Commercial vehicle team cruise control method and system, computer equipment and storage medium

Abstract

The invention discloses a commercial vehicle team cruise control method and system, computer equipment and a storage medium, and relates to the technical field of automatic driving and vehicle infrastructure cooperative control, and the method comprises the steps: carrying out the vehicle role interaction of a team vehicle according to a cooperative relation and a vehicle role interaction logic; establishing a surrounding environment potential field model, and describing an interaction relationship between vehicles inside and outside the motorcade; calculating the longitudinal and transverse potential fields of the vehicles of the fleet, and controlling the distance between the vehicles and the safe distance; a model prediction controller for team cruise is constructed by considering the target speed and the environmental influence; and the safety, speed and control constraints of vehicle movement are set, and the driving performance is optimized. The stability of formation control is enhanced, the formation requirements of complex scenes are met, the real-time dynamic control capability is improved, and the formation energy efficiency is optimized.

Description

Technical Field

[0001] The present invention belongs to the technical field of autonomous driving and vehicle-road collaborative control, and specifically relates to a commercial vehicle platoon cruise control method, system, computer equipment and storage medium. Background Art

[0002] With the rapid development of autonomous driving technology, vehicle cooperative control has become a key technology in multi-vehicle platooning. In commercial vehicle cooperative platooning, ensuring vehicle safety, efficient driving, and fuel efficiency optimization are the main research goals. Many researchers have proposed related methods based on different control approaches, but in practical applications, existing technologies still have many shortcomings.

[0003] Li Minglang and Guo Wenjing addressed the uncertainty of autonomous vehicle motion models and employed a model predictive control (MPC) approach within a collaborative adaptive cruise control (CACC) framework to optimize vehicle platooning control. Simulation experiments verified that the proposed controller effectively reduced the maximum acceleration and position following error of the following vehicle. Its adaptability to varying headway distances indicated that smaller headway distances resulted in better tracking. However, the proposed controller did not fully consider issues such as inter-vehicle communication delays and data packet loss. Furthermore, the platooning performance in complex driving scenarios, such as curves or slopes, was not tested. Considering that platooning can achieve basic driving functions, while traditional longitudinal and lateral control methods suffer from potential instability and lateral offset, Jiang Jin proposed a coupled control strategy for vehicle platooning based on model predictive control (MPC) and simultaneous localization and mapping (SLAM). This strategy further addressed the inherent limitations of traditional control methods in terms of stability and reliability, particularly in complex environments and high-speed driving conditions. Yang Dongqi et al. proposed a method combining rolling horizon optimization with distributed convex optimization to solve the fuel-efficient speed planning problem for heterogeneous vehicle platoons. The speed planning algorithm is used to adapt to changing road slopes, and a quadratic spacing strategy based on the expected formation speed is designed to improve flexibility. The distributed algorithm provides the optimal trajectory for all following vehicles based on minimizing the spacing error, and improves the PID controller through the bi-power reaching law. However, the adaptation to heterogeneous vehicle dynamic parameters needs to be considered. Wang et al. constructed a formation transition model based on a finite state machine and proposed a two-layer formation control scheme, in which the upper layer is used for trajectory planning, and the lower layer adopts a control method based on model predictive control (MPC). Trajectory planning is performed by combining a distributed consensus algorithm, and acceleration constraints are added on this basis. Wang et al. proposed a collision avoidance motion planning architecture based on a mixed environment. To solve the collision avoidance problem of CAV fleets during merging and splitting, a speed obstacle algorithm based on potential fields was developed, which can detect collision risks and calculate collision-free speed solutions. An optimization model based on predictive control was proposed for single vehicles and fleets. The effectiveness of the architecture in different obstacle scenarios was verified through numerical experiments. The results show that this method can effectively identify collision risks in the fleet and adjust vehicle speeds to avoid collisions.

[0004] Xie Chunli proposed a dynamic lane-changing trajectory planning method that integrates environmental potential fields. By calculating the potential field function of obstacles and road boundaries, the comprehensive environmental potential field value at a point on the lane is obtained to assess road passability. Combining time-space sampling and trajectory generation with a fifth-order polynomial, the lane-changing trajectory is optimized to improve safety and comfort. Simulation results verify the smoothness of trajectory curvature and speed stability. Xu Weirui constructed a gravitational potential field based on a virtual dynamic target point and multiple repulsive potential fields (including obstacles, road boundaries, and centerlines). Combining model predictive control, he incorporated the environmental potential field into the objective function, optimizing the reliability and accuracy of the vehicle's obstacle avoidance trajectory. This method effectively considers vehicle dynamics and road conditions, improving high-speed obstacle avoidance capabilities. However, it does not fully consider the smoothness and comfort of trajectory planning under high-speed driving conditions, and the algorithm's performance may be limited in high-density traffic or complex environments.

[0005] In summary, although multi-vehicle cooperative platooning technology has made certain progress in control methods and optimization algorithms, the existing technology still has the following shortcomings: First, most methods fail to fully consider practical issues such as inter-vehicle communication delays and data packet loss, which may lead to instability in the platooning control system and affect the coordination and safety between vehicles. Second, the existing technology has poor adaptability in complex driving scenarios (such as curves, slopes, etc.) and cannot ensure the stability and safety of vehicle platooning under variable road conditions. Finally, although some research has begun to focus on the optimization of inter-vehicle coordination, the challenges of adapting heterogeneous vehicle dynamic parameters, high real-time requirements, and limited communication bandwidth have not been fully addressed, resulting in the existing methods still being insufficient in terms of efficiency and real-time performance.

[0006] This paper proposes a new cruise control method for multi-vehicle cooperative platooning of commercial vehicles. By introducing an inter-vehicle communication mechanism, this study effectively overcomes the limitation of traditional methods in that they cannot directly obtain environmental and vehicle status information. In practical applications, since the status and control information of some vehicles in the environment are difficult to directly measure using on-board sensors, the use of inter-vehicle communication can more conveniently obtain accurate data, thus providing a reliable basis for controller design and actual control execution. By fully considering practical issues such as communication delay and data packet loss, and combining it with driving scenarios in complex environments, the control method proposed in this paper can not only improve the safety and stability of vehicle platoons under different driving conditions, but also enhance the overall reliability and comfort of the system. Summary of the Invention

[0007] In view of the above-mentioned problems, the present invention is proposed.

