1. L2 is the code of the leading vehicle joining the team behind, and F2 is the code of the following vehicle joining the team behind. If the team joining the team behind is a single vehicle, the corresponding rules remain unchanged, and the relevant items of F2 in the table do not need to be changed. The state change rules of other following vehicles in the rear team are the same as F2.

[0235] Table 1 Status change rules after the Merge event is triggered

[0236]

[0237]

[0238] In the event of a platoon split, the main vehicle in the multi-vehicle platoon will continuously monitor the changes in the vehicle in front of it and the surrounding driving environment. If the driving conditions no longer meet the requirements of platoon following driving, the split signal will be triggered to separate the main vehicle from the current platoon. The main vehicle will modify its motion state to separate from the current platoon. The changes in the motion state of the vehicle before and after the split are shown in Table 2 below:

[0239] Table 2 Status change rules after Split event is triggered

[0240]

[0241]

[0242] In a specific embodiment, there are four lanes on the structured road, including two main lanes, a ramp lane, and an auxiliary lane transitioning from the main lane to the ramp. The lane width is 4m. A convoy of five vehicles travels in the right main lane at a speed of 25m / s. An obstacle vehicle changes lanes from the left main lane to the ramp, with a longitudinal speed of 22m / s and a lateral acceleration of -0.5m / s 2 . In order to avoid the obstacle vehicle, the controlled main vehicle needs to separate from the convoy first and then merge back into the convoy. Through simulation, the driving risk value and collision risk threshold map, tire longitudinal force change map, front wheel angle change map, longitudinal speed change map, lateral position change map, lateral acceleration change map, and vehicle spacing change map perceived by each controlled main vehicle in the convoy during the collaborative collision avoidance process are obtained;

[0243] In at least one other embodiment, a control system using the above-mentioned intelligent networked vehicle formation collision avoidance control method is also provided, including: a risk perception and processing module, which performs risk perception and processing during vehicle driving, and is responsible for detecting collision risks and generating collision avoidance plans by analyzing comprehensive information on vehicle motion status and traffic conditions; a motion planning and tracking module, which performs path planning and tracking after risk perception and processing, and is responsible for using the vehicle motion planning controller to accurately track the collision avoidance motion state and trajectory of the vehicle under different driving modes; a system unification and coordination module, which performs unified coordination based on path planning and tracking, and is responsible for the unification and coordination between the functional modules of the entire system. It is responsible for defining the motion status of vehicles and obstacles, establishing switching rules between single-vehicle cruising and multi-vehicle formation modes, and determining event triggering rules for formation splitting and merging.

[0244] In summary, the control method for collision avoidance of the intelligent networked vehicle formation includes: risk perception and processing during vehicle driving, and path planning and tracking after risk perception and processing;

[0245] Unified coordination is carried out based on path planning and tracking; collision avoidance is achieved during platoon driving by performing merging and splitting operations on the platoon, and two sets of optimization strategies based on model predictive control are implemented for collision avoidance path planning and trajectory tracking in the two modes of single-vehicle cruising and multi-vehicle platooning.

[0246] Based on the above ideal embodiments of the present invention, the relevant staff can make various changes and modifications without departing from the technical concept of the present invention through the above description. The technical scope of the present invention is not limited to the contents of the specification, and its technical scope must be determined according to the scope of the claims.

Claims

1. A control method for collision avoidance of an intelligent networked vehicle formation, characterized in that: include: Sense and handle risks while the vehicle is in motion; Path planning and tracking after risk perception and processing; Unified coordination based on path planning and tracking.

2. The method for controlling collision avoidance of an intelligent connected vehicle platoon as claimed in claim 1, characterized in that: The risk perception and processing during vehicle driving includes: Based on the dynamic window method, the problem of searching for the optimal path in geometric space is transformed into the problem of searching for the optimal speed set in speed space, and the feasible speed set of vehicles is obtained, that is, Determine the set of achievable velocities: If the current speed is v(t) = (v(t), θ(t)), in a limited time period Δt, the speed range that the main vehicle can achieve should be: Among them, RV represents the achievable speed set; and Respectively represent the minimum / maximum linear acceleration, and Respectively represent the minimum / maximum angular acceleration; The speed that causes the main vehicle to leave the drivable area is: Among them, ROV represents the speed set that causes the main vehicle to leave the drivable area; a and b are the boundaries of the drivable area.

