A Collision Warning Mechanism-Based Cooperative Consistency Control Method for Heterogeneous Vehicle Groups

Abstract

This invention relates to a heterogeneous vehicle group cooperative consistency control method based on a collision warning mechanism, belonging to the field of intelligent transportation application technology. It includes: setting up a typical single-lane heterogeneous vehicle group traffic scenario on an urban road; acquiring and analyzing vehicle state information data through roadside sensing units, and constructing a four-level collision warning mechanism based on dual-indicator fusion, combining vehicle distance and collision time; establishing a longitudinal kinematic model of vehicles based on the kinematic characteristics of heterogeneous vehicles, combined with the vehicle information topology, and simultaneously constructing a traditional human-driven vehicle control model and a connected autonomous vehicle control model based on collision warning levels; and establishing a heterogeneous vehicle group cooperative consistency control method based on vehicle speed and spacing within the heterogeneous vehicle group. This invention enables heterogeneous vehicle groups based on a collision warning mechanism to quickly achieve state consistency and maintain stable driving.

Description

Technical Field

[0001] This invention belongs to the field of intelligent transportation technology and relates to a method for cooperative consistency control of heterogeneous vehicle groups based on a collision warning mechanism. Background Technology

[0002] Against the backdrop of rapid global economic development and urbanization, the number of motor vehicles continues to rise, highlighting the growing contradiction between urban road resource supply and traffic demand. With the continuous development of vehicle-to-everything (V2X) technology, connected vehicle infrastructure, and connected autonomous vehicles, a hybrid traffic pattern of connected autonomous vehicles and traditional human-driven vehicles will persist for decades. In this scenario, cooperative intersection collision warning, through real-time information exchange between connected vehicles, predicts risks and achieves collaborative collision avoidance, effectively reducing traffic accidents. While it has a significant positive impact on vehicle collaborative control, this is limited to pure connected autonomous vehicle scenarios, where the entire vehicle group must possess a high degree of connectivity and automation. Traditional human-driven vehicles, lacking communication capabilities, still rely on human drivers for control. In this scenario, heterogeneous traffic participants exhibit significant differences in the dimensions and scope of information acquisition and vehicle dynamics characteristics, and there is also a considerable gap in control decision-making levels between connected autonomous vehicles and human-driven vehicles. Therefore, during operation, connected autonomous vehicles, with their leading and collaborative capabilities, will gradually integrate human-driven vehicles, ultimately forming a heterogeneous vehicle horde.

[0003] In recent years, the deep integration of edge computing and cloud computing technologies has provided a new technological paradigm for intelligent transportation systems. In various complex traffic scenarios, relying on intelligent roadside units, in-vehicle intelligent terminals, and edge computing nodes, a closed loop of perception, decision-making, and control has been formed. Through a technical path of layered perception fusion, distributed decision-making, and hierarchical control execution, full-chain coordination from global traffic situation awareness to precise local vehicle control has been achieved, ensuring the stable passage of heterogeneous vehicle groups. Furthermore, connected autonomous vehicles and traditional human-driven vehicles differ substantially at the control decision-making level. These heterogeneous characteristics not only exacerbate the nonlinear coupling complexity of coordinated control of heterogeneous vehicle groups in single-lane scenarios but also pose core challenges to the consistency, stability, and driving efficiency of vehicle group operation. Therefore, research on control mechanisms for heterogeneous vehicle groups in single-lane environments has significant practical implications. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a heterogeneous vehicle group cooperative consistency control method based on a collision warning mechanism, which solves the problems of heterogeneous vehicle group consistency control and consistency recovery after disturbance under sudden events.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A method for cooperative consistency control of heterogeneous vehicle groups based on a collision warning mechanism, the method comprising: Set up a single-lane traffic flow scenario on an urban road with mixed traffic of different types of vehicles, in which connected autonomous vehicles and traditional human-driven vehicles are mixed in a random proportion and drive in a platoon. Roadside sensing units are deployed on both sides of the road to collect vehicle status information. Combined with vehicle distance and collision time, a collision warning mechanism based on dual-indicator fusion is constructed. A longitudinal kinematic model of the vehicle is established based on the vehicle's kinematic characteristics, and a traditional human-vehicle cooperative control model and a connected autonomous vehicle cooperative control model based on collision warning levels are constructed by combining the vehicle information topology. A collaborative consistency control method for heterogeneous vehicle groups is established based on vehicle speed and spacing to achieve consistency control for the entire heterogeneous vehicle group.