[0008] Therefore, the technical problem addressed by the present invention is that, when driving at close distances, traditional platooning control is susceptible to speed fluctuations and following vehicle delays, resulting in platoon instability. The present invention addresses this problem through a potential field simplification strategy. The potential field model within the platoon focuses on longitudinal following and lateral avoidance, ignoring the influence of distant surrounding vehicles, thereby reducing computational complexity and improving real-time responsiveness. At the same time, by combining the trajectory reference of the lead vehicle with the vehicle dynamics model, vehicles in the platoon can accurately track the lead vehicle's path, reducing lateral and longitudinal offset errors and significantly improving platoon stability.

[0009] Traditional control methods struggle to handle the impact of non-intelligent vehicles in mixed traffic environments. This invention utilizes an improved environmental potential field model, distinguishing between internal and external vehicle interaction logic to effectively address dynamic interactions in complex scenarios. The lead vehicle handles the impact of external vehicle interactions, while follower vehicles focus solely on the platoon's interior, reducing computational overhead and improving collaborative efficiency. Furthermore, the dynamically adjusted safety distance parameter within the potential field model adapts to changes in vehicle speed and platoon status in real time, ensuring vehicle safety in complex traffic conditions.

[0010] In rapidly changing road scenarios, traditional controllers are often unable to adapt due to delays or failures. The present invention improves real-time control through an objective function optimization strategy. A control increment smoothing constraint term is added to the objective function to limit drastic changes in acceleration, deceleration, and steering, thereby improving stability and reliability. In addition, a rolling optimization strategy is used to update the control input in real time, quickly adjust vehicle behavior based on the latest environmental information, and ensure the vehicle's efficient response to dynamic environments. At the same time, by introducing stability constraints, drastic acceleration, deceleration, and steering are limited, further improving the stability and response efficiency of vehicle behavior, and ensuring the stability and safety of vehicle formations in highly dynamic environments.

[0011] To solve the above technical problems, the present invention provides the following technical solutions: a commercial vehicle platoon cruise control method, comprising: performing vehicle role interaction on the platoon vehicles according to the collaborative relationship and vehicle role interaction logic; establishing a surrounding environment potential field model to describe the interaction relationship between vehicles inside and outside the platoon; calculating the longitudinal and lateral potential fields of the platoon vehicles to control the spacing and safety distance between the two vehicles; constructing a model predictive controller for platoon cruise considering the target speed and environmental influences; and setting safety, speed and control constraints for vehicle motion to optimize driving performance.

[0012] As a preferred embodiment of the commercial vehicle platoon cruise control method of the present invention, the vehicle role interaction includes dividing the vehicles in the platoon into a lead vehicle and a follower vehicle according to different cooperative relationships;

[0013] The pilot car uses free cruise control;

[0014] The following vehicle uses platoon cruise control to follow the trajectory of the vehicle in front of the platoon;

[0015] The front vehicle includes the pilot vehicle and other vehicles in the convoy;

[0016] During the control process, the following vehicle controls the safe distance between itself and the vehicle in front, without considering the safe distance maintained by the following vehicle in the rear within the convoy.

[0017] As a preferred embodiment of the commercial vehicle platoon cruise control method of the present invention, the vehicle role interaction further includes, when a leading vehicle in the platoon changes lanes, the following vehicle determines whether there are different environmental interference vehicles in the adjacent vehicles;

[0018] When there are different environmental interference vehicles, you cannot change lanes following the vehicle in front, otherwise a collision will occur;

[0019] When there is no different interfering vehicle in the environment, it is allowed to change lanes by following the vehicle in front.

[0020] As a preferred solution of the commercial vehicle platoon cruise control method of the present invention, wherein: the surrounding environment potential field model includes an external environment potential field model of the platoon and an internal environment potential field model of the platoon;

[0021] The potential field model of the external environment of the convoy is constructed by using free cruise control to construct the potential field model of the vehicles outside the convoy;

[0022] The potential field model of the environment within the convoy does not maintain the wedge shape of the rear end of the potential field shape, and the potential field model of the vehicles within the convoy is constructed through a safe distance.

[0023] As a preferred solution of the commercial vehicle platoon cruise control method of the present invention, wherein: the vehicle potential field model in the platoon includes calculating the longitudinal potential field and the lateral potential field;

[0024] Set a safe distance in front and behind the vehicle outline;

[0025] The longitudinal potential field calculation includes potential field values ​​affected by safety distances between the vehicle side area and the vehicle rear area;

[0026] The lateral potential field uses a Gaussian-like function to calculate the potential field value affected by surrounding vehicles.

[0027] As a preferred embodiment of the commercial vehicle platoon cruise control method of the present invention, the model predictive controller of the platoon cruise includes setting a vehicle kinematic model, an objective function and control constraints;

[0028] The objective function includes comprehensive consideration of the environmental potential field, tracking of the preceding vehicle's predicted trajectory, and vehicle economy and comfort;

[0029] The control constraints include the vehicle's desired speed, acceleration, and front wheel slip angle increment.

[0030] Minimize the deviation under different constraints through the objective function;

[0031] The deviation includes position deviation, speed deviation and safety potential field weight;

[0032] By considering the potential field influence of vehicles outside the convoy and the remaining vehicles in the convoy on the controlled vehicle, the weight of the safety potential field obstacle avoidance function is dynamically adjusted to feedback the potential risks and interference factors of the environment.

[0033] As a preferred embodiment of the commercial vehicle platoon cruise control method of the present invention, the model predictive controller of the platoon cruise further comprises performing target control according to an objective function to achieve a target state;

[0034] The target state includes a reference trajectory and speed setting;

[0035] When a high risk is detected in the surrounding potential field model, the controller increases the weight of the speed deviation and adds an additional penalty term to force the vehicle to adjust its speed or position;

[0036] Minimize the magnitude of changes in vehicle behavior by controlling the increments;

[0037] The influence of the potential field is quantified as an additional disturbance term through the environmental potential field model and added to the dynamic update of the control increment to adjust the acceleration or steering behavior of the vehicle.

[0038] As a preferred embodiment of the commercial vehicle platoon cruise control method of the present invention, the objective function comprises constructing a safety potential field obstacle avoidance function by substituting the vehicle kinematic model into the potential field calculation function, and obtaining the potential field value of the controlled vehicle's position at each prediction step in the prediction time domain;

[0039] Set the distance between vehicles in the platoon and predict the lateral and longitudinal offset errors between the predicted trajectory of the following vehicle and the preceding vehicle in the time domain;

[0040] In the absence of obstacles, the deviation between the speed in the time domain and the target speed is predicted to obtain the reference target speed;

[0041] Reduce the control increment and construct the overall objective function.

[0042] As a preferred solution of the commercial vehicle platoon cruise control method described in the present invention, it includes: the optimizing driving performance includes setting vehicle speed control constraints; setting wheel deflection angle increment constraints.

[0043] Another object of the present invention is to provide a commercial vehicle platoon cruise control system that addresses the shortcomings of traditional vehicle collaboration and improves the safety and efficiency of platoon travel. The vehicle role interaction module determines the role of each vehicle and implements corresponding control strategies. The environmental potential field modeling module establishes potential field models inside and outside the platoon to describe the relationship between vehicles. The potential field calculation module controls the distance between vehicles and the safety distance. The model prediction control module achieves real-time optimization control under the influence of target speed and environment. The safety monitoring module monitors the surrounding environment in real time to determine the safety and risk of lane changes. The control constraint management module formulates and adjusts the safety and control constraints of vehicle movement, thereby optimizing the speed and position control of vehicles in a dynamic environment and ensuring the smooth and safe operation of the platoon.

[0044] To solve the above technical problems, the present invention provides the following technical solutions: a commercial vehicle platoon cruise control system, comprising: a vehicle role interaction module, used to determine the roles of vehicles in the platoon and implement corresponding control strategies; an environmental potential field modeling module, used to establish a potential field model of vehicles inside and outside the platoon, and describe the interaction relationship between vehicles; a potential field calculation module, used to calculate the longitudinal and lateral potential fields of the vehicles, and control the spacing and safety distance between vehicles; a model prediction control module, used to perform real-time optimization control and adjust the vehicle driving status according to the target speed and environmental influence; a safety monitoring module, used to monitor the surrounding environment in real time, and judge the safety and potential risks when changing lanes; a control constraint management module, used to formulate and optimize the safety and handling constraints of vehicle movement, dynamically adjust the control strategy based on environmental changes, and optimize the vehicle's speed and position control.

[0045] A computer device includes a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the steps of the method described in the configuration of a large power plant relay protection information system are implemented.

[0046] A computer-readable storage medium stores a computer program thereon, wherein when the computer program is executed by a processor, the steps of the method described in the configuration of a relay protection information system of a large power plant are implemented.

[0047] The present invention utilizes a potential field simplification strategy to focus on longitudinal following and lateral avoidance within the platoon, minimizing the computational complexity impacted by distant surrounding vehicles and thereby improving the control system's real-time responsiveness. By combining a vehicle dynamics model with a reference to the lead vehicle's trajectory, the system ensures high-precision path tracking for each vehicle, effectively mitigating common speed fluctuations and lateral and longitudinal offsets in platooning control. This reduces the risk of loss of control during operation and significantly improves the overall stability of the platoon.

[0048] Aiming at complex driving scenarios such as curves and ramps, the present invention adopts a dynamically adjusted environmental potential field model and vehicle interaction logic to ensure the safety and stability of vehicle formations in complex environments. By distinguishing the interaction logic inside and outside the formation, the system can effectively deal with the dynamic interference of external non-intelligent vehicles and ensure the continuity and safety of the queue operation. In addition, the dynamically adjusted potential field model can optimize the vehicle safety distance parameters in real time according to changes in road conditions, so that the vehicle can maintain stable operation under different road conditions. Compared with traditional methods, this technology has significant advantages in adapting to dynamic environmental changes and responding to interference from external non-intelligent vehicles, especially showing better vehicle coordination capabilities in changeable road conditions.

[0049] The objective function optimization strategy and rolling optimization mechanism significantly enhance the real-time dynamic response of vehicle platooning. By introducing a control increment smoothing constraint into the objective function, the system effectively limits drastic changes in vehicle acceleration and braking, ensuring smooth and reliable control. The rolling optimization mechanism further enhances the system's adaptability to dynamic environmental changes, enabling vehicles to adjust their behavior in real time based on the latest environmental information.

[0050] The present invention combines the model predictive control method and potential field strategy to design an energy-saving speed planning algorithm, so that each vehicle in the queue can minimize the energy consumed in acceleration and control while maintaining a safe distance, significantly improving the energy utilization rate of the entire formation. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive efforts. Among them:

[0052] Figure 1 An operation identification flowchart of a commercial vehicle platoon cruise control method provided by one embodiment of the present invention.

[0053] Figure 2 A schematic diagram of the longitudinal potential field calculation area for vehicles in a platoon of commercial vehicles according to an embodiment of the present invention is provided.

[0054] Figure 3 A schematic diagram of the longitudinal and lateral potential field calculation areas for vehicles in a commercial vehicle platoon according to a cruise control method for platooning provided by one embodiment of the present invention.

[0055] Figure 4 A schematic diagram of the lane line potential field of a commercial vehicle platoon cruise control method provided in accordance with an embodiment of the present invention.

[0056] Figure 5 A system functional architecture diagram of a commercial vehicle platoon cruise control system provided in accordance with one embodiment of the present invention.

[0057] Figure 6 A schematic diagram of an example of a commercial vehicle platoon cruise control method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0058] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without making creative efforts should fall within the scope of protection of the present invention.

[0059] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0060] Example 1

[0061] Reference Figure 1-Figure 4 , which is the first embodiment of the present invention, provides a commercial vehicle platoon cruise control method, comprising:

[0062] To ensure stable operation of intelligent vehicles in closely spaced platoons in complex road environments, the control performance of the vehicles in the platoon must be largely consistent, including acceleration, steering, and braking performance. This facilitates overall platoon control. Furthermore, due to the close spacing between vehicles, control precision is relatively high. The platooning system proposed in this paper relies on autonomous vehicles that can communicate with each other. Because some status and control information of surrounding vehicles is difficult to measure directly through onboard sensors, communication can more easily obtain accurate data, providing a reliable basis for controller design and specific control execution, thereby enhancing system safety.

[0063] Step S1: According to the collaborative relationship and vehicle role interaction logic, the fleet vehicles are assigned vehicle role interactions;

[0064] Furthermore, vehicle role interaction includes the division of vehicles in a convoy into leader vehicles and follower vehicles based on their collaborative relationships;

[0065] The lead vehicle adopts free cruise control; the basic control logic of the following vehicle avoiding the lead vehicle and the lane-changing vehicle avoiding the straight-going vehicle is still followed.

[0066] The following vehicle adopts team cruise control and follows the trajectory of the vehicle in front of the team (such as the lead vehicle or other vehicles in the team); in the control process, the following vehicle controls the safety distance with the front vehicle and does not consider the safety distance maintained by the following vehicle in the team.

[0067] Specifically, for the following vehicle, the interaction logic has changed greatly compared with the free cruise strategy. First of all, the primary task of the following vehicle is to follow the trajectory of the vehicle in front of the team, which may be the lead vehicle or other vehicles in the team. The trajectory includes the path and speed and still needs to ensure the effective safety distance with the front vehicle. The following vehicle, like the lead vehicle, still does not need to consider the following vehicle in the team.

[0068] Therefore, for the following vehicle in the team, the front environmental vehicle outside the team has little influence on the following vehicle, or in other words, the influence of the front environmental vehicle on the team is mainly borne by the lead vehicle.

[0069] At this time, the influence of other vehicles on the side of the team on the following vehicle still exists, which is specifically manifested as follows: when the vehicle in front of the team changes lanes, the following vehicle judges whether there is other environmental interference vehicle adjacent to the following vehicle; when there is other environmental interference vehicle, the following vehicle cannot change lanes with the vehicle in front of the team, otherwise a collision accident will occur; when there is no other environmental interference vehicle, the following vehicle is allowed to change lanes with the vehicle in front of the team.

[0070] In summary, for the following vehicle, in the normal driving process, it basically does not need to consider avoiding the vehicle in front of the team, but still needs to follow the principle of the lane-changing vehicle avoiding the straight-going vehicle. For the following vehicle, the lane line in the road has little influence on the following vehicle.

[0071] Step S2: establishing a surrounding environment potential field model to describe the interaction relationship of the vehicles in and outside the team;

[0072] The surrounding environment potential field model includes a vehicle potential field model, a road potential field model and a driving direction potential field model.

[0073] Preferably, in the team cruise control, the establishment of the environment potential field model is still to describe the interaction relationship of the surrounding environment and the vehicle in the team. According to the analysis of the interaction logic of the team cruise control, the interaction logic of the vehicle outside the team and the vehicle in the team is the same, and the environment potential field model outside the team and the environment potential field model inside the team are the same.

[0074] The environment potential field model outside the team adopts free cruise control to construct the vehicle potential field model outside the team.

[0075] The potential field model within a platoon, which models the influence of other vehicles on the controlled vehicle, has undergone significant changes. The most significant change is that vehicles in the platoon can maintain a smaller distance between them. The leading vehicle no longer needs to guide the following vehicle to change lanes, and its potential field shape no longer needs to maintain a wedge-shaped rear end.

[0076] For a controlled vehicle, the value of the artificial potential field u at a certain (m, n) in the environment is mn The calculation of also consists of the following three parts:

[0077] u mn =r mn +e mn +s mn

[0078] Among them, the road potential field value at this point is r mn , the potential field value of the environmental vehicle is e mn , the target velocity potential field value of the vehicle is s mn .

[0079] Step S3: Calculate the longitudinal and lateral potential fields of the convoy vehicles to control the vehicle spacing and safety distance;

[0080] Furthermore, similar to vehicles outside the platoon, the potential field model for vehicles within the platoon is divided into calculating the longitudinal potential field and the lateral potential field. A local coordinate system is established with the direction of the surrounding vehicle body as the positive x-axis and the rear end of the vehicle's outer contour as the origin.

[0081] Compared with vehicles outside the platoon, calculating the potential field of the leading vehicle in the platoon is relatively simple, and a safe distance is set before and after the vehicle outline.

[0082] Longitudinal potential field distribution A of vehicles in the convoy car The calculation is as follows Figure 2 As shown, the potential field value affected by the safety distance between the vehicle side area and the vehicle rear area is calculated;

[0083] The environment around the vehicle is divided into Figure 3 The parts shown include the vehicle side area β and the vehicle rear area α. The design calculation point is point p. The calculation rules for the longitudinal potential field distribution are as follows:

[0084]

[0085] Among them, U car is the vehicle potential field constant, representing the maximum value of the ambient vehicle potential field; s p is the safety distance of the vehicle potential field in the convoy.

[0086] The calculation of the environmental vehicle potential field is based on the longitudinal potential field coefficient A carThe lateral calculation rule adopts a Gaussian function, and the overall calculation formula is as follows:

[0087]

[0088] Where D is the equivalent distance of the lateral potential field, and when p is in front of the vehicle, it is the Euclidean distance between the potential field calculation point around the environment vehicle and the edge of the environment vehicle;

[0089] like Figure 3 As shown in , when the calculation point is p3, D = d3; when the calculation point p is on the side of the vehicle p2, D = d2; when the calculation point p is at the rear of the vehicle p1, D = d1; σ v is the convergence coefficient of the vehicle potential field, which determines the lateral influence range of the potential field.

[0090] It should be noted that when modeling a road potential field, the constraints of lane lines on the road must be considered when a controlled vehicle moves along or changes lanes. These constraints can be described by the road potential field. Lane lines on the road can be divided into two types: boundary lane lines, which are lane lines on both sides that vehicles cannot cross; and crossable lane lines in the middle of the road, which vehicles can cross to change lanes according to actual conditions.

[0091] Assuming that the controlled vehicle is driving in the middle lane at this time, the road potential field generated by the two lane lines l2 and l3 in the middle of the road for the potential field calculation point (m, n) is as follows:

[0092]

[0093] Among them, U lane is the lane line potential field coefficient d lane,i is the distance l from a lane line in section A i distance; σ r is the lane line potential field convergence coefficient;

[0094] k(i) is the lane line gain coefficient, and its value is related to the location of the controlled vehicle. For example, when the controlled vehicle is driving in the left lane, the adjacent lane line to the controlled vehicle is l2, so k(2) = 1. Since l3 is separated from the controlled vehicle by a lane, it is believed that during driving, the road constraint on the vehicle is to hope that each vehicle will stay in its own lane and try not to make large-scale lane changes. Therefore, the value of k(3) is greater than k(2). In this paper, k(3) = 2.

[0095] The road boundary lines l1 and l4 are insurmountable, and the influence of the road boundary on the road potential field gradually decreases to zero on the side facing the lane and continues to increase to infinity on the side facing away from the lane.

[0096] A quadratic curve is used to describe this characteristic, and the calculation formula is as follows:

[0097]

[0098] The entire road potential field r mn The calculation formula is the superposition of the middle lane line of the road and the boundary lines on both sides of the road:

[0099]

[0100] In the driving direction potential field modeling, the vehicle is given a tendency to move forward in the lane, which provides a certain degree of assistance for the vehicle to change lanes when an obstacle appears in front.

[0101]

[0102] x r is the longitudinal distance between the potential field point and the controlled vehicle, The velocity potential field coefficient is a negative constant, and ε is a positive constant, which ensures that the potential field value is always positive in the prediction time domain.

[0103] Ignoring the influence of other environmental factors, the potential field function makes the potential field in front of the controlled vehicle lower than the potential field behind it, so that the controlled vehicle maintains a tendency to move forward.

[0104] Step S4: Construct a model predictive controller for platoon cruising considering target speed and environmental influences;

[0105] It should be noted that the vehicle kinematic model, objective function and control constraints are set;

[0106] The objective function includes comprehensive consideration of the environmental potential field, tracking of the preceding vehicle's predicted trajectory, and vehicle economy and comfort;

[0107] The control constraints include the desired vehicle speed, acceleration, and front wheel slip angle increment.

[0108] Minimize the deviation under different constraints through the objective function, including position deviation, speed deviation and safety potential field weight;

[0109] By considering the potential field influence of vehicles outside the convoy and the remaining vehicles in the convoy on the controlled vehicle, the weight of the safety potential field obstacle avoidance function is dynamically adjusted to feedback the potential risks and interference factors of the environment.

[0110] Furthermore, target control is performed according to the objective function to achieve the target state, including reference trajectory and speed setting;

[0111] When a high risk is detected in the surrounding potential field model, the controller increases the weight of the speed deviation and adds an additional penalty term to force the vehicle to adjust its speed or position;

[0112] Minimize the magnitude of changes in vehicle behavior by controlling the increments;

[0113] The influence of the potential field is quantified as an additional disturbance term through the environmental potential field model and added to the dynamic update of the control increment to adjust the acceleration or steering behavior of the vehicle.

[0114] Specifically, in addition to the environmental vehicle potential field, target speed, and economic comfort requirements, the following vehicle control also needs to track the predicted control trajectory of the preceding vehicle. Therefore, the objective function is set as follows:

[0115]

[0116] Among them, U APF (i) is the safety potential field obstacle avoidance function. By substituting the vehicle kinematic model mentioned above into the potential field calculation function, the potential field value of the controlled vehicle position at each prediction step i in the prediction time domain is obtained. The control trajectory of the autonomous vehicle is largely determined by the calculation of the safety potential field function;

[0117] e y (i) and e x (i) The lateral and longitudinal offset errors Sd between the predicted trajectories of the following vehicle and the preceding vehicle in the prediction time domain are respectively the set inter-vehicle distance within the platoon;

[0118] e v (i)=v(i)-v des To predict the deviation between the speed and the target speed in the time domain, the main function of this item is to provide a reference target speed for the vehicle in an obstacle-free situation;

[0119] Δu(k+i|t) is the control increment, which makes the increment minimum considered when optimizing the solution, increases the comfort of the vehicle and improves fuel economy; Q, R, P, W are weight matrices, N p is the prediction time domain, N c To control the time domain, v des A reference speed is set.

[0120] Step S5: Set safety, speed and control constraints of vehicle movement to optimize driving performance.

[0121] Specifically, the vehicle speed control constraint can be set as:

[0122] 0≤v≤1.1*v des

[0123] -4.9m / s 2 ≤a≤1m / s 2

[0124] Among them, v des is the desired speed of the vehicle, and a is the vehicle acceleration.

[0125] Set the wheel deflection increment constraint settings as follows:

[0126] -25°≤δ≤25°

[0127] -9.4° / s≤ω≤9.4° / s

[0128] Where δ is the front wheel deflection angle of the vehicle, and ω is the rotational acceleration of the front wheel deflection angle of the vehicle.

[0129] This paper proposes a new method for team cruise control in a multi-vehicle cooperative platoon of commercial vehicles. By introducing an inter-vehicle communication mechanism, this study effectively overcomes the limitation of traditional methods that cannot directly obtain environmental and vehicle status information. In practical applications, since the status and control information of some vehicles in the environment are difficult to measure directly through on-board sensors, the use of vehicle-to-vehicle communication can more conveniently obtain accurate data, thereby providing a reliable basis for controller design and actual control execution. By fully considering practical issues such as communication delay and data packet loss, and combining driving scenarios in complex environments, the control method proposed in this paper can not only improve the safety and stability of vehicle platoons under different driving conditions, but also enhance the overall reliability and comfort of the system.

[0130] Example 2

[0131] Reference Figure 5 , is an embodiment of the present invention, which provides a commercial vehicle platoon cruise control system, including: a vehicle role interaction module 100, an environmental potential field modeling module 200, a potential field calculation module 300, a model predictive control module 400, a safety monitoring module 500, and a control constraint management module 600.

[0132] The vehicle role interaction module 100 is used to determine the roles of vehicles in the fleet and implement corresponding control strategies.

[0133] Specifically, the roles of each vehicle in the fleet are determined, including the lead vehicle and follower vehicles; a communication mechanism is set up between vehicles to achieve data sharing.

[0134] Input vehicle status (position, speed, acceleration), current environmental information, and fleet structure; identify the lead vehicle and follower vehicle based on vehicle status and collaborative relationships, and assign corresponding control strategies; share role information through the vehicle-to-vehicle communication mechanism to ensure that all vehicles can obtain role status in real time; output the role information of each vehicle in the fleet (lead vehicle, follower vehicle).

[0135] By clearly demarcating the roles of the lead vehicle and follower vehicles, the fleet can collaborate efficiently, ensuring that all vehicles follow the set trajectory and reducing confusion and uncertainty between vehicles. Real-time data sharing between vehicles enhances the level of collaboration within the fleet, ensuring that each vehicle can dynamically adjust based on the current fleet status, thereby improving the overall smoothness and safety of driving.

[0136] The environmental potential field modeling module 200 is used to establish a potential field model of vehicles inside and outside the fleet, and to describe the interaction between the vehicles.

[0137] Specifically, a potential field model of the surrounding environment is established to describe the interaction between internal and external vehicles.

[0138] Input the position, speed, acceleration, and road information of vehicles inside and outside the fleet; construct the vehicle potential field, road potential field, and driving direction potential field, and evaluate the potential field interaction between the fleet and the surrounding vehicles; update the potential field model to reflect environmental changes, such as the dynamic state of nearby vehicles; output the potential field model data, including each potential field value, for reference by subsequent modules.

[0139] By constructing an environmental potential field model, the external traffic conditions can be more comprehensively reflected, helping vehicles to respond quickly in complex road environments and effectively avoid potential collisions. The potential field model's detailed analysis of surrounding vehicle behavior enhances the vehicle's foresight of upcoming traffic changes, improving driving safety and stability.

[0140] The potential field calculation module 300 is used to calculate the longitudinal and lateral potential fields of the vehicle and control the vehicle spacing and safety distance.

[0141] Specifically, the longitudinal and lateral potential fields of the vehicle are calculated to ensure a safe distance between vehicles.

[0142] Input environmental potential field model data (from the environmental potential field modeling module 200) and the preceding vehicle information (speed and position); calculate the longitudinal and lateral potential fields based on the vehicle safety distance requirements, and evaluate the vehicle spacing and relative speed; adjust the potential field model in real time to adapt to dynamic road conditions and changes inside and outside the fleet; output the calculated potential field value and provide it to the model prediction control module and the safety monitoring module.

[0143] By calculating the longitudinal and lateral potential fields, the module achieves precise control of vehicle spacing, optimizes each vehicle's route, and improves the overall driving efficiency of the fleet. Real-time potential field calculation ensures that following vehicles maintain a safe distance from the preceding vehicle, significantly reducing accident risks and enhancing driving safety.

[0144] The model prediction control module 400 is used to perform real-time optimization control and adjust the vehicle driving state according to the target speed and environmental influences.

[0145] Specifically, based on the potential field data, the control input (acceleration and steering) of the vehicle at the target speed is calculated.

[0146] Input potential field values ​​(from the model predictive control module 400), current vehicle state, and target trajectory; set the objective function, including tracking the trajectory and speed of the preceding vehicle as well as vehicle economy and comfort considerations; solve the control instructions through the optimal control algorithm (such as MPC) to minimize the deviation in the objective function; output control instructions (acceleration, steering angle) and send them to the actual control system.

[0147] The model predictive control module can adjust control instructions based on real-time environmental information and the status of the preceding vehicle, enabling the vehicle to flexibly adapt to different driving conditions and improve stability and comfort.

[0148] The safety monitoring module 500 is used to monitor the surrounding environment in real time and determine the safety and potential risks when changing lanes.

[0149] Specifically, it monitors the vehicle's safety status in real time and warns of potential collision risks.

[0150] Input the vehicle's current state (position, speed), potential field calculation results, and external environment information; monitor the vehicle's surroundings and identify vehicles with potential collision risks; when high-risk situations are identified, issue adjustment instructions to the model predictive control module; and output safety warning information for reference by the control system and driver.

[0151] It can instantly identify potential collision risks and respond to them, reducing the chance of accidents and enhancing the driver's sense of security. Especially in dense traffic conditions, passengers can feel significantly more secure. The safety warning information provided enables the driver to take measures when necessary, such as adjusting driving speed or route, thereby improving overall driving safety and response efficiency.

[0152] The control constraint management module 600 is used to formulate and optimize the safety and handling constraints of vehicle motion, dynamically adjust the control strategy based on environmental changes, and optimize the vehicle's speed and position control.

[0153] Specifically, it manages the speed and control constraints of vehicle movement and optimizes driving performance.

[0154] The current state of the vehicle, control instructions (from the model predictive control module 400), and safety monitoring results are input; various control constraints are set according to the vehicle's kinematic model to ensure that the vehicle travels within a safe and comfortable range; control constraints are dynamically adjusted to maintain vehicle stability when the environment changes; and optimized control constraint information is output and fed back to the model predictive control module.

[0155] By dynamically managing speed and handling constraints, the vehicle can maintain optimal performance under various road conditions, reduce energy consumption and wear, and save operating costs for the fleet; this module significantly improves ride comfort by ensuring that the vehicle's acceleration and handling are within a reasonable range, especially on long-distance driving, allowing passengers to experience a smoother driving experience.

[0156] Example 3

[0157] The third embodiment of the present invention is different from the first two embodiments in that:

[0158] Specifically, if the commercial vehicle platoon cruise control method is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), magnetic disk or optical disk, and other media that can store program codes.

[0159] The computer program includes several instructions that can cause a computer device (such as a server, a personal computer or other network device) to perform the following steps:

[0160] The system identifies the current convoy members and divides them into a leader vehicle and a follower vehicle based on their collaborative relationship. The leader vehicle adopts free cruise control, while the follower vehicles adopt team cruise control. The leader vehicle cruises freely according to traffic rules to avoid collision with the vehicle in front. The follower vehicle is controlled based on the dynamic trajectory of the leading vehicle (leader vehicle or other following vehicle) to ensure that it maintains an effective safe distance from the leading vehicle. The follower vehicle does not need to consider the impact of the rear vehicles in the convoy and mainly focuses on the relative position and speed with the vehicle in front.

[0161] A potential field model consisting of three parts is created: vehicle potential field model, road potential field model and driving direction potential field model; the environmental potential field model outside the convoy adopts free cruise control; the setting of the environmental potential field model inside the convoy takes into account the small vehicle spacing between vehicles, allowing subsequent vehicles to travel in coordination under the guidance of the vehicle in front; the potential field value calculation is divided into three parts: road potential field value, environmental vehicle potential field value and vehicle target speed potential field value.

[0162] The x-axis is used as the direction of the surrounding vehicle body, and the rear end of the vehicle's outer contour is set as the origin of the coordinate system. The longitudinal potential field calculation sets a safe distance in front and behind the vehicle contour and calculates the potential field value. The lateral potential field calculation calculates the lateral potential field based on the relative position of the vehicle and adjacent vehicles. The road boundary lane lines and crossable lane lines are identified, and the vehicle is controlled to travel along the lane through the potential field function.

[0163] Define the objective function, comprehensively considering the potential field effect, preceding vehicle tracking accuracy, economy, and comfort. Control constraints should include desired speed, acceleration, and front wheel angle increment. The objective function is designed to minimize position deviation, speed deviation, and safety potential field weights, enhancing driving safety by dynamically adjusting weights. Monitor the environmental safety potential field in real time, and adjust the control strategy when risks are detected.

[0164] Set upper and lower limits for vehicle speed to ensure driving within a safe range; ensure vehicle steering is handled within a reasonable range to reduce passenger discomfort; implement dynamic control, and adjust the applied control increments through real-time data feedback and changes in the forward environment to ensure driving smoothness.

[0165] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0166] More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk cartridge (magnetic devices), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0167] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0168] The computer program of the present invention further supports real-time operations. For example, the program of the present invention can quickly process and analyze data to meet real-time requirements, for example, using efficient algorithms and optimized data structures to accelerate calculations; through multi-threading or asynchronous processing, multiple tasks can be performed simultaneously, thereby improving response speed; after receiving input, the system can return the results to the user or other systems in a timely manner through a fast feedback mechanism; the ability to process real-time data streams, for example, through a stream processing framework (such as Apache Kafka, Apache Flink, etc.) to analyze and display data in real time; using an event-driven model, the system can respond to specific events in a timely manner to ensure the implementation of real-time operations; effectively manage computing resources in the system to ensure that the performance of real-time operations can still be maintained under high load conditions.

[0169] Example 4

[0170] Reference Figure 6 , which is an embodiment of the present invention, provides a commercial vehicle platoon cruise control method. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through experiments.

[0171] During the cruise control process of a multi-vehicle cooperative formation of commercial vehicles, vehicles need to make real-time decisions and adjustments based on their surroundings (including other vehicles in the formation and vehicles in the external environment) to achieve safe and efficient coordinated control. To this end, this paper proposes a dynamic predictive control method based on an environmental vehicle potential field model. Through potential field calculations, the cruise strategy of the formation vehicles is adjusted in real time, thereby optimizing the formation stability and avoiding potential traffic risks. The specific implementation steps are as follows:

[0172] Vehicle driving environment potential field modeling.

[0173] In this embodiment, through the analysis of the interaction logic of the vehicle platoon cruise control, it can be concluded that the interaction logic of the vehicles in the external environment of the platoon to the controlled vehicles in the platoon remains consistent, however, the influence of other vehicles in the platoon on the controlled vehicles has changed significantly. This change is mainly reflected in the fact that the vehicles in the platoon can maintain a smaller vehicle-to-vehicle distance, thereby achieving a tighter formation structure. In addition, when the front vehicle is in the platoon cruise state, it no longer needs to guide the rear vehicle to change lanes to maintain the stability of the queue, and the rear end shape of its potential field model does not need to maintain a wedge feature. This adjustment effectively reduces the complex interaction requirements between vehicles in the platoon, while optimizing the stability and coordination of the platoon operation. The platoon vehicles are regarded as control objects, and based on the environmental vehicle potential field model, the platoon vehicles adjust their control strategies in real time according to the speed, distance and lateral offset of the front and rear vehicles. Assume that the position of the vehicles in the platoon on the road is (x cari ,y cari ), i=1,2,3,4, the position information of the vehicle in front is (x front1, y front1 ), (x front2, y front2 ), the position information of the rear vehicle is (x rear ,y rear ), the relative speed between the convoy and the surrounding vehicles is v r1 v r2 .

[0174] The speed of the vehicles in the controlled convoy is v car =10m / s, the speed of the front and rear vehicles is v front1 =8m / s,v front2 =12m / s,v rear =8m / s.

[0175] The longitudinal distance between the controlled vehicle and the preceding vehicle is K1 = 20m, K2 = 10m, and the longitudinal distance between the controlled vehicle and the following vehicle is K rear =15m, the lateral distance between the front environment vehicle and the controlled vehicle is M1=1.5m, M2=0, the lateral distance between the rear environment vehicle and the controlled vehicle is M rear =0, vehicle reaction time ΔT=1.5s, vehicle safety distance is s min =6m.

[0176] like Figure 6 As shown, according to the formula of the present invention, the longitudinal potential field of the controlled vehicle is as follows:

[0177] S=v r ·ΔT+s min =2*1.5+6=9m

[0178] A car1 =Acar2 =U car ,A carrear =0

[0179] The ambient vehicle potential field is:

[0180]

[0181] The vehicle's lane line potential field is:

[0182]

[0183]

[0184]

[0185]

[0186] The entire road potential field r mn for:

[0187]

[0188] The driving direction potential field is modeled as:

[0189]

[0190] The vehicle driving environment potential field is:

[0191] u mn =r mn +e mn +s mn

[0192] Design of model predictive controller for vehicle platooning cruising strategy

[0193] The platooning cruise controller design fully incorporates the platoon environmental potential field model to achieve efficient and safe platooning cruise control. In the controller design and optimization, the dynamic impacts of vehicles outside the platoon and those within the platoon on the controlled vehicles are treated differently. Specifically:

[0194] In controller design, the objective function J(k) is used to minimize the deviation under different constraints, including position deviation.

[0195] e y(i) , speed deviation e v(i) and safety potential field weight U APF (i) Dynamically adjust U by considering the potential field influence of vehicles outside the convoy and other vehicles inside the convoy on the controlled vehicle. APF The weight of (i) is used to reflect the potential risks and interferences of the surrounding environment in the control strategy.

[0196] In the controller design of the present invention, the reference trajectory and speed are set as part of the target state. If a high risk is detected in the potential field model (such as surrounding vehicles are too close or the formation is disordered), the controller can increase the e v(i) The optimization goal of the control input Δu(k+i|t) is to minimize the magnitude of changes in vehicle behavior. By introducing the environmental potential field model, the impact of the potential field can be quantified as an additional perturbation term and added to the dynamic update of Δu(k+i|t), thereby adjusting the vehicle's acceleration or steering behavior. For example, when the potential field model detects a high level of dangerous field intensity in a certain direction, the penalty weight for acceleration in that direction can be dynamically increased to guide vehicles to avoid or adjust their formation.

[0197] While the environmental potential field model provides individual dynamic constraints for each vehicle, platoon cruising requires a focus on overall coordination. Formation constraints (e.g., formation distance deviation or overall coordination metrics) can be incorporated into the objective function, enabling inter-vehicle coordinated control through dynamic adjustment of weights Q, R, and P. For example, a metric for formation compactness can be quantified using the relative position potential field between adjacent vehicles, adding relevant constraints to the control optimization.

[0198] During platooning, the fleet achieves efficient coordination and safe cruising between vehicles by combining an environmental potential field model with a platooning cruise controller. The potential field model quantifies the dynamic impact of the fleet's external environment and internal coordination relationships, providing the controller with precise environmental perception and constraint information. The platooning cruise controller, using data input from the potential field model, dynamically adjusts the reference trajectory, speed, and control strategy of the fleet members, thereby optimizing the fleet's formation, reducing energy consumption, and improving safety. Through real-time updates of the potential field model and adaptive optimization of the controller, the fleet can achieve close collaboration in complex traffic environments, ensuring the stability and efficiency of platooning.

[0199] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A commercial vehicle platoon cruise control method, characterized by: include, According to the collaborative relationship and vehicle role interaction logic, the fleet vehicles are assigned vehicle role interactions; Establish a potential field model of the surrounding environment to describe the interaction between vehicles inside and outside the fleet; Calculate the longitudinal and lateral potential fields of convoy vehicles to control the distance between vehicles and the safety distance; Construct a model predictive controller for team cruising considering target speed and environmental impact; Set safety, speed, and handling constraints on vehicle motion to optimize driving performance.

2. The commercial vehicle platoon cruise control method according to claim 1, characterized in that: The vehicle role interaction includes: According to different cooperative relationships, vehicles in the convoy are divided into leading vehicles and following vehicles; The pilot car uses free cruise control; The following vehicle uses platoon cruise control to follow the trajectory of the vehicle in front of the platoon; The front vehicle includes the pilot vehicle and other vehicles in the convoy; During the control process, the following vehicle controls the safe distance between itself and the vehicle in front, without considering the safe distance maintained by the following vehicle in the rear within the convoy.

3. A commercial vehicle platoon cruise control method according to claim 1 or 2, characterized in that: The vehicle role interaction further includes: When the vehicle in front of the convoy changes lanes, the following vehicle determines whether there are different environmental interference vehicles in the adjacent vehicles; When there are different environmental interference vehicles, you cannot change lanes following the vehicle in front, otherwise a collision will occur; When there is no different interfering vehicle in the environment, it is allowed to change lanes by following the vehicle in front.

4. The commercial vehicle platoon cruise control method according to claim 3, characterized in that: The surrounding environment potential field model includes: The potential field model of the external environment of the convoy and the potential field model of the internal environment of the convoy; The potential field model of the external environment of the convoy is constructed by using free cruise control to construct the potential field model of the vehicles outside the convoy; The potential field model of the environment within the convoy does not maintain the wedge shape of the rear end of the potential field shape, and the potential field model of the vehicles within the convoy is constructed through a safe distance.

5. A commercial vehicle platoon cruise control method according to claim 1 or 4, characterized in that: The vehicle potential field model in the convoy includes: Calculate longitudinal and transverse potential fields; Set a safe distance in front and behind the vehicle outline; The longitudinal potential field calculation includes potential field values ​​affected by safety distances between the vehicle side area and the vehicle rear area; The lateral potential field uses a Gaussian-like function to calculate the potential field value affected by surrounding vehicles.

6. A commercial vehicle platoon cruise control method according to claim 5, characterized in that: The model predictive controller for the team cruise includes: Set up the vehicle kinematic model, objective function and control constraints; The objective function includes comprehensive consideration of the environmental potential field, tracking of the preceding vehicle's predicted trajectory, and vehicle economy and comfort; The control constraints include the vehicle's desired speed, acceleration, and front wheel slip angle increment. Minimize the deviation under different constraints through the objective function; The deviation includes position deviation, speed deviation and safety potential field weight; By considering the potential field influence of vehicles outside the convoy and the remaining vehicles in the convoy on the controlled vehicle, the weight of the safety potential field obstacle avoidance function is dynamically adjusted to feedback the potential risks and interference factors of the environment.

7. A commercial vehicle platoon cruise control method according to claim 6, characterized in that: The model predictive controller of the team cruise also includes: Perform target control according to the target function to achieve the target state; The target state includes a reference trajectory and speed setting; When a high risk is detected in the surrounding potential field model, the controller increases the weight of the speed deviation and adds an additional penalty term to force the vehicle to adjust its speed or position; Minimize the magnitude of changes in vehicle behavior by controlling the increments; The influence of the potential field is quantified as an additional disturbance term through the environmental potential field model and added to the dynamic update of the control increment to adjust the acceleration or steering behavior of the vehicle.

8. A commercial vehicle platoon cruise control method according to claim 1 or 7, characterized in that: The objective function includes: By substituting the vehicle kinematics model into the potential field calculation function, a safety potential field obstacle avoidance function is constructed to obtain the potential field value of the controlled vehicle's position at each prediction step in the prediction time domain. Set the distance between vehicles in the platoon and predict the lateral and longitudinal offset errors between the predicted trajectory of the following vehicle and the preceding vehicle in the time domain; In the absence of obstacles, the deviation between the speed in the time domain and the target speed is predicted to obtain the reference target speed; Reduce the control increment and construct the overall objective function.

9. A commercial vehicle platoon cruise control method according to claim 1, 2, 4, 6 or 7, characterized in that: Said, including said optimizing driving performance, include, Set vehicle speed control constraints; Sets the wheel deflection angle increment constraint.

10. A commercial vehicle platoon cruise control system, characterized in that: include, A vehicle role interaction module (100) for determining the roles of vehicles in a fleet and implementing corresponding control strategies; An environmental potential field modeling module (200) is used to establish a potential field model of vehicles inside and outside the fleet, and describe the interaction between the vehicles; A potential field calculation module (300) is used to calculate the longitudinal and lateral potential fields of the vehicle and control the vehicle spacing and safety distance; A model prediction control module (400) is used to perform real-time optimization control and adjust the vehicle driving state according to the target speed and environmental influence; A safety monitoring module (500) is used to monitor the surrounding environment in real time and determine the safety and potential risks when changing lanes; The control constraint management module (600) is used to formulate and optimize the safety and control constraints of vehicle movement, dynamically adjust the control strategy based on environmental changes, and optimize the speed and position control of the vehicle.

11. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the commercial vehicle platoon cruise control method are implemented.

12. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the commercial vehicle platoon cruise control method are implemented.

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