3. The control method for collision avoidance of an intelligent networked vehicle platoon as claimed in claim 2, characterized in that: The main vehicle needs to leave the speed range where there is a risk of collision. The driving risk needs to be quantified: Vehicle virtual mass: M = m (1.566 × 10 -14 ·v 6.687 +0.3345); Among them, m and v are the actual mass and speed of the obstacle vehicle respectively; Vehicle Type: Where s and k represent the size and type of the vehicle, respectively. * and k * are standard values, ω1 and ω2 are undetermined constants; Among them, (x s ,y s ) and (x o ,y o ) are the positions of the main vehicle and the obstacle vehicle, ε and ρ are undetermined coefficients; Acceleration: Where a is the acceleration vector of the obstacle vehicle, θ a Represents the direction of a and the vector r from the main vehicle to the obstacle vehicle d The clockwise angle between them, K is an undetermined coefficient; Road conditions: Among them, ε is the road visibility; μ is the road adhesion coefficient; ρ is the road curvature; τ is the road slope; γ1, γ2, γ2, γ4 are all unknown constants, ε * is the standard road visibility, μ * is the standard road adhesion coefficient, ρ* is the standard road curvature, and τ* is the standard road slope; The driving risks caused by obstacles on the road are: Among them, G and ξ are undetermined coefficients; The collision velocity set is determined by: At some point in the future f , when the main vehicle and the obstacle vehicle appear at the same position at the same time, it is considered that a collision occurs, that is: Among them, P s (t f ) and P0(t f ) respectively represent the f The positions of the main vehicle and the obstacle vehicle at the moment; P s (t0) is the position of the host vehicle at time t0; vs is the speed of the host vehicle; The location at the time of the collision is an area covered by the vehicle geometry, which contains all the points of the cumulative shape of the host vehicle and the obstacle vehicle, namely: in, represents Minkowski addition; ConfP(S, O) represents the position covered by the vehicle; The main vehicle and the obstacle vehicle are regarded as two vehicles with radii of r s and r O ConfP(S, O) is a circle with a radius of r. s +r O The set of points covered by the extended circle of , thus we get: ConfP(S,O)={P|||P-O||≤r s +r O }; Among them, P represents the possible collision point, and O is the center of mass of the obstacle vehicle; In t f The speed set of the main vehicle that caused the collision at the moment: The set of collision velocities at any time t is the union of SVO, where t belongs to the interval (t0, ∞); The speeds in the set GVO are the set of speeds at which the main vehicle collides with the obstacle vehicle at a certain time in the future (t0,∞); By adjusting the speed of the main vehicle, it will no longer be in the collision speed set after the driving risk reaches the collision warning threshold at a certain moment in the future. To avoid collision, the main vehicle cannot travel within the following speed range: in, Indicates the driving risk when the collision warning threshold is reached; Indicates a risk of collision The GVO area expands as the collision warning threshold increases, thereby obtaining a feasible speed set with a lower level of collision risk, and shrinks as the collision warning threshold decreases, thereby obtaining a feasible speed set with a higher level of collision risk. The feasible speed set of the main vehicle considering vehicle dynamics constraints, driving rules requirements and collision avoidance requirements is obtained, which is expressed as:

4. The control method for collision avoidance of an intelligent networked vehicle platoon as claimed in claim 3, characterized in that: The path planning and tracking after risk perception and processing includes: The vehicle motion state is modeled using the dynamic equation. The vehicle motion model: The motion of the vehicle relative to the global coordinate system is: Where X and Y represent the lateral and longitudinal positions of the vehicle, respectively; θ represents the heading angle of the vehicle; v x and v y Respectively represent the lateral and longitudinal speeds of the vehicle; r represents the yaw angular velocity; I Z represents the moment of inertia; l f and l r Respectively represent the distance from the vehicle's center of gravity to the rear axle and the front axle; F yT Indicates the longitudinal force of the tire; F xf and F xr Represents the lateral forces on the front and rear wheels.

5. The control method for collision avoidance of an intelligent networked vehicle platoon as claimed in claim 4, characterized in that: If the vehicle has front-wheel steering, using the linear tire model, the lateral force is expressed as: Among them, α f and α r Represent the side slip angles of the front and rear wheels respectively; δ is the front wheel turning angle; C f and C f denote the cornering stiffness of the front and rear wheels respectively; The system state vector is: x = [Y v y X v x θ r] T ; The control input is: z = [F yT δ] T , the control output is: y = [X v y θ] T ; in: In order to p Internal prediction control output, discretize the above model into an incremental prediction model: Among them, Δx(k)=x(k)-x(k-1), Δz(k)=z(k)-z(k-1); Prediction time domain N p The intra-prediction output vector and the control time domain N c (N p >N c ) is the control input increment vector:

6. The method for controlling collision avoidance of an intelligent networked vehicle platoon as claimed in claim 5, characterized in that: Based on the motion planning of model predictive control, for the free cruise mode, only the path with the lowest risk is sought to maintain the desired motion state. The cost function corresponding to this mode is defined as J crus ; For the multi-vehicle platoon mode, the vehicle also needs to maintain unified motion with the vehicle in front of it. The cost function corresponding to this mode is J pltn ; The motion planning tasks of different modes are uniformly described as a corresponding multi-constraint optimization problem: s.t.(k=1,…,N p ) y s (k)≤y s_max +e k ; e k ≥0; a≤x(k)≤b; in min (k)≤v(k)≤v max (to); θ min (k)≤θ(k)≤θ max (k); F xT ≤T max / R eff ; The multi-objective function includes collision risk, control output, control input, slack variables, and the longitudinal and lateral errors of the predicted trajectory between the main vehicle and its preceding vehicle; Q, R, P, W X and W Y is the weight factor of the above sub-goal; X pre and Y pre is the lateral and longitudinal position of the leading vehicle; d is the distance between two consecutive vehicles in the formation; At each time step, no matter what mode the main vehicle is in, the MPC-based motion planning controller will continuously observe the real-time driving environment information of the main vehicle, predict the trajectory of the obstacle vehicle, and calculate the size and distribution of driving risks around the main vehicle within the prediction time range. Once the driving risk of the main vehicle at any time step exceeds the specified threshold, the collision avoidance path planning algorithm will be triggered, that is, the collision speed set GVO is calculated using the speed barrier method, the feasible speed set FV is determined using the dynamic window method, and then the cost function is solved to generate the optimal collision avoidance path and speed. Finally, the generated control scheme is output to the main vehicle for execution; In a dynamic environment, if the driving environment around the main vehicle remains unchanged, or the driving risk intensity that changes in the future time step does not exceed the threshold, the main vehicle will continue to execute the current control plan; otherwise, the controller will update the driving risk intensity, GVO, FV and control plan according to environmental changes and trends.

7. The method for controlling collision avoidance of an intelligent networked vehicle platoon as claimed in claim 6, characterized in that: The unified coordination based on path planning and tracking includes: An automaton architecture is used to realize the collaboration and operation of formation merging and splitting. The automaton architecture is defined as a tuple Q: Q=<Manv,VehSta,ConInt,ConOut,Event,TranProc,SubSyst,SybCons,SubInit> Among them, Manv = {Cruising; Platooning} is a finite set of operations, representing the control mode of the system; VehSta=[Y,v y ,X,v x , θ, r] is the state vector of the system related to the host vehicle and its neighboring vehicles; ConInt=[F yT , δ T ] is the control input vector of the host vehicle, which is composed of the tire longitudinal force and the front wheel steering angle; ConOut = [X, v y , θ] is the control output vector of the main vehicle, which consists of the lateral position, longitudinal velocity and heading angle; Event = {Merge, Split} is the trigger event set for system mode switching, including formation merging and splitting; SubSyst, SybCons, SubInit, and TranProc represent the continuously controlled vehicle subsystem, modal transition operation constraints, initial conditions, and transition process, respectively.

8. The method for controlling collision avoidance of an intelligent networked vehicle platoon as claimed in claim 7, characterized in that: In order to accurately track the conversion operation of the system state, the motion states of the main vehicle and the obstacle vehicle are: The motion status of the main vehicle: Among them, VehID is the main vehicle identifier; VehLop, VehLap, VehSpd, VehAng, VehAcc, VehAngAcc represent the longitudinal position, lateral position, speed, heading angle, acceleration and angular acceleration of the main vehicle respectively; PltnDumy is the formation activation flag, which is 1 when the main vehicle is allowed to join the formation and 0 when the main vehicle is cruising alone; PltnID is the fleet identifier; PltnNum, PltnHdwy, PltnSpd, and PrecedID represent the number of vehicles in the formation, the preset vehicle spacing, the preset speed, and the vehicle identifier in front of the main vehicle, respectively; The motion state of the obstacle vehicle: Among them, ObsID is the obstacle vehicle identifier; ObsLop, ObsLap, ObsSpd, ObsAng, ObsAcc, ObsAngAcc represent the obstacle vehicle's longitudinal position, lateral position, speed, heading angle, acceleration, and angular acceleration respectively; ReltSpd, ReltAng, ReltDis, ObsCRPF, and ObsPrty represent the relative speed, relative heading angle, relative distance, collision risk intensity, and the priority of the main vehicle in dealing with the obstacle vehicle, respectively; When the operation transition signal is activated, the control constraints and targets of the controlled master vehicle are adjusted through formation merging and splitting, and the master vehicle switches its operation mode from the current state to another operation mode according to the updated control instructions.

9. The method for controlling collision avoidance of an intelligent networked vehicle platoon as claimed in claim 8, characterized in that: A single cruising vehicle or a platoon vehicle following behind a platoon is considered a candidate vehicle for forming a new platoon. The lead vehicle will continuously monitor the motion state of the candidate vehicle. If the following speed and vehicle spacing requirements are met, the merge signal will be activated to form a new platoon with the candidate vehicle. The lead vehicle will modify its motion state to follow the candidate vehicle at an appropriate speed and vehicle spacing. In a formation split event, the lead vehicle in a multi-vehicle formation will continuously monitor changes in the vehicle in front of it and the surrounding driving environment. If the driving conditions no longer meet the requirements of formation following driving, a split signal will be triggered to separate the lead vehicle from the current formation. The lead vehicle will modify its motion state to separate from the current formation.

10. A control system using the intelligent connected vehicle platoon collision avoidance control method as claimed in claim 1, characterized in that: include: Risk perception and processing module, which performs risk perception and processing during vehicle driving; Motion planning and tracking module, which performs path planning and tracking after risk perception and processing; The system unification and coordination module performs unified coordination based on path planning and tracking.

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