[0006] Furthermore, a collision warning mechanism based on dual-indicator fusion, combining inter-vehicle distance and collision time, is constructed, including setting the inter-vehicle distance as the distance between vehicles in the group. With the car in front Distance between:

[0007] In the formula, For workshop distance, For vehicles At any moment Location, The length of the vehicle; Collision time Represented as:

[0008] In the formula, For vehicles and vehicles relative velocity, For vehicles and vehicles The actual distance between them; At the time of collision Based on this, set a collision time based on a safe distance. :

[0009]

[0010] In the formula, This indicates the time required for the entire braking process; The time required from the discovery of an emergency to taking action; For time step; Set workshop safety distance threshold and collision time threshold based on safe distance :

[0011]

[0012] In the formula, , , , These correspond to the workshop distance thresholds for the respective risk levels, and ; , , , These correspond to the respective collision time thresholds based on safe distance for each risk level, and ; According to the threshold and Configure the four-level collision warning mechanism shown in the table below:

[0013] Among them, risk level When in a state without warning Moreover, the Level IV warning level is the most urgent.

[0014] Furthermore, a longitudinal kinematic model of the vehicle is established based on its kinematic characteristics, and is expressed as follows:

[0015] In the formula, , , They represent the first The vehicle is Position, velocity, and acceleration information at any given moment; Indicates the first The car is The control input at time represents the first time in the heterogeneous vehicle group. The acceleration of a vehicle is its jerk, which is the rate of change of acceleration.

[0016] For traditional human drivers, a segmented car-following model based on warning levels is used to characterize the driving behavior of traditional human drivers under the action of intelligent warning systems. In the absence of warnings, an improved linear car-following model is used to control acceleration, while in the presence of warnings, a restricted response model based on safety levels is used to control acceleration. For connected autonomous vehicles, a collaborative control framework that integrates the interaction of vehicle potential fields, communication latency, and safety warning thresholds is used to adjust the vehicle's control input.

[0017] Furthermore, the segmented car-following model based on early warning levels includes: setting the desired distance between drivers and vehicles. :

[0018] In the formula, Indicates the total safe time. This indicates the driver's reaction time under warning conditions. Indicates the time step. Indicates the length of the vehicle.

[0019] In the absence of warnings, control is achieved using the following linear car-following model:

[0020] In the formula, Driving for traditional people Control input; , , Traditional human driving The current position, speed, and acceleration of the vehicle. For the desired vehicle speed; They represent the vehicles in front. exist The acceleration is constant, with the vehicle in front either a connected autonomous vehicle or a traditional human-driven vehicle; To assess the driver's sensitivity to deviations in distance between the vehicle and the vehicle, The driver's sensitivity to the deviation between their own speed and the desired speed. This refers to the driver's sensitivity to acceleration deviations of heterogeneous vehicles.

[0021] In the absence of warning, the traditional acceleration update rule for human drivers is as follows:

[0022] In the formula, The maximum allowable acceleration for heterogeneous vehicles; In the absence of warning, the traditional speed update rule for human drivers is as follows:

[0023] In the formula, The maximum speed allowed for heterogeneous vehicles; In the absence of warning, the traditional rule for updating the location of a person driving a vehicle is as follows:

[0024] Under warning conditions, the traditional human-driven vehicle control model based on the warning level is as follows:

[0025]

[0026]

[0027] The model under early warning status is based on the risk level. Based on; for The warning factor at any given time is a normalized representation of the warning risk indicator, representing the intensity of the vehicle's subjective perception of risk. for The level of warning at any given moment; They represent the vehicles in front. exist The location at any given moment, and whether the vehicle in front is a connected autonomous vehicle or a traditional human-driven vehicle; For traditional dynamic safety distances between drivers and vehicles, under warning conditions, the higher the warning level, the greater the expected safety distance for drivers and vehicles. While decelerating It also increases, eventually achieving consistency in the state of heterogeneous vehicles; For the dynamic expected speed, under warning conditions, the higher the warning level, the lower the expected speed for a traditional human driver. While decelerating This also reduces the overall size of the vehicle, ultimately achieving consistency in the state of heterogeneous vehicles. , These are the gain coefficients of the position and speed of a traditional driver on the warning level.

[0028] Under warning conditions, the acceleration update rule is as follows:

[0029] Under alert status, the speed update rule is as follows:

[0030] Under alert status, the location update rule is as follows:

[0031] Furthermore, for connected autonomous vehicles, a collaborative control framework that integrates the effects of the workshop safety potential field, communication latency, and safety warning thresholds is adopted to adjust the vehicle's control input, while also considering dynamic control gains based on warning levels.

[0032] First, establish a workshop safety potential field model:

[0033] In the formula, Indicates the actual mass of the vehicle; The higher-order power terms representing velocity reflect the nonlinear amplification effect of velocity on inertia; is the scaling factor for higher-order power terms; to avoid the safety potential field value being 0 due to heterogeneous vehicle parking, Set as a constant; This represents the potential field gain coefficient; For speed-related parameters; For vehicles At any moment speed; For vehicles At any moment The location.

[0034] Based on a safety potential field model and a vehicle motion model, the system shares and analyzes the surrounding vehicle state information of the connected autonomous vehicle through a roadside perception unit, and considers dynamic control gain based on a collision warning mechanism and time delay in vehicle communication. Based on the early warning threshold mechanism of the safety potential field, a controller for connected autonomous vehicles is constructed. Setting the desired vehicle spacing for connected autonomous vehicles :

[0035] In the formula, Indicates the total safe time. This indicates the response time of a connected autonomous vehicle under warning conditions. Indicates the time step. Indicates the length of the vehicle.

[0036] In the absence of warnings, the controller of the connected autonomous vehicle is:

[0037] In the formula, For connected autonomous vehicles At any moment The integrated control input, , , and Connected autonomous vehicles exist The position, velocity, acceleration, and potential field value of the vehicle in the heterogeneous vehicle group at all times; , and They represent the vehicles in front. exist The location, speed, and acceleration at any given moment; whether the vehicle in front is a connected autonomous vehicle or a traditional human-driven vehicle. , and These represent the position, velocity, and acceleration of the lead vehicle in a heterogeneous vehicle group, respectively. For communication delay, For vehicle length, This is the standard safety potential field value; , , and These are the gain coefficients for the position, speed, acceleration, and safety potential field of the connected autonomous vehicle, respectively.

[0038] In the warning state, the controller of the connected autonomous vehicle is:

[0039]

[0040]

[0041]

[0042]

[0043] In the formula, It serves as an early warning factor. , , and These are the gain coefficients of the connected autonomous vehicle based on the warning level, including position, speed, acceleration, and safety potential field. This refers to the timeframe from the triggering of a warning to its termination for the connected autonomous vehicle. As the warning level increases, the corresponding warning factor also increases. A decay function is used to reduce the overall gain coefficient as the warning level increases, resulting in a smoother control input and consistent vehicle state. When there is no warning, ,but This corresponds to the above-mentioned control method for connected automated vehicles in the absence of warning.

[0044] Furthermore, a heterogeneous vehicle group collaborative consistency control method is established based on the vehicle speed and inter-vehicle distance of the heterogeneous vehicle group, which constrains the speed, inter-vehicle distance, and safety potential field of the entire heterogeneous vehicle group.

[0045] Set vehicle spacing constraints; when a heterogeneous vehicle group reaches a stable state, if the vehicles... For traditional drivers, the distance between them and the vehicle in front tends to be close to... If the vehicle For connected autonomous vehicles, the distance to the vehicle in front tends to be close to... The vehicle in front can be a traditional human-driven vehicle or a connected autonomous vehicle. The vehicle spacing constraint is expressed as:

[0046]

[0047] A speed difference constraint is set so that when the heterogeneous vehicle group reaches a steady state, the speed difference between adjacent vehicles is zero, as shown in the following formula:

[0048] Setting up standard safety potential field constraints, firstly through the desired vehicle speed... and expected vehicle spacing Calculate the standard safety potential field :

[0049] When the entire heterogeneous vehicle group reaches uniformity, the difference between the safety potential field of any vehicle and the standard safety potential field approaches zero, as shown in the following equation:

[0050] In the formula, Indicates vehicle The safety potential field value in a heterogeneous vehicle group, vehicle For connected autonomous vehicles or traditional human-driven vehicles.

[0051] The beneficial effects of this invention are as follows: (1) This invention takes into account the impact of two indicators on the warning level. Based on the two indicators of the distance between the current vehicle and the vehicle in front and the collision time based on the safe distance, a four-level collision warning mechanism is designed. By optimizing the collision warning level determination rules, the reliability of vehicle collision warning is improved.

[0052] (2) The present invention takes into account the influence of the expected vehicle spacing and expected vehicle speed of dynamic human driving based on the collision warning level. Based on the impact of collision warning on the traditional human driving driver, the control model of traditional human driving is improved; it ensures that safety control and consistency control are achieved while the collision warning system participates in the control, and at the same time meets the driver's state changes in response to the collision warning.

[0053] (3) This invention takes into account the influence of the workshop safety potential field and the dynamic control gain period of the connected automatic vehicle based on the collision warning level. Based on the information such as the distance between the current vehicle and the vehicle in front, speed and vehicle equivalent mass, a workshop safety potential field model is established. Based on the workshop safety potential field model, workshop communication delay and safety warning threshold, a connected automatic vehicle controller is constructed to adjust the control input of the connected automatic vehicle, which can effectively ensure the safety of heterogeneous vehicle groups and the stability of vehicle driving.

[0054] (4) The improved heterogeneous vehicle group coordination consistency control method of the present invention through the collision warning mechanism can enable the heterogeneous vehicle group to quickly restore the consistency state of the entire vehicle group after experiencing an emergency, thereby improving the anti-interference ability and coordination safety of the heterogeneous vehicle group.

[0055] Other advantages, objectives, and features of the invention will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination, or may be learned from practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0056] To make the objectives, technical solutions, and advantages of the present invention clearer, the preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings, wherein: Figure 1 This is a schematic flowchart of a heterogeneous vehicle group cooperative consistency control method based on a collision warning mechanism according to an embodiment of the present invention. Figure 2 This is a flowchart illustrating the process of determining the early warning level based on two indicators. Figure 3 This is a typical image of a heterogeneous traffic scenario on a single-lane urban road. Figure 4 This is a schematic diagram for determining the standard safety potential field in the workshop. Detailed Implementation

[0057] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0058] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures. They should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0059] In the accompanying drawings of the embodiments of the present invention, the same or similar reference numerals correspond to the same or similar components. In the description of the present invention, it should be understood that if terms such as "upper," "lower," "left," "right," "front," and "rear" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the drawings, they are only for the convenience of describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the terms used to describe positional relationships in the drawings are only for illustrative purposes and should not be construed as limiting the present invention. For those skilled in the art, the specific meaning of the above terms can be understood according to the specific circumstances.

[0060] See Figures 1-4 This embodiment provides a method for cooperative consistency control of heterogeneous vehicle groups based on a collision warning mechanism, which includes the following steps: I. Setting up a typical single-lane heterogeneous vehicle group traffic scenario on urban roads Set up a single-lane traffic flow scenario on an urban road containing heterogeneous vehicles—connected autonomous vehicles and traditional human-driven vehicles—in which connected autonomous vehicles and traditional human-driven vehicles travel in a randomized platooning ratio.

[0061] By deploying roadside sensing units on both sides of road infrastructure, connected autonomous vehicles receive data from these units and communicate with surrounding vehicles through integrated onboard sensing modules. This allows them to continuously and in real-time obtain precise status information about multiple vehicles ahead and those immediately behind them in their driving direction. In contrast, traditional human drivers rely on visual observation, experience-based judgment, and onboard sensors for information acquisition, which can only provide subjective and delayed perception and assessment of the status of vehicles immediately ahead and the surrounding local traffic environment.

[0062] Meanwhile, two unexpected events were set up on the road to simulate the process of heterogeneous vehicle groups recovering consistency after being disturbed, demonstrating their anti-interference ability and collaborative safety.

[0063] II. By utilizing roadside sensing units to collect vehicle status information from heterogeneous vehicle groups, a four-level collision warning mechanism based on dual-indicator fusion is constructed. The specific steps are as follows: 1. Utilize workshop distance and collision time with increased distance ETTC The collision warning level determination criteria are set as follows: (1) Workshop distance The primary indicator for judging the safety of two vehicles is as follows:

[0064] In the formula, For vehicle spacing, For vehicles At any moment Location The length of the vehicle; (2) Collision time This refers to the time required for the vehicle to reach the expected conflict point from its current location. Based on the vehicle's kinematics model, it is expressed as:

[0065] In the formula, For the current vehicle and adjacent vehicles relative velocity, For the current vehicle At any moment speed, For vehicles and vehicles The actual distance between them.

[0066] (3) Collision time based on safe distance

[0067] To make collision detection results more stable and reduce false alarms, a collision time based on a safe distance is used. ETTC For both autonomous connected vehicles and traditional human drivers on the road, if the vehicle in front brakes or decelerates suddenly, the vehicle must ensure that it can avoid a rear-end collision after a series of maneuvers. Collision time is based on safe distance. ETTC By supplementing key dimensions such as vehicle dynamic characteristics and environmental interference factors, the traditional By relying solely on distance and relative speed and ignoring the limitations of complex operating conditions, this approach more accurately quantifies the collision risk between vehicles and obstacles, ultimately providing a basis for decision-making in collision warning active safety systems.

[0068] ETTC The calculation is expressed as:

[0069]

[0070] In the formula, This includes reaction time and braking time, representing the total time required for the entire braking process. The time required from the discovery of an emergency to taking action; For time step.

[0071] 2. For example Figure 2 As shown, vehicle status information is collected through roadside sensing units to construct a four-level collision warning mechanism that integrates two indicators: safe distance between vehicles and collision time based on enhanced distance.

[0072] Set workshop safety distance threshold and collision time threshold based on safe distance ,in , , , The workshop distance thresholds corresponding to the respective warning levels, and ; , , , The collision time thresholds based on safe distance correspond to the respective warning levels, and This is used to determine the final warning level, with level four being the most urgent.

[0073]

[0074]

[0075] According to the threshold and The four-level collision warning mechanism shown in Table 1 is configured, where the risk level is... Additionally, when in a state without warning : Table 1

[0076] Third, a longitudinal kinematic model of the vehicle is established based on the vehicle's kinematic characteristics, and a traditional human-vehicle cooperative control model and a connected autonomous vehicle cooperative control model based on collision warning level are constructed by combining the vehicle information topology.

[0077] 1. To achieve coordinated control and collision warning of vehicles in a mixed vehicle group, a unified vehicle kinematics description model is established.

[0078] In a single-lane straight-ahead scenario, a classic point-mass motion model is used to model the motion states of both connected autonomous vehicles and traditional manually driven vehicles. Within this framework, the... The kinematic behavior of the vehicle is expressed as follows:

[0079] In the formula, , , They represent the first The vehicle is Position, velocity, and acceleration information at any given moment; Indicates the first The car is The control input at time represents the first time in the heterogeneous vehicle group. The vehicle's jerk, or rate of change of acceleration, reflects its dynamic response characteristics and provides a unified mathematical basis for subsequent design of cooperative control algorithms and early warning strategies.

[0080] 2. To accurately depict the driving behavior of traditional drivers under the action of intelligent early warning systems, a segmented car-following model based on early warning levels is proposed.

[0081] Based on whether the system issues a warning signal, the model divides the vehicle acceleration decision into two modes: an improved linear car-following model is used in the absence of a warning, while a restricted response model based on safety level is used in the presence of a warning.

[0082] Specifically, the acceleration control rules for traditional human driving in a single-lane scenario are designed as follows: (1) No warning state: Set the desired vehicle spacing for drivers :

[0083] In the formula, Indicates the total safe time. This indicates the driver's reaction time under warning conditions. Indicates the time step. Indicates the length of the vehicle.

[0084] In the absence of warnings, control is achieved using the following linear car-following model:

[0085] In the formula, Driving for traditional people Control input; , , Traditional human driving The current position, speed, and acceleration of the vehicle. For the desired vehicle speed; Indicates the vehicle in front exist The acceleration is constant, with the vehicle in front either a connected autonomous vehicle or a traditional human-driven vehicle; To assess the driver's sensitivity to deviations in distance between the vehicle and the vehicle, The driver's sensitivity to the deviation between their own speed and the desired speed. This refers to the driver's sensitivity to acceleration deviations of heterogeneous vehicles.

[0086] In the absence of warning, the traditional acceleration update rule for human drivers is as follows:

[0087] In the formula, The maximum allowable acceleration for heterogeneous vehicles; In the absence of warning, the traditional speed update rule for human drivers is as follows:

[0088] In the formula, The maximum speed allowed for heterogeneous vehicles; In the absence of warning, the traditional rule for updating the location of a person driving a vehicle is as follows:

[0089] (2) Warning status: Under warning conditions, the traditional human-driven vehicle control model based on the warning level is as follows:

[0090]

[0091]

[0092] In the formula, the model under the early warning state refers to the risk level ahead. Based on; for The warning factor at any given time is a normalized representation of the warning risk indicator, representing the intensity of the vehicle's subjective perception of risk. for The level of warning at any time, The number of warning levels in this invention is 4. They represent the vehicles in front. exist The location at any given moment, and whether the vehicle in front is a connected autonomous vehicle or a traditional human-driven vehicle; For traditional dynamic safety distances between drivers and vehicles, under warning conditions, the higher the warning level, the greater the expected safety distance for drivers and vehicles. While decelerating It also increases, eventually achieving consistency in the state of heterogeneous vehicles; To dynamically predict the desired vehicle speed, under warning conditions, the higher the warning level, the lower the expected speed for a traditional human driver. While decelerating This also reduces the overall size, ultimately achieving consistency in the state of heterogeneous vehicles. , These are the gain coefficients of the position and speed of a traditional driver on the warning level.

[0093] Under warning conditions, the acceleration update rule is as follows:

[0094] Under alert status, the speed update rule is as follows:

[0095] Under alert status, the location update rule is as follows:

[0096] 3. Connected Vehicle Cooperative Control Model To ensure the driving safety and platooning coordination of connected autonomous vehicles in mixed-traffic environments, a collaborative control framework integrating inter-vehicle potential field effects, communication delays, and safety warning thresholds is proposed. This framework describes the interaction between vehicles by constructing a safety potential energy field and explicitly considers delay effects and safety threshold constraints in the control law to achieve distributed regulation of the motion state of connected autonomous vehicles.

[0097] (1) When the actual distance between connected autonomous vehicles Approximately the desired vehicle spacing At that time, the virtual reaction force generated by the safety potential field increases sharply with the increase of distance, forcing the vehicle in front to decelerate, thus ensuring the safety of vehicles in a heterogeneous vehicle group. The specific safety potential field model of the workshop is as follows: The safety potential field strength is the current vehicle With the car in front The relative position function, the anisotropic safety potential function, and the equivalent mass are expressed as follows:

[0098]

[0099]

[0100] In the formula, Indicates equivalent mass. Indicates the actual mass of the vehicle; This represents the desired speed of a heterogeneous vehicle group. The vehicle speed is multiplied by a coefficient. Scaling of higher-order power terms prevents potentially distorted speed values. This represents the potential field gain coefficient. The direction coefficient of the safety potential field describes the direction of the safety potential field. The higher-order power terms representing velocity reflect the nonlinear amplification effect of velocity on inertia. Add a constant term. This is to ensure that the correction factor remains positive when the speed of the vehicle in front approaches zero. For vehicle distance attenuation, Indicates vehicle With the target vehicle The relative position vector between them, the magnitude of which is given by the following formula:

[0101] In the formula, These are speed-related parameters.

[0102] Based on this, the expression for the safety potential field function related to vehicle distance can be derived as follows:

[0103] In this embodiment, the vehicle Located in the vehicle Behind, therefore the vehicle Location Always greater than or equal to 0, and satisfying Therefore, the safety potential field function can be simplified to:

[0104] (2) Design of CAVs controller based on early warning level with time delay and potential field model Based on the aforementioned safety potential field function, a distributed cooperative control law is proposed, which explicitly considers the time delay in vehicle communication. The system includes a warning threshold mechanism for the aforementioned safety potential field, and considers dynamic control gains based on the collision warning mechanism. The control objective is to achieve state synchronization and maintain the desired distance between heterogeneous vehicles, under realistic communication constraints. Consider the states of the vehicle in front of the target vehicle and the lead vehicle: In the absence of warnings, the controller of the connected autonomous vehicle is:

[0105] In the formula, For connected autonomous vehicles At any moment The integrated control input, , , and Connected autonomous vehicles exist The position, velocity, acceleration, and potential field value of the vehicle in the heterogeneