Cooperative control method and device for autonomous vehicle queue

Abstract

The invention discloses an autonomous vehicle queue cooperative control method and device, the distance and speed between vehicles are ensured to be accurately controlled through a longitudinal control strategy, the safe distance can be maintained, the change of traffic flow can be adapted, the fluency and safety of a vehicle queue are improved, a transverse control strategy is responsible for lane keeping, steering and lane changing of the vehicles, and the vehicle queue cooperative control method and device are provided. By accurately controlling the transverse position and the steering angle of the vehicle, it is ensured that the vehicle stably runs in a lane, meanwhile, the lane changing operation can be safely and efficiently completed, the sharp turning and obstacle avoidance cooperative safety control strategy enables a motorcade to rapidly make adjustments when facing a complex scene, sharp turning and obstacle avoidance actions of the vehicle are achieved through cooperative control, and the driving safety of the vehicle is improved. According to the method, collision is avoided, driving safety is kept, information sharing and action coordination between vehicles are achieved through cooperative control of the motorcade, and the safety and stability of the motorcade are further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle platoon control, and in particular to a method and device for cooperative control of an autonomous vehicle platoon. Background Art

[0002] As intelligent transportation systems become increasingly mature, autonomous vehicle platoon collaborative control technology is gradually becoming a key force in promoting innovation and development in the transportation field. This technology aims to achieve close collaboration and optimized scheduling of multiple autonomous vehicles during driving through a highly integrated control system, thereby improving traffic efficiency, ensuring driving safety and reducing energy consumption.

[0003] Traditional vehicle control systems mostly focus on the driving stability and safety of a single vehicle, such as adaptive cruise control (ACC) and lane keeping assist system (LKAS). Although these systems have improved the convenience and safety of driving to a certain extent, they have failed to fully utilize the synergy effect of convoy driving. Summary of the invention

[0004] In view of this, the present invention proposes an autonomous vehicle platoon cooperative control method and device, which can solve the defect of the prior art that the cooperative effect of the platoon driving is not fully utilized.

[0005] The technical solution of the present invention is achieved in this way:

[0006] A method for cooperative control of an autonomous vehicle platoon, specifically comprising:

[0007] Establish a fleet system model to simulate the behavior of the fleet under different driving conditions;

[0008] Based on the fleet system model, formulate the longitudinal control strategy of the vehicle queue, the lateral control strategy of the vehicle queue, and the coordinated safety control strategy of vehicle sharp turning and obstacle avoidance;

[0009] Control the distance and speed between vehicles according to the longitudinal control strategy of the vehicle platoon;

[0010] Control the lateral position of the vehicle, the steering of the vehicle and the lane change of the vehicle according to the lateral control strategy of the vehicle platoon;

[0011] The vehicle's sharp turn steering angle and speed as well as the vehicle's obstacle avoidance action are controlled based on the vehicle's sharp turn and obstacle avoidance collaborative safety control strategy.

[0012] As a further optional solution of the autonomous vehicle platoon cooperative control method, the longitudinal control strategy of the vehicle platoon is formulated based on the platoon system model, specifically including:

[0013] Based on the fleet system model, a vehicle dynamics model is established;

[0014] Build a model of the entire fleet based on the vehicle dynamics model;

[0015] Use linear control theory and nonlinear control theory to analyze the stability of the fleet model and evaluate the response characteristics and stability of the fleet under different driving conditions;

[0016] Perform bidirectional chord stability analysis on the convoy to evaluate the interaction between the front and rear vehicles in the convoy;

[0017] The vehicle control law is designed based on the response characteristics and stability of the convoy, as well as the interaction between the front and rear vehicles in the convoy.

[0018] As a further optional solution of the autonomous vehicle platoon cooperative control method, the vehicle control law is designed based on the response characteristics and stability of the platoon and the interaction between the front and rear vehicles of the platoon, which specifically includes:

[0019] According to the response characteristics and stability of the convoy, as well as the interaction between the front and rear vehicles of the convoy, control objectives are set, including stabilizing the distance between convoys, reducing speed fluctuations, and improving lane keeping accuracy;

[0020] According to the control target, select the corresponding vehicle control law;

[0021] The parameters of the vehicle control law are adjusted to design the vehicle control law.

[0022] As a further optional solution of the autonomous vehicle platoon cooperative control method, the lateral control strategy of the vehicle platoon is formulated based on the platoon system model, specifically including:

[0023] Design a lane keeping control strategy that integrates sensor data from the lane departure warning system and the lane keeping assist system and uses a feedback control strategy to dynamically adjust the vehicle's lateral position to keep the vehicle in the center of the lane;

[0024] Design steering control strategies to adjust the vehicle's steering response using an electric power steering system and gain adjustment control algorithms;

[0025] Design lane change control strategies, adjust the vehicle's lane change parameters through lane change detection and lane change decision algorithms, and execute control methods.

[0026] As a further optional solution of the autonomous vehicle platoon cooperative control method, the steering control strategy is designed to adjust the steering response of the vehicle by adopting an electric steering system and a gain adjustment control algorithm, specifically including:

[0027] Integrate gain regulation control algorithms into electric power steering systems;

[0028] Analyze the steering response characteristics of the vehicle's electric steering system under different speeds and driving conditions;

[0029] Evaluate the steering response characteristics of the electric steering system;

[0030] The gain regulation control algorithm adjusts the parameters of the electric steering system based on the evaluation results.

[0031] As a further optional solution of the autonomous vehicle platoon cooperative control method, the vehicle sharp turn and obstacle avoidance cooperative safety control strategy is formulated based on the platoon system model, specifically including:

[0032] According to the fleet system model, the obstacles and road environment information around the vehicle are detected in real time;

[0033] Accurately identify and locate detected obstacles, and determine their position, size, and motion status;

[0034] Based on the detected obstacle information, formulate an obstacle avoidance path planning strategy, including using a path planning algorithm to calculate the best obstacle avoidance path, and using a local avoidance strategy to adjust the vehicle's driving path in real time;

[0035] When vehicles are avoiding obstacles, the information sharing mechanism of the vehicle team is used to achieve collaborative obstacle avoidance among the vehicles.

[0036] When the vehicle makes a sharp turn, the steering angle and speed are adjusted according to the vehicle's dynamic model.

[0037] An autonomous vehicle platoon cooperative control device, comprising:

[0038] An establishment module is used to establish a fleet system model, wherein the fleet system model is used to simulate the behavior of the fleet under different driving conditions;

[0039] A formulation module is used to formulate the longitudinal control strategy of the vehicle queue, the lateral control strategy of the vehicle queue, and the coordinated safety control strategy of the vehicle sharp turn and obstacle avoidance according to the convoy system model;

[0040] A longitudinal control module, used to control the distance and speed between vehicles according to the longitudinal control strategy of the vehicle platoon;

[0041] A lateral control module, used to control the lateral position of the vehicle, the steering of the vehicle and the lane change of the vehicle according to the lateral control strategy of the vehicle platoon;

[0042] The collaborative safety control module is used to control the vehicle's sharp turning steering angle and speed as well as the vehicle's obstacle avoidance action based on the vehicle's sharp turning and obstacle avoidance collaborative safety control strategy.

[0043] A computing device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the autonomous vehicle platoon cooperative control method when executing the computer program.

[0044] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the autonomous vehicle platoon cooperative control method described above.

[0045] The beneficial effects of the present invention are as follows: by establishing a fleet system model and formulating a detailed control strategy based on the model, the behavior of the fleet under different driving conditions can be effectively simulated, thereby improving the overall stability of the fleet system, including maintaining the stability of the relative speed change and acceleration between the front and rear vehicles of the fleet under complex conditions such as sudden acceleration and deceleration, and preventing excessive oscillation or instability between the fleets. The longitudinal control strategy ensures that the distance and speed between vehicles are accurately controlled, especially in highways and urban traffic, it can maintain a safe distance and adapt to changes in traffic flow, and improve the smoothness and safety of the fleet. The lateral control strategy is responsible for the lane keeping, steering and lane changing of the vehicle, and ensures that the vehicle drives smoothly in the lane by accurately controlling the lateral position and steering angle of the vehicle, while being able to complete the lane change operation safely and efficiently. The sharp turn and obstacle avoidance collaborative safety control strategy enables the fleet to make rapid adjustments when facing complex scenes, and realizes the vehicle's sharp turn and obstacle avoidance actions through collaborative control, avoids collisions and maintains driving safety. Through the collaborative control of the fleet, information sharing and action coordination between vehicles are realized, interference and conflicts within the fleet are reduced, and the safety and stability of the fleet are further improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0047] Figure 1 A schematic flow chart of the steps of a method for cooperative control of an autonomous vehicle platoon provided by an embodiment of the present invention;

[0048] Figure 2 A schematic diagram of the composition of an autonomous vehicle platoon cooperative control device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0049] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only 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.

[0050] refer to Figure 1 to Figure 2 , a cooperative control method for an autonomous vehicle platoon, specifically comprising:

[0051] Establish a fleet system model to simulate the behavior of the fleet under different driving conditions;

[0052] Based on the fleet system model, formulate the longitudinal control strategy of the vehicle queue, the lateral control strategy of the vehicle queue, and the coordinated safety control strategy of vehicle sharp turning and obstacle avoidance;

[0053] Control the distance and speed between vehicles according to the longitudinal control strategy of the vehicle platoon;

[0054] Control the lateral position of the vehicle, the steering of the vehicle and the lane change of the vehicle according to the lateral control strategy of the vehicle platoon;

[0055] The vehicle's sharp turn steering angle and speed as well as the vehicle's obstacle avoidance action are controlled based on the vehicle's sharp turn and obstacle avoidance collaborative safety control strategy.

[0056] In this embodiment, by establishing a convoy system model and formulating a detailed control strategy based on the model, the behavior of the convoy under different driving conditions can be effectively simulated, thereby improving the overall stability of the convoy system. This includes maintaining the stability of the relative speed change and acceleration between the front and rear vehicles of the convoy under complex conditions such as sudden acceleration and deceleration, and preventing excessive oscillation or instability between the convoys. The longitudinal control strategy ensures that the distance and speed between vehicles are accurately controlled, especially in highways and urban traffic, it can maintain a safe distance and adapt to changes in traffic flow, and improve the smoothness and safety of the convoy. The lateral control strategy is responsible for the lane keeping, steering and lane changing of the vehicle. By accurately controlling the lateral position and steering angle of the vehicle, it ensures that the vehicle drives smoothly in the lane, and can complete the lane change operation safely and efficiently. The sharp turn and obstacle avoidance collaborative safety control strategy enables the convoy to make rapid adjustments when facing complex scenes, and realizes the vehicle's sharp turn and obstacle avoidance actions through collaborative control to avoid collisions and maintain driving safety. Through the convoy collaborative control, information sharing and action coordination between vehicles are realized, interference and conflicts within the convoy are reduced, and the safety and stability of the convoy are further improved.

[0057] Preferably, the vehicle queue longitudinal control strategy is formulated based on the fleet system model, specifically including:

[0058] Based on the fleet system model, a vehicle dynamics model is established;

[0059] Build a model of the entire fleet based on the vehicle dynamics model;

[0060] Use linear control theory and nonlinear control theory to analyze the stability of the fleet model and evaluate the response characteristics and stability of the fleet under different driving conditions;

[0061] Perform bidirectional chord stability analysis on the convoy to evaluate the interaction between the front and rear vehicles in the convoy;

[0062] The vehicle control law is designed based on the response characteristics and stability of the convoy, as well as the interaction between the front and rear vehicles in the convoy.

[0063] In this embodiment, by establishing a detailed vehicle dynamics model and building the entire fleet model based on the model, the dynamic behavior of each vehicle in the fleet can be more accurately reflected. This high-precision modeling method helps to simulate the performance of the fleet under various driving conditions more realistically, and provides a solid foundation for the subsequent control strategy design. The stability analysis of the fleet model using linear control theory and nonlinear control theory can comprehensively evaluate the response characteristics and stability of the fleet under different working conditions. This method not only considers the linear characteristics of the system, but also takes into account the influence of nonlinear factors, thereby improving the accuracy and reliability of the analysis. The two-way chord stability analysis pays special attention to the interaction between the front and rear vehicles of the fleet, especially the stability under extreme working conditions such as rapid acceleration or rapid deceleration. This analysis method helps to have a deeper understanding of the dynamic behavior within the fleet, so as to design a more effective control law to improve the overall stability of the fleet. Based on the response characteristics and stability of the fleet, as well as the interaction between the front and rear vehicles of the fleet, the designed vehicle control law can better adapt to changes in actual driving conditions. This control law can not only maintain the stable operation of the fleet under normal working conditions, but also quickly adjust the control strategy under extreme working conditions to ensure the safety and efficiency of the fleet.

[0064] It should be noted that, first, a detailed dynamic model is established for each vehicle, including acceleration, braking, steering and other characteristics. These models should be able to accurately reflect the behavior of the vehicle under different driving conditions. Based on the vehicle dynamics model, a model of the entire fleet is constructed, which includes factors such as interaction between vehicles, communication delay, sensor error, etc. The stability of the fleet model is analyzed using linear control theory to determine the key parameters and influencing factors of the system. For systems with significant nonlinear characteristics, nonlinear control theory is used for supplementary analysis to ensure the stability of the system under various working conditions. The two-way chord stability of the fleet in longitudinal control is focused on. The interaction between the front and rear vehicles of the fleet is analyzed, especially the stability under rapid acceleration or deceleration. According to the results of stability analysis, the vehicle control law is designed. In addition, for fleet system models with strong nonlinearity, linearization methods can be used to convert them into linear systems for analysis. Linearization processing is usually based on the operating point or equilibrium point of the system. High-order terms are ignored through methods such as Taylor expansion to obtain a linearized model of the system. According to linear control theory, appropriate stability criteria are selected to analyze the stability of the fleet system. Common stability criteria include Lyapunov stability theory, small perturbation method, etc. These criteria can help evaluate the stability performance of the fleet system under different working conditions.

[0065] Preferably, the vehicle control law is designed based on the response characteristics and stability of the convoy and the interaction between the front and rear vehicles of the convoy, specifically including:

[0066] According to the response characteristics and stability of the convoy, as well as the interaction between the front and rear vehicles of the convoy, control objectives are set, including stabilizing the distance between convoys, reducing speed fluctuations, and improving lane keeping accuracy;

[0067] According to the control target, select the corresponding vehicle control law;

[0068] The parameters of the vehicle control law are adjusted to design the vehicle control law.

[0069] In this embodiment, by setting control objectives, such as stabilizing the distance between convoys and reducing speed fluctuations, the stability of the convoy in a complex traffic environment can be significantly improved, which helps to prevent unnecessary oscillations and instability within the convoy and ensure the smooth operation of the convoy as a whole; considering the interaction between the front and rear vehicles in the convoy, the designed vehicle control law can respond more quickly to the state changes of the front vehicle, such as acceleration and deceleration, which helps to reduce the lag of the rear vehicle to the state changes of the front vehicle and improve the overall response speed of the convoy; by adjusting the parameters of the vehicle control law, the control effect can be further optimized. For example, by adjusting the proportional, integral and differential coefficients of the PID controller, the control of the distance between convoys can be achieved. More precise control of distance and speed fluctuations can be achieved to improve the overall performance and safety of the fleet. When designing the vehicle control law, lane keeping accuracy is used as one of the control objectives to help improve the driving stability of the vehicle within the lane. By adopting advanced control algorithms, such as model predictive control (MPC), smoother and more accurate lane keeping can be achieved, reducing the safety risks caused by lateral deviation. The designed vehicle control law not only focuses on the control effect of a single vehicle, but also considers the overall collaborative control of the fleet. By introducing the fleet collaborative control strategy, information sharing and coordinated control between vehicles in the fleet can be achieved, improving the dynamic behavior control capability of the fleet in complex scenarios, such as sharp turns and obstacle avoidance.

[0070] Preferably, the vehicle platoon lateral control strategy is formulated based on the platoon system model, specifically including:

[0071] Design a lane keeping control strategy that integrates sensor data from the lane departure warning system and the lane keeping assist system and uses a feedback control strategy to dynamically adjust the vehicle's lateral position to keep the vehicle in the center of the lane;

[0072] Design steering control strategies to adjust the vehicle's steering response using an electric power steering system and gain adjustment control algorithms;

[0073] Design lane change control strategies, adjust the vehicle's lane change parameters through lane change detection and lane change decision algorithms, and execute control methods.

[0074] In this embodiment, the lane keeping control strategy can sense the deviation of the vehicle from the center line of the lane in real time by integrating the sensor data of the lane departure warning system (LDWS) and the lane keeping assist system (LKAS), and dynamically adjust the lateral position of the vehicle by using the feedback control strategy (such as PID control) to ensure that the vehicle travels in the center of the lane, thereby improving the accuracy and stability of lane keeping and reducing the safety risks caused by lateral deviation; the steering control strategy adopts the electric steering system (EPS), and uses its high adjustability and fast response characteristics, combined with the gain adjustment control algorithm, to adjust the control parameters according to the changes in the actual driving environment, so that the steering response of the vehicle is more stable and comfortable, which not only improves the safety of vehicle driving, but also improves the riding experience of passengers; the lane change control strategy can comprehensively evaluate the safety and efficiency of lane change through lane change detection and lane change decision algorithm (such as rule-based decision or machine learning-based decision). In the execution control stage, a smooth lane change process is achieved by accurately controlling the steering angle and speed of the vehicle. This strategy not only reduces the risk of collision during the lane change process, but also improves the driving efficiency and safety of vehicle queues in complex traffic environments.

[0075] It should be noted that the lane keeping control strategy is specifically designed as follows:

[0076] Sensor data fusion: First, use sensors such as the lane departure warning system (LDWS) and lane keeping assist system (LKAS) to obtain key data such as lane position and vehicle lateral position;

[0077] Control algorithm selection: Based on the sensor data, select an appropriate control algorithm, such as PID control or model predictive control (MPC), to adjust the lateral position of the vehicle to keep it in the center of the lane;

[0078] Algorithm optimization: By continuously optimizing the control algorithm, the accuracy and robustness of lane keeping are improved to ensure that the vehicle can stably stay in the lane under various driving conditions.

[0079] The steering control strategy design is as follows:

[0080] Vehicle dynamics modeling: Establish a vehicle dynamics model, including the vehicle's steering response, tire grip and other characteristics, to provide a basis for steering control;

[0081] Control algorithm implementation: Based on the vehicle dynamics model, design a steering control algorithm, such as gain adjustment or adaptive control, to achieve precise control of the vehicle steering angle;

[0082] System verification and tuning: Verify the effectiveness of the steering control strategy through simulation or real vehicle testing, and perform tuning based on the test results to improve the stability and comfort of steering control.

[0083] The lane change control strategy is designed as follows:

[0084] Environmental perception: Using sensors such as blind spot detection and front obstacle detection to perceive surrounding environment information in real time and provide data support for lane change decisions;

[0085] Lane change decision algorithm: Design a lane change decision algorithm based on rules or machine learning, comprehensively consider the safety and efficiency of lane changes, and determine whether to change lanes;

[0086] Execution control: After deciding to change lanes, a smooth lane change process is achieved by precisely controlling the lateral and longitudinal movement of the vehicle. This includes adjusting parameters such as steering angle and vehicle speed to ensure safety and comfort during the lane change process.

[0087] Preferably, the design of the steering control strategy, by using an electric steering system and a gain adjustment control algorithm to adjust the steering response of the vehicle, specifically includes:

[0088] Integrate gain regulation control algorithms into electric power steering systems;

[0089] Analyze the steering response characteristics of the vehicle's electric steering system under different speeds and driving conditions;

[0090] Evaluate the steering response characteristics of the electric steering system;

[0091] The gain regulation control algorithm adjusts the parameters of the electric steering system based on the evaluation results.

[0092] In this embodiment, the electric power steering system (EPS) can accurately adjust the steering angle and response speed of the vehicle with its adjustability and fast response characteristics, combined with the gain adjustment control algorithm, which ensures that the steering action of the vehicle is more accurate and smooth under different driving conditions, and improves the driving comfort and safety; the gain adjustment control algorithm can adjust the control parameters in real time according to the changes in the vehicle driving environment (such as road conditions, vehicle speed changes, etc.), so that the response characteristics of the electric power steering system match the current driving conditions. This dynamic adaptability helps the vehicle maintain stable steering performance under complex road conditions and improves the overall robustness of the autonomous fleet system; through the electric power steering system integrated with the gain adjustment control algorithm, the autonomous driving vehicle can better realize functions such as lane keeping and automatic steering. The improvement of these driving assistance functions not only improves the intelligence level of the vehicle, but also reduces the burden on the driver and enhances the convenience and safety of driving; through the analysis and evaluation of the steering response characteristics of the electric power steering system and the application of the gain adjustment control algorithm, the steering control strategy can be continuously optimized and improved. This optimization not only improves the accuracy and stability of the vehicle steering control.

[0093] It should be noted that the driving state of the vehicle is monitored in real time, including key parameters such as vehicle speed, acceleration, steering wheel angle, wheel angle, and vehicle yaw rate, and information about the vehicle's surrounding environment, such as road curvature, lane line position, obstacle distance, etc., is collected. Based on real-time data, the driving state of the vehicle is evaluated to determine whether the vehicle is in a stable driving, rapid acceleration, rapid deceleration, turning, etc., and the deviation between the current driving state of the vehicle and the ideal state is analyzed, such as the deviation between the actual driving trajectory and the expected trajectory, the deviation between the actual vehicle speed and the target vehicle speed, etc. According to the state evaluation results, a set of gain calculation rules or algorithms are designed. These rules or algorithms should be able to calculate appropriate gain values ​​according to different driving states. The calculation of the gain value can be based on empirical formulas, table lookup methods, fuzzy logic, neural networks, etc. For example, when making a sharp turn, it may be necessary to increase the steering gain to improve the vehicle's response speed. When driving stably, it may be necessary to reduce the gain to improve driving comfort. The calculated gain value is applied to the control parameters of the electric steering system (EPS), such as adjusting the output torque and steering angle of the motor, and the adjusted vehicle response is monitored in real time to evaluate the effect of the gain adjustment. If the effect is not ideal, further adjustments will be made based on the feedback information.

[0094] Preferably, the vehicle sharp turn and obstacle avoidance collaborative safety control strategy is formulated based on the fleet system model, specifically including:

[0095] According to the fleet system model, the obstacles and road environment information around the vehicle are detected in real time;

[0096] Accurately identify and locate detected obstacles, and determine their position, size, and motion status;

[0097] Based on the detected obstacle information, formulate an obstacle avoidance path planning strategy, including using a path planning algorithm to calculate the best obstacle avoidance path, and using a local avoidance strategy to adjust the vehicle's driving path in real time;

[0098] When vehicles are avoiding obstacles, the information sharing mechanism of the vehicle team is used to achieve collaborative obstacle avoidance among the vehicles.

[0099] When the vehicle makes a sharp turn, the steering angle and speed are adjusted according to the vehicle's dynamic model.

[0100] In this embodiment, the obstacles and road environment information around the vehicle are detected in real time, which can ensure that the vehicle can quickly perceive potential risks in a complex and changeable driving environment, accurately identify and locate the detected obstacles, and not only determine the physical characteristics of the obstacles (such as position and size), but also analyze their motion state, which provides reliable data support for subsequent obstacle avoidance decisions. Based on the detected obstacle information, the path planning algorithm (such as the RRT algorithm) is used to calculate the best obstacle avoidance path, which can optimize the vehicle's driving path and improve traffic efficiency while ensuring safety. The application of local avoidance strategies (such as the dynamic window method) enables the vehicle to respond quickly when facing sudden obstacles, adjust the driving path in real time, ensure vehicle safety, and use the information of the vehicle team. The information sharing mechanism realizes collaborative obstacle avoidance among vehicles. This collaborative mechanism not only enhances the overall response capability of the fleet, but also avoids the chain reaction that may be caused by the obstacle avoidance of a single vehicle, ensuring the stability and safety of the fleet. The collaborative obstacle avoidance algorithm can coordinate the obstacle avoidance actions of each vehicle, avoid collisions within the fleet, and improve the overall operation efficiency and safety of the fleet. When the vehicle makes a sharp turn, the steering angle and speed are adjusted according to the vehicle's dynamic model, which can effectively prevent oversteering or skidding and ensure the stability and safety of the vehicle during sharp turns. The dynamic adjustment capability of the vehicle is also reflected in its adaptability to the overall movement of the fleet. The sharp turn information of the leading vehicle can be obtained by the following vehicle in real time and corresponding adjustments can be made, thereby maintaining the overall stability of the fleet and the consistency of the driving trajectory.

[0101] An autonomous vehicle platoon cooperative control device, comprising:

[0102] An establishment module is used to establish a fleet system model, wherein the fleet system model is used to simulate the behavior of the fleet under different driving conditions;

[0103] A formulation module is used to formulate the longitudinal control strategy of the vehicle queue, the lateral control strategy of the vehicle queue, and the coordinated safety control strategy of the vehicle sharp turn and obstacle avoidance according to the convoy system model;

[0104] A longitudinal control module, used to control the distance and speed between vehicles according to the longitudinal control strategy of the vehicle platoon;

[0105] A lateral control module, used to control the lateral position of the vehicle, the steering of the vehicle and the lane change of the vehicle according to the lateral control strategy of the vehicle platoon;

[0106] The collaborative safety control module is used to control the vehicle's sharp turning steering angle and speed as well as the vehicle's obstacle avoidance action based on the vehicle's sharp turning and obstacle avoidance collaborative safety control strategy.

[0107] A computing device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the autonomous vehicle platoon cooperative control method when executing the computer program.

[0108] A computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the autonomous vehicle platoon cooperative control method described above.

[0109] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions or improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

[0110] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for cooperative control of an autonomous vehicle platoon, characterized in that: Specifically include: Establish a fleet system model to simulate the behavior of the fleet under different driving conditions; Based on the fleet system model, formulate the longitudinal control strategy of the vehicle queue, the lateral control strategy of the vehicle queue, and the coordinated safety control strategy of vehicle sharp turning and obstacle avoidance; Control the distance and speed between vehicles according to the longitudinal control strategy of the vehicle platoon; Control the lateral position of the vehicle, the steering of the vehicle and the lane change of the vehicle according to the lateral control strategy of the vehicle platoon; The vehicle's sharp turn steering angle and speed as well as the vehicle's obstacle avoidance action are controlled based on the vehicle's sharp turn and obstacle avoidance collaborative safety control strategy.

2. The autonomous vehicle platoon cooperative control method according to claim 1, characterized in that: The vehicle queue longitudinal control strategy is formulated based on the fleet system model, specifically including: Based on the fleet system model, a vehicle dynamics model is established; Build a model of the entire fleet based on the vehicle dynamics model; Use linear control theory and nonlinear control theory to analyze the stability of the fleet model and evaluate the response characteristics and stability of the fleet under different driving conditions; Perform bidirectional chord stability analysis on the convoy to evaluate the interaction between the front and rear vehicles in the convoy; The vehicle control law is designed based on the response characteristics and stability of the convoy, as well as the interaction between the front and rear vehicles in the convoy.

3. The autonomous vehicle platoon cooperative control method according to claim 2, characterized in that: The vehicle control law is designed based on the response characteristics and stability of the convoy, as well as the interaction between the front and rear vehicles of the convoy, including: According to the response characteristics and stability of the convoy, as well as the interaction between the front and rear vehicles of the convoy, control objectives are set, including stabilizing the distance between convoys, reducing speed fluctuations, and improving lane keeping accuracy; According to the control target, select the corresponding vehicle control law; The parameters of the vehicle control law are adjusted to design the vehicle control law.

4. The autonomous vehicle platoon cooperative control method according to claim 3, characterized in that: The vehicle platoon lateral control strategy is formulated based on the platoon system model, specifically including: Design a lane keeping control strategy that integrates sensor data from the lane departure warning system and the lane keeping assist system and uses a feedback control strategy to dynamically adjust the vehicle's lateral position to keep the vehicle in the center of the lane; Design steering control strategies to adjust the vehicle's steering response using an electric power steering system and gain adjustment control algorithms; Design lane change control strategies, adjust the vehicle's lane change parameters through lane change detection and lane change decision algorithms, and execute control methods.

5. The autonomous vehicle platoon cooperative control method according to claim 4, characterized in that: The steering control strategy is designed to adjust the steering response of the vehicle by using an electric steering system and a gain adjustment control algorithm, specifically including: Integrate gain regulation control algorithms into electric power steering systems; Analyze the steering response characteristics of the vehicle's electric steering system under different speeds and driving conditions; Evaluate the steering response characteristics of the electric steering system; The gain regulation control algorithm adjusts the parameters of the electric steering system based on the evaluation results.

6. The autonomous vehicle platoon cooperative control method according to claim 5, characterized in that: The above-mentioned vehicle sharp turn and obstacle avoidance collaborative safety control strategy is formulated based on the fleet system model, specifically including: According to the fleet system model, the obstacles and road environment information around the vehicle are detected in real time; Accurately identify and locate detected obstacles, and determine their position, size, and motion status; Based on the detected obstacle information, formulate an obstacle avoidance path planning strategy, including using a path planning algorithm to calculate the best obstacle avoidance path, and using a local avoidance strategy to adjust the vehicle's driving path in real time; When vehicles are avoiding obstacles, the information sharing mechanism of the vehicle team is used to achieve collaborative obstacle avoidance among the vehicles. When the vehicle makes a sharp turn, the steering angle and speed are adjusted according to the vehicle's dynamic model.

7. An autonomous vehicle platoon cooperative control device, characterized in that: include: An establishment module is used to establish a fleet system model, wherein the fleet system model is used to simulate the behavior of the fleet under different driving conditions; A formulation module is used to formulate the longitudinal control strategy of the vehicle queue, the lateral control strategy of the vehicle queue, and the coordinated safety control strategy of the vehicle sharp turn and obstacle avoidance according to the convoy system model; A longitudinal control module, used to control the distance and speed between vehicles according to the longitudinal control strategy of the vehicle platoon; A lateral control module, used to control the lateral position of the vehicle, the steering of the vehicle and the lane change of the vehicle according to the lateral control strategy of the vehicle platoon; The collaborative safety control module is used to control the vehicle's sharp turning steering angle and speed as well as the vehicle's obstacle avoidance action based on the vehicle's sharp turning and obstacle avoidance collaborative safety control strategy.

8. A computing device, characterized in that The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the autonomous vehicle platoon cooperative control method according to any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, implements the steps of the autonomous vehicle platoon cooperative control method according to any one of claims 1 to 6.

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