A cooperative lane-changing control method for CACC platoon in mixed traffic flow environment

Abstract

The application discloses a CACC vehicle platoon cooperative lane-changing control method in a mixed traffic flow environment, and steps include: determining an initial lane-changing gap of a target lane and a target connected automatic driving vehicle controlled cooperatively with the CACC vehicle platoon; obtaining vehicle motion state information of the CACC vehicle platoon, the target connected automatic driving vehicle and a preceding vehicle thereof; constructing a longitudinal control method, generating a lane-changing gap through vehicle cooperative deceleration, and controlling the vehicle to complete lane-changing by using a lateral control algorithm; and after the CACC vehicle platoon finishes lane-changing, controlling the vehicle to cruise at an expected distance by using the longitudinal control method again. When the vehicle gap in the target lane does not have lane-changing conditions, the CACC vehicle platoon can effectively realize lane-changing through cooperative control between vehicles.

Description

Technical Field

[0001] This invention relates to the field of intelligent connected autonomous vehicles and traffic control, specifically to a CACC (Cooperative Adaptive Cruise Control) method for vehicle fleet cooperative lane changing in a mixed traffic flow environment. Background Technology

[0002] In recent years, with the rapid development of vehicle-to-everything (V2X) and autonomous driving technologies, connected and autonomous vehicle technology has attracted increasing attention from researchers. Cooperative Adaptive Cruise Control (CACC), as an important control technology, is widely used in the field of connected and autonomous vehicles. CACC vehicles are more sensitive, have shorter reaction times, and smaller time intervals between vehicles in a platoon. By coordinating the speed and spacing between vehicles, it can maximize the use of road space and improve traffic efficiency.

[0003] Existing research on CACC vehicles largely focuses on longitudinal control. However, in real-world traffic conditions, which are complex and ever-changing, lane-changing maneuvers are unavoidable when CACC vehicles face challenges such as navigating merging lanes, exiting main roads, or encountering accidents ahead. Current CACC control methods lack research on lane-changing issues from a platoon-wide perspective. Controlling a CACC platoon to change lanes often disrupts the initial platoon structure. Existing control methods rarely address issues such as how to control lane changes when there is no suitable clearance in the target lane for the platoon or any individual vehicle, how the CACC platoon coordinates with connected autonomous vehicles (CAVs) in the target lane, and how vehicles adjust their motion based on real-time conditions. Furthermore, there is a lack of research on lateral and longitudinal control collaboration at the platoon level based on control theory.

[0004] Therefore, in mixed traffic flow environments, how to help CACC fleets complete lane changes when there is insufficient clearance in the target lane is an urgent problem to be solved. Summary of the Invention

[0005] The technical problem to be solved by this invention is to provide a CACC (Continuous Accelerated Collision-Assisted Vehicle) lane-changing control method that can create lane-changing gaps in a mixed traffic flow environment, in order to solve the problem of insufficient target lane gaps when CACC vehicles change lanes.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0007] A CACC (Competitive Accelerator and Collision Avoidance) vehicle cooperative lane-changing control method in a mixed traffic flow environment includes the following steps:

[0008] S1) Select any connected autonomous vehicle in the target lane as the target connected autonomous vehicle, and use the gap between the target connected autonomous vehicle and the vehicle in front of it as the initial lane-changing gap of the CACC fleet.

[0009] S2) Real-time acquisition of vehicle motion status information of the CACC fleet, target connected autonomous vehicles and their predecessors;

[0010] S3) Create lane-changing gaps sequentially for vehicles starting from the last vehicle in the CACC convoy. Specifically, a preset longitudinal control method is used. Based on the expected distance and control target before the lane change is completed, and according to the vehicle motion state information of the CACC convoy, the target connected autonomous vehicle and the CACC convoy, the target connected autonomous vehicle and the CACC convoy are controlled to decelerate in coordination so that the size of the lane-changing gap for each vehicle meets the requirements.

[0011] S4) The vehicles starting from the last car in the CACC fleet will change lanes sequentially. Specifically, when the lane change gap of the corresponding vehicle meets the requirements, the vehicle will be tracked for lane change. Then, the vehicle will be controlled to change lanes along the planned trajectory using the preset vehicle lateral dynamics model and lateral control algorithm. For the last car after lane change, the vehicle will be controlled to continue driving in the longitudinal direction at a constant speed.

[0012] S5) Using a preset longitudinal control method, based on the expected spacing after lane change and the control target, the CACC fleet vehicles are controlled to cruise at the expected spacing after lane change.

[0013] Furthermore, the longitudinal control method specifically includes:

[0014] Before the CACC fleet completes a lane change, the last car in the CACC fleet is used as the guide car for the target connected autonomous vehicle. The last car in the CACC fleet is guided by the car in front of it in the corresponding lane change gap, and the remaining cars in the CACC fleet are guided by the adjacent car behind them. If the distance between the cars in the CACC fleet and the car in front of it in the corresponding lane change gap is less than the safe lane change distance, the target connected autonomous vehicle and all cars in the CACC fleet are controlled to decelerate in accordance with the movement state of the corresponding guide car until the distance between the cars in the CACC fleet and the cars in front and behind it in the corresponding lane change gap is greater than or equal to the safe lane change distance.

[0015] After the CACC convoy finishes changing lanes, it controls each vehicle to use the adjacent vehicle in front as a guide vehicle to cruise at the desired spacing and speed.

[0016] Furthermore, the longitudinal control method is constructed with vehicle driving safety, car-keeping performance, and comfort as its objectives. The construction process of the longitudinal control method is as follows:

[0017] The system state-space equation is obtained based on the vehicle motion state information of the controlled vehicle and the corresponding guide vehicle.

[0018] The longitudinal performance indicators of the system are designed with safety, car-keeping, and comfort in mind, and control targets are set before and after lane change.

[0019] Set system longitudinal constraints and introduce relaxation factors to expand the range of constraint conditions;

[0020] Based on the system state-space equation and system longitudinal performance index, a system longitudinal prediction model is established, and a comprehensive performance function is constructed with the goal of optimal car-keeping performance and comfort, while ensuring vehicle driving safety.

[0021] The weights of safety, car-keeping, and comfort of the controller are adaptively optimized and adjusted using a fuzzy control algorithm.

[0022] Furthermore, the expression for the system state-space equation is as follows:

[0023]

[0024] ; ;

[0025] in, , , It is a coefficient matrix; , These represent the control quantity and the disturbance quantity, respectively. , which is a system state variable; for The following distance between the vehicle being controlled at all times and the guide vehicle; for The relative speed between the vehicle and the guide vehicle is constantly being controlled; for The vehicle's speed is constantly being controlled; for The acceleration of the vehicle is constantly being controlled; for The rate of change of acceleration of the vehicle being controlled at all times; The system sampling time; It is a time constant;

[0026] The expression for the system's longitudinal performance index is as follows:

[0027]

[0028] in, The following distance error, which represents safety, is specifically the error between the actual following distance and the expected following distance before or after lane change. As a representative of chasm The relative speed between the vehicle and the guide vehicle is constantly being controlled; and These represent comfort. The acceleration and rate of change of acceleration of the vehicle are constantly being controlled;

[0029] The constraints of the system's vertical constraints are as follows:

[0030]

[0031] in, , , , , These are the following distance between the controlled vehicle and the guide vehicle, the speed of the controlled vehicle, the acceleration of the controlled vehicle, the rate of change of the acceleration of the controlled vehicle, and the lower limit of the hard constraint of the control quantity. , , , , The following distance between the controlled vehicle and the guide vehicle, the speed of the controlled vehicle, the acceleration of the controlled vehicle, the rate of change of the acceleration of the controlled vehicle, and the upper limit of the hard constraints on the control variables. , , , , It is a relaxation factor; , , , , The following distance between the controlled vehicle and the guide vehicle, the speed of the controlled vehicle, the acceleration of the controlled vehicle, the rate of change of the acceleration of the controlled vehicle, and the lower limit of the soft constraint of the control quantity; , , , , The following distance between the controlled vehicle and the guide vehicle, the speed of the controlled vehicle, the acceleration of the controlled vehicle, the rate of change of the acceleration of the controlled vehicle, and the upper limit of the soft constraint of the control quantity;

[0032] The system longitudinal prediction model is a system longitudinal prediction model considering constraints in a quadratic optimization problem, and its expression is as follows:

[0033]

[0034]

[0035] in, and These are the coefficient matrices generated during the derivation of the quadratic form. Represents the control quantity matrix; , These are the hard constraint matrices representing the lower and upper limits of the system state variables, respectively. , These are the hard constraint matrices representing the upper and lower limits of the control quantity, respectively. , These are the soft constraint matrices representing the lower and upper limits of the system state variables, respectively. , These are the soft constraint matrices representing the upper and lower limits of the control quantity, respectively. Let be the relaxation factor matrix. , , , It is a coefficient matrix;

[0036] The expression for the comprehensive performance function is as follows:

[0037]

[0038] in, , and These are the weight matrices for performance error, control input, and soft constraint coefficients, respectively. This represents the system's longitudinal performance indicators. Indicates the control quantity. Indicating ideal performance, Represents a quadratic function. This is the soft constraint coefficient;

[0039] The weights of safety, car-keeping, and comfort of the controller are adaptively optimized and adjusted using a fuzzy control algorithm.

[0040] Furthermore, the control objective before lane changing is to control the speed difference between vehicles, vehicle acceleration, and rate of acceleration change to approach zero, while ensuring that the headway between vehicles is not less than the lane changing safety clearance.

[0041] Furthermore, the expected clearance before lane changing is completed is greater than or equal to the lane changing safety clearance, and the expression for the lane changing safety clearance is as follows:

[0042]

[0043]

[0044] in, and These represent the front and rear safety clearances required for lane changing; Represents vehicle reaction time; Represents the braking delay time; Represents the vehicle's minimum acceleration; This represents the speed of the vehicle after the lane change interval; This represents the speed of the vehicle in front during the lane change interval; The speed of the controlled vehicle.

[0045] Furthermore, when planning the lane-changing trajectory for the vehicle, a fifth-order polynomial is used to plan the vehicle's lane-changing trajectory, as shown in the following expression:

[0046]

[0047] in, and for The vertical and horizontal positions of the time plan. , , are the coefficients of the polynomial.

[0048] Furthermore, the vehicle lateral dynamics model adopts a linear two-degree-of-freedom model, as shown below:

[0049]

[0050] in, The yaw angle of the controlled vehicle. For the quality of the controlled vehicles. and These are the lateral stiffnesses of the front and rear wheels of the controlled vehicle, respectively. The longitudinal speed of the controlled vehicle. and These are the distances from the front and rear axles of the controlled vehicle to its center of gravity, respectively. Let be the moment of inertia of the controlled vehicle about its axis perpendicular to the ground. For the front wheel steering angle, This represents the lateral displacement in the vehicle coordinate system.

[0051] Furthermore, the cost function of the lateral control algorithm, with error elimination as its core, is constructed using the Lagrange multiplier method, as shown below:

[0052]

[0053] in, and These are the error state matrices. and control quantity The penalty matrix, and It is the coefficient matrix obtained by discretization and simplification using the Euler method. For Lagrange multipliers, and The error state matrix and control quantity are discretized. The error state matrix after discretization One of them.

[0054] Furthermore, the control objective after lane changing is to control the speed difference between vehicles, vehicle acceleration, and rate of change of acceleration to approach zero, while ensuring that the distance between vehicles approaches the desired distance for cruising. The expression for the desired distance after lane changing is as follows:

[0055]

[0056] in, This refers to the workshop time distance coefficient; Minimum following distance; The speed difference between the controlled vehicle and the lead vehicle in the convoy; The coefficient is constant. The speed of the controlled vehicle.

[0057] Compared with the prior art, the advantages of the present invention are as follows:

[0058] By combining lateral and longitudinal control that take into account vehicle dynamics, the system achieves integrated control of the CACC convoy's lane change preparation, lane change execution, and cruise after the lane change, enabling the CACC convoy to complete lane changes stably and orderly.

[0059] By coordinating deceleration control of the CACC convoy and the CAV in the target lane, the distance between the vehicle and the vehicle in front of the target gap is increased as quickly as possible while ensuring the rear safety clearance, so as to generate the front safety clearance that meets the requirements of CACC vehicle lane change.

[0060] By classifying the vehicle's safety status, the weights of performance indicators in the multi-objective system longitudinal prediction model are adjusted in real time to output the optimal result suitable for the current working conditions. Attached Figure Description

[0061] Figure 1 This is a flowchart of the CACC (Cyber-Assisted Accelerator) vehicle coordinated lane-changing control system according to an embodiment of the present invention.

[0062] Figure 2 This is a schematic diagram of a scenario according to an embodiment of the present invention.

[0063] Figure 3 This is a schematic diagram of the control method according to an embodiment of the present invention.

[0064] Figure 4 This is a schematic diagram illustrating the application of fuzzy control in the longitudinal control algorithm in an embodiment of the present invention.

[0065] Figure 5 This is a schematic diagram of the vehicle's motion trajectory according to an embodiment of the present invention. Detailed Implementation

[0066] The present invention will be further described below with reference to the accompanying drawings and specific preferred embodiments, but this does not limit the scope of protection of the present invention.

[0067] This example provides a cooperative adaptive cruise control (CACC) platooning lane-changing control method for mixed traffic flow scenarios on highways. When there is insufficient clearance between vehicles in front and behind the target lane, it generates sufficient clearance to meet lane-changing requirements through cooperative control of the CACC platoon and the target connected autonomous vehicle (CAV), enabling the CACC platoon to complete lane changes stably and orderly. Figure 1 As shown, it includes the following steps:

[0068] S1) Select any CAV in the target lane as the target CAV. In this embodiment, the lane that the CACC fleet travels in after changing lanes is called the target lane, and the lane that the CACC fleet travels in before changing lanes is called the current lane. Select the gap between the target CAV and the vehicle in front of it as the initial lane-changing gap of the CACC fleet.

[0069] S2) Based on technologies such as vehicle-to-everything (V2X), it can obtain real-time vehicle motion status information of the CACC fleet, the target CAV and the vehicle in front of it;

[0070] S3) Create lane-changing gaps sequentially for vehicles starting from the last car in the CACC convoy. Specifically, a preset longitudinal control method is used. Based on the expected spacing and control target before the lane change is completed, and according to the vehicle motion state information of the CACC convoy, the target CAV and the CACC vehicles are controlled to decelerate in coordination so that the size of the lane-changing gap corresponding to the vehicle meets the requirements.

[0071] S4) The vehicles starting from the last car in the CACC fleet change lanes in sequence. Specifically, when the lane change gap of the corresponding vehicle meets the requirements, the vehicle's lane change trajectory is planned. Then, the preset vehicle lateral dynamics model and lateral control algorithm are used to control the vehicle to change lanes along the planned trajectory. After the last car changes lanes, the last car is controlled to continue driving in the longitudinal direction at a state of constant speed.

[0072] S5) Using the same longitudinal control method as in step S3, based on the desired spacing after lane change and the control objective, control the CACC fleet vehicles to cruise at the desired spacing after lane change.

[0073] It should be noted that the method in this embodiment is applied to mixed traffic flow in an intelligent connected environment, and makes the following assumptions: There are CAVs (Autonomous Vehicles) available for cooperative control in the traffic flow on the target lane; the vehicles in the CAV and CACC (Autonomous Accelerator and Cruise Control) fleets are the same type of autonomous intelligent connected vehicles, and can be cooperatively controlled according to different objectives. When the CACC fleet needs to change lanes, but encounters situations such as... Figure 2 When the lane change clearance is insufficient, the control method proposed in this embodiment begins to be implemented after the corresponding position of the last vehicle in the CACC convoy in the current lane is within the gap between the target CAV and the vehicle in front in the target lane. The steps are explained in detail below.

[0074] In step S1 of this embodiment, the vehicle preceding the target connected autonomous vehicle (CAV) can be any vehicle, such as... Figure 3 As shown, when the position of the last vehicle in the CACC fleet in the current lane is located at the position corresponding to the gap between the target connected autonomous vehicle (CAV) and the vehicle in front in the target lane, cooperative control begins.

[0075] In step S2 of this embodiment, the vehicle motion state information includes the vehicle's speed, acceleration, and position information, which are used for the coordinated control of the CACC fleet and the target CAV in step 3.

[0076] Step S3 of this embodiment is based on a model predictive control algorithm with vehicle dynamics and adaptive weights to construct a longitudinal control method with the objectives of vehicle driving safety, car following, and comfort. The method generates lane change gaps one by one, starting from the last vehicle in the convoy, through coordinated vehicle deceleration.

[0077] Specifically, in step S3 of this embodiment, the object of collaborative control is... Figure 3 The CACC convoy and target CAV shown are specifically designed for coordinated deceleration control of the controlled objects based on an adaptive weighted model predictive control algorithm. The longitudinal control method specifically includes:

[0078] Before the CACC team completes the lane change, the last car of the CACC team will be used as the lead car of the target CAV. The last car of the CACC team will be led by the car in front of it in the corresponding lane change gap (i.e., the car in front of the target CAV). The remaining cars of the CACC team will be led by the adjacent car behind them.

[0079] If the distance between a vehicle in the CACC convoy and the vehicle in front of it in the corresponding lane change gap is less than the safe lane change distance, the controlled target CAV and all vehicles in the CACC convoy will follow the movement of the corresponding guide vehicle and decelerate until the distance between the vehicle and the vehicles in front and behind it in the corresponding lane change gap is greater than or equal to the safe lane change distance.

[0080] After the CACC convoy finishes changing lanes, it controls each vehicle to use the adjacent vehicle in front as a guide vehicle to cruise at the desired spacing and speed.

[0081] Before the lane change is completed, step S3 performs longitudinal control to sequentially create the safety clearances required for CACC vehicle lane changes. Taking the last car in a CACC convoy changing lanes as an example, such as... Figure 3 As shown, the target CAV in the target lane moves with the last car in the CACC platoon as its guide car, maintaining a sufficient lane-changing safety clearance. The last car uses the car in front of it (i.e., the target CAV) at the initial lane-changing clearance as its guide car, adjusting itself based on the longitudinal distance between itself and the guide car and the guide car's movement. When the distance between itself and the guide car is less than the lane-changing safety clearance, the last car will decelerate to create the necessary clearance. The remaining vehicles in the CACC platoon all move with their adjacent following car as their guide cars, following the last car and decelerating. This ensures that sufficient forward safety clearance is created between the vehicles in front of the lane-changing clearance corresponding to the target lane before changing lanes. Once the forward and backward clearance of the CACC vehicles meets the requirements, the lane change takes place.

[0082] The process of constructing the longitudinal control method in step S3 of this embodiment is as follows:

[0083] S3.1) Constructing the system vehicle state-space expression: In this embodiment, the vehicle is simplified as a moving point mass in the longitudinal direction, ignoring factors such as air resistance, rolling resistance, communication delay, and faults. The operation of the CACC fleet system in the longitudinal direction is affected by each vehicle; therefore, the following distance, speed, relative speed with the vehicle in front, acceleration, and rate of change of acceleration during vehicle movement are selected as system state variables. Based on the vehicle motion state information of the controlled vehicle and the corresponding guide vehicle, the system state-space equation is obtained, as follows:

[0084]

[0085] ; ;

[0086] in, , , It is a coefficient matrix; , These represent the control quantity and the disturbance quantity, respectively. , which is a system state variable; for The following distance between the vehicle being controlled at all times and the guide vehicle; for The relative speed between the vehicle and the guide vehicle is constantly being controlled; for The vehicle's speed is constantly being controlled; for The acceleration of the vehicle is constantly being controlled; for The rate of change of acceleration of the vehicle being controlled at all times; The system sampling time; It is a time constant;

[0087] S3.2) Constructing System Longitudinal Performance Indicators: Considering safety, camber response, and comfort, design the system's longitudinal performance indicators.

[0088]

[0089] in, The following distance error, which represents safety, is specifically the error between the actual following distance and the expected following distance before or after lane change. The relative speed between vehicles is used to represent car-following performance. and These are acceleration and rate of change of acceleration, respectively, used to represent comfort.

[0090] In this embodiment, to ensure that the vehicles in the convoy follow the vehicle in front comfortably and stably with a safe following distance in the longitudinal direction, the actual following distance and the expected following distance error, vehicle speed, relative speed, acceleration and acceleration change rate are selected as performance indicators for model predictive control. Since the longitudinal control of the vehicles is divided into two stages before and after the lane change is completed, the expected distance and control objectives are different in different stages.

[0091] Specifically, the control objective before lane changing is to ensure that the headway between vehicles (i.e., the desired headway before lane changing) is not less than the lane changing safety clearance, while controlling the speed difference between vehicles, vehicle acceleration, and rate of change of acceleration to approach zero. The expression for the lane changing safety clearance is:

[0092]

[0093]

[0094] in, and These represent the front and rear safety clearances required for lane changing; Represents vehicle reaction time; Represents the braking delay time; Represents the vehicle's minimum acceleration; This represents the speed of the vehicle after the lane change interval; This represents the speed of the vehicle in front during the lane change interval; This represents the speed of vehicles changing lanes.

[0095] As can be seen, since there are actual following distance and expected following distance, when the actual following distance is less than the expected following distance, the vehicle will decelerate under the longitudinal control method of this embodiment, and the acceleration will be adjusted in real time according to the change of distance.

[0096] S3.3) Constructing System Longitudinal Constraints: System longitudinal constraints are set, and a relaxation factor is introduced to expand the constraint range. Considering that strict physical constraints may lead to vehicle instability, a relaxation factor is introduced to expand the feasible region. Without affecting the normal operation of the system, the constraint range is appropriately expanded. The constraint conditions are as follows:

[0097]

[0098] in, , , , , These are the following distance between the controlled vehicle and the guide vehicle, the speed of the controlled vehicle, the acceleration of the controlled vehicle, the rate of change of the acceleration of the controlled vehicle, and the lower limit of the hard constraint of the control quantity. , , , , The following distance between the controlled vehicle and the guide vehicle, the speed of the controlled vehicle, the acceleration of the controlled vehicle, the rate of change of the acceleration of the controlled vehicle, and the upper limit of the hard constraints on the control variables. , , , , It is a relaxation factor; , , , , The following distance between the controlled vehicle and the guide vehicle, the speed of the controlled vehicle, the acceleration of the controlled vehicle, the rate of change of the acceleration of the controlled vehicle, and the lower limit of the soft constraint of the control quantity; , , , , The following distance between the controlled vehicle and the guide vehicle, the speed of the controlled vehicle, the acceleration of the controlled vehicle, the rate of change of the acceleration of the controlled vehicle, and the upper limit of the soft constraint of the control quantity;

[0099] S3.4) Construction of the System Longitudinal Prediction Model: Based on the previously constructed system state-space equations and system longitudinal performance indicators, a prediction model in the form of a constraint matrix in the quadratic optimization problem is established and used as the prediction model for the MPC controller. The prediction model expression is as follows:

[0100]

[0101]

[0102] in, and These are the coefficient matrices generated during the derivation of the quadratic form. Represents the control quantity matrix; , These are the hard constraint matrices representing the lower and upper limits of the system state variables, respectively. , These are the hard constraint matrices representing the upper and lower limits of the control quantity, respectively. , These are the soft constraint matrices representing the lower and upper limits of the system state variables, respectively. , These are the soft constraint matrices representing the upper and lower limits of the control quantity, respectively. Let be the relaxation factor matrix. , , , It is a coefficient matrix;

[0103] Based on the analysis of the system's longitudinal performance indicators, the comprehensive performance function, constructed with the goal of optimizing both following speed and comfort while ensuring the driving safety of CACC vehicles, can be expressed as follows:

[0104]

[0105] in, , and These are the weight matrices for performance error, control input, and soft constraint coefficients, respectively. This represents the longitudinal performance index of the system, specifically... The system's longitudinal performance metrics at any given time. This represents the control quantity, specifically... The amount of control at any given moment Indicating ideal performance, Represents a quadratic function. This is the soft constraint coefficient;

[0106] S3.5) Constructing an Adaptive Weighting Method: As a supplement to step S3.2, this embodiment uses a fuzzy control algorithm to adaptively optimize and adjust the weights of safety, car-following, and comfort in the controller. Vehicles have different requirements for safety, car-following, and comfort under different driving conditions. When there is a risk of collision, the weight of safety indicators needs to be increased; conversely, the weight of comfort can be increased. Fuzzy control theory is used to adaptively optimize and adjust the weights of safety, car-following, and comfort in the MPC controller. Figure 4 As shown, in this embodiment, the reciprocal of the workshop time distance and the collision time distance is used as the basis for collision risk judgment and input into the fuzzy controller to obtain the safety, following performance, and comfort weights after adaptive optimization.

[0107] In step S4 of this embodiment, lateral control is performed on the CACC vehicle that meets the lane change clearance requirements. First, a fifth-order polynomial is used to program the lane change trajectory. Then, lateral lane change control is performed on the vehicle based on a two-degree-of-freedom vehicle model and the LQR algorithm. Figure 3 As shown, whenever the lane-changing gap corresponding to a vehicle meets the lane-changing requirements through coordinated control in step S3, lateral control is performed on the vehicle to achieve lane-changing one by one, starting from the last vehicle in the CACC convoy. Specifically, this includes:

[0108] S4.1) Lane-changing trajectory planning: The lane-changing trajectory of the vehicle is planned using a fifth-order polynomial, as shown in the following expression:

[0109]

[0110] in, and for The vertical and horizontal positions of the time plan. , , The coefficients are those of a polynomial. The lane-changing trajectory of the vehicle is repeatedly calculated as the vehicle moves until the lane change is completed.

[0111] S4.2) Vehicle Lateral Dynamics Model Selection: A linear two-degree-of-freedom model is selected as the lateral dynamics control model. The vehicle state-space equations under two degrees of freedom are shown below:

[0112]

[0113] in, The yaw angle of the controlled vehicle. For the quality of the controlled vehicles. and These are the lateral stiffnesses of the front and rear wheels of the controlled vehicle, respectively. The longitudinal speed of the controlled vehicle. and These are the distances from the front and rear axles of the controlled vehicle to its center of gravity, respectively. Let be the moment of inertia of the controlled vehicle about its axis perpendicular to the ground. For the front wheel steering angle, This represents the lateral displacement in the vehicle coordinate system;

[0114] S4.3) Calculate the lateral control cost function: This embodiment focuses on error elimination and uses the Lagrange multiplier method to construct the cost function of the lateral control algorithm, as shown below.

[0115]

[0116] in, and These are the error state matrices. and control quantity The penalty matrix, and It is the coefficient matrix obtained by discretization and simplification using the Euler method. For Lagrange multipliers, This is the error state matrix after discretization. The control quantity is after discretization. The error state matrix after discretization One of them.

[0117] like Figure 3 As shown, in step S4 of this embodiment, after the last car in the CACC convoy completes its lane change, the gap between the last car and the original preceding car of the target CAV becomes the lane change gap for the second-to-last car in the CACC convoy. Since the last car continues to travel at a near-constant speed after completing its lane change, and deceleration control was performed before the last car completed its lane change, the lane change gap for the second-to-last car continuously increases. During this process, the longitudinal control method in step S3 can also be executed to ensure that the second-to-last car maintains a lane change gap with the preceding car (i.e., the car in front of the target CAV) by controlling the coordinated deceleration of the vehicles. Figure 3 The original front vehicle (HDV) and rear vehicle (i.e., the target CAV) of the middle target CAV Figure 3 The spacing between the last car in the CCAC convoy is greater than or equal to the lane-changing safety clearance. Once the lane-changing clearance of the second-to-last car meets the requirement, step S4 is executed again to control the second-to-last car to change lanes. This process is repeated, with steps S3 and S4 executed in sequence, so that the remaining cars in the convoy can change lanes in sequence while meeting the lane-changing clearance requirements.

[0118] In step 5 of this embodiment, after the CACC convoy completes a lane change, the vehicles are no longer subject to lateral control. Instead, longitudinal control enables the CACC vehicles to cruise at the desired distance. Specifically, after the CACC convoy completes a lane change, each vehicle in the convoy uses the adjacent vehicle in front as a guide vehicle. When the distance between a vehicle in the CACC convoy and the guide vehicle is not equal to the desired distance after the lane change, the longitudinal control method adjusts itself based on the desired distance after the lane change and the control target after the lane change, enabling the CACC convoy to cruise at the desired distance and speed.

[0119] Correspondingly, the control objective after lane change is to control the speed difference between vehicles, vehicle acceleration, and rate of acceleration change to approach zero, under the premise that the distance between vehicles tends to the desired distance for cruising (i.e., the desired distance after lane change), so as to control the CAAC convoy to cruise at the desired distance after lane change.

[0120] In this embodiment, the expression for the expected spacing after lane changing is as follows:

[0121]

[0122] in, This refers to the workshop time distance coefficient; Minimum following distance; The speed difference between the controlled vehicle and the lead vehicle in the convoy; The coefficient is constant. The speed of the controlled vehicle.

[0123] As mentioned above, the longitudinal control method used in step 5 of this embodiment is basically the same as the longitudinal control method used in step 3. The difference is that the expected spacing and control target of the system longitudinal performance index of the longitudinal control method adopt the expected spacing and control target after the lane change is completed.

[0124] The effectiveness of the method in this embodiment will be verified through specific experiments below.

[0125] A co-simulation platform using CarSim / Matlab / Simulink was built to verify the feasibility and effectiveness of the control method. The vehicle model from CarSim was imported into Matlab / Simulink, where relevant modules, S-functions, and script files were used to control the vehicle.

[0126] Specifically, using a speed of 72 km / h, the CACC convoy consists of three vehicles, numbered Vehicle 2, Vehicle 3, and Vehicle 4, with Vehicle 2 being the rear vehicle. In the target lane, Vehicle 5 is the following vehicle, and Vehicle 1 is the preceding vehicle. Vehicle 5 is a controllable connected autonomous vehicle, while Vehicle 1 is an uncontrolled human-driven vehicle. The initial lateral position of the CACC convoy is -1.65m. When all CACC vehicles reach a lateral position of 1.65m, it indicates that the entire CACC convoy has completed the lane change. The movement trajectories of each vehicle are as follows... Figure 5 As shown, initially, the lateral position of all vehicles was -1.65m. Due to the insufficient longitudinal lane change clearance, no vehicles changed lanes. Subsequently, as the control method was implemented, the longitudinal clearance met the requirements, and the convoy began to change lanes one by one.

[0127] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A CACC (Competitive Accelerated Lane Changing) method for vehicle fleet coordination in a mixed traffic flow environment, characterized in that, Includes the following steps: S1) Select any connected autonomous vehicle in the target lane as the target connected autonomous vehicle, and use the gap between the target connected autonomous vehicle and the vehicle in front of it as the initial lane-changing gap of the CACC fleet; where CACC represents Cooperative Adaptive Cruise Control. S2) Real-time acquisition of vehicle motion status information of the CACC fleet, target connected autonomous vehicles and their predecessors; S3) Create lane-changing gaps sequentially for vehicles starting from the last vehicle in the CACC convoy. Specifically, a preset longitudinal control method is used. Based on the desired distance and control target before the lane change is completed, and according to the vehicle motion state information of the CACC convoy, the target connected autonomous vehicle, and the vehicle in front, the target connected autonomous vehicle and the CACC vehicle are controlled to decelerate in coordination, so that the size of the lane-changing gap for each vehicle meets the requirements. The longitudinal control method specifically includes: Before the CACC fleet completes a lane change, the last car in the CACC fleet is used as the guide car for the target connected autonomous vehicle. The last car in the CACC fleet uses the car in front of it in the corresponding lane change gap as its guide car, and the remaining cars in the CACC fleet use the adjacent car behind them as their guide cars. If the distance between the cars in the CACC fleet and the car in front of it in the corresponding lane change gap is less than the safe lane change distance, the target connected autonomous vehicle and all cars in the CACC fleet are controlled to decelerate in accordance with the movement state of the corresponding guide car until the distance between the cars in the CACC fleet and the cars in front and behind them in the corresponding lane change gap is greater than or equal to the safe lane change distance. After the CACC convoy finishes changing lanes, each vehicle is controlled to cruise at the desired distance and speed, with the adjacent vehicle in front acting as the guide vehicle. S4) The vehicles starting from the last car in the CACC fleet change lanes sequentially. Specifically, when the lane change gap of the corresponding vehicle meets the requirements, the vehicle's lane change trajectory is planned. Then, the preset vehicle lateral dynamics model and lateral control algorithm are used to control the vehicle to change lanes along the planned trajectory. For the last car after the lane change, the vehicle is also controlled to continue driving in the longitudinal direction at a speed that tends to be constant. S5) Using a preset longitudinal control method, based on the expected spacing after lane change and the control target, the CACC fleet vehicles are controlled to cruise at the expected spacing after lane change.

2. The CACC (Competitive Accelerator and Collaborative Lane Changing) method for vehicle fleet coordination in a mixed traffic flow environment according to claim 1, characterized in that, The longitudinal control method is constructed with the objectives of vehicle driving safety, car-keeping performance, and comfort. The construction process of the longitudinal control method is as follows: The system state-space equation is obtained based on the vehicle motion state information of the controlled vehicle and the corresponding guide vehicle. The longitudinal performance indicators of the system are designed with safety, car-keeping, and comfort in mind, and control targets are set before and after lane change. Set system longitudinal constraints and introduce relaxation factors to expand the range of constraint conditions; Based on the system state-space equation and system longitudinal performance index, a system longitudinal prediction model is established, and a comprehensive performance function is constructed with the goal of optimal car-keeping performance and comfort, while ensuring vehicle driving safety. The weights of safety, car-keeping, and comfort of the controller are adaptively optimized and adjusted using a fuzzy control algorithm.

3. The CACC (Competitive Lane Changing Control) method for vehicle fleet coordination in a mixed traffic flow environment according to claim 2, characterized in that, The expression for the system state-space equation is as follows: ； ； in, , , It is a coefficient matrix; , These represent the control quantity and the disturbance quantity, respectively. , which is a system state variable; for The following distance between the vehicle being controlled at all times and the guide vehicle; for The relative speed between the vehicle and the guide vehicle is constantly being controlled; for The vehicle's speed is constantly being controlled; for The acceleration of the vehicle is constantly being controlled; for The rate of change of acceleration of the vehicle being controlled at all times; This refers to the system sampling time. It is a time constant; The expression for the system's longitudinal performance index is as follows: in, The following distance error, which represents safety, is specifically the error between the actual following distance and the expected following distance before or after lane change. As a representative of chasm The relative speed between the vehicle and the guide vehicle is constantly being controlled; and These represent comfort. The acceleration and rate of change of acceleration of the vehicle are constantly being controlled; The constraints of the system's vertical constraints are as follows: in, , , , , These are the following distance between the controlled vehicle and the guide vehicle, the speed of the controlled vehicle, the acceleration of the controlled vehicle, the rate of change of the acceleration of the controlled vehicle, and the lower limit of the hard constraint of the control quantity. , , , , The following distance between the controlled vehicle and the guide vehicle, the speed of the controlled vehicle, the acceleration of the controlled vehicle, the rate of change of the acceleration of the controlled vehicle, and the upper limit of the hard constraints on the control variables. , , , , It is a relaxation factor; , , , , The following distance between the controlled vehicle and the guide vehicle, the speed of the controlled vehicle, the acceleration of the controlled vehicle, the rate of change of the acceleration of the controlled vehicle, and the lower limit of the soft constraint of the control quantity; , , , , The following distance between the controlled vehicle and the guide vehicle, the speed of the controlled vehicle, the acceleration of the controlled vehicle, the rate of change of the acceleration of the controlled vehicle, and the upper limit of the soft constraint of the control quantity; The system longitudinal prediction model is a system longitudinal prediction model considering constraints in a quadratic optimization problem, and its expression is as follows: in, and These are the coefficient matrices generated during the derivation of the quadratic form. Represents the control quantity matrix; , These are the hard constraint matrices representing the lower and upper limits of the system state variables, respectively. , These are the hard constraint matrices representing the upper and lower limits of the control quantity, respectively. , These are the soft constraint matrices representing the lower and upper limits of the system state variables, respectively. , These are the soft constraint matrices representing the upper and lower limits of the control quantity, respectively. Let be the relaxation factor matrix. , , , It is a coefficient matrix; The expression for the comprehensive performance function is as follows: in, , and These are the weight matrices for performance error, control input, and soft constraint coefficients, respectively. This represents the system's longitudinal performance indicators. Indicates the control quantity. Indicating ideal performance, Represents a quadratic function. is the relaxation factor, representing the soft constraint coefficient; The weights of safety, car-keeping, and comfort of the controller are adaptively optimized and adjusted using a fuzzy control algorithm.

4. The CACC (Competitive Lane Changing Control) method for vehicle fleet coordination in a mixed traffic flow environment according to claim 3, characterized in that, The control objective before lane change is to control the speed difference between vehicles, vehicle acceleration, and rate of change of acceleration to approach zero, while ensuring that the headway between vehicles is not less than the lane change safety clearance. The control objective after lane change is to control the speed difference between vehicles, vehicle acceleration, and rate of acceleration change to approach zero, while ensuring that the distance between vehicles approaches the desired distance for cruising.

5. The CACC (Competitive Lane Changing Control) method for vehicle fleet coordination in a mixed traffic flow environment according to claim 4, characterized in that, The expected clearance before lane changing is completed is greater than or equal to the lane changing safety clearance. The expression for the lane changing safety clearance is: in, and These represent the front and rear safety clearances required for lane changing; Represents vehicle reaction time; Represents the braking delay time; Represents the vehicle's minimum acceleration; This represents the speed of the vehicle after the lane change interval; This represents the speed of the vehicle in front during the lane change interval; The speed of the controlled vehicle.

6. The CACC (Competitive Lane Changing Control) method for vehicle fleet coordination in a mixed traffic flow environment according to claim 4, characterized in that, The expression for the expected spacing after lane changing is as follows: in, This refers to the workshop time distance coefficient; Minimum following distance; The speed difference between the controlled vehicle and the lead vehicle in the convoy; The coefficient is constant. The speed of the controlled vehicle.

7. The CACC (Competitive Lane Changing Control) method for vehicle fleet coordination in a mixed traffic flow environment according to claim 1, characterized in that, When planning the lane-changing trajectory for a vehicle, a fifth-order polynomial is used, as shown in the following expression: in, and for The vertical and horizontal positions of the time plan. , , are the coefficients of the polynomial.

8. The CACC (Competitive Accelerator and Collaborative Lane Changing) method for vehicle fleet coordination in a mixed traffic flow environment according to claim 1, characterized in that, The vehicle lateral dynamics model uses a linear two-degree-of-freedom model, as shown below: in, The yaw angle of the controlled vehicle. For the quality of the controlled vehicles. and These are the lateral stiffnesses of the front and rear wheels of the controlled vehicle, respectively. The longitudinal speed of the controlled vehicle. and These are the distances from the front and rear axles of the controlled vehicle to its center of gravity, respectively. Let be the moment of inertia of the controlled vehicle about its axis perpendicular to the ground. For the front wheel steering angle, This represents the lateral displacement in the vehicle coordinate system.

9. The CACC (Competitive Lane Changing Control) method for vehicle fleet coordination in a mixed traffic flow environment according to claim 1, characterized in that, The cost function of the lateral control algorithm, with error elimination as its core, is constructed using the Lagrange multiplier method, as shown below: in, and These are the error state matrices. and control quantity The penalty matrix, and It is the coefficient matrix obtained by discretization and simplification using the Euler method. For Lagrange multipliers, This is the error state matrix after discretization. The control quantity is after discretization. The error state matrix after discretization One of them.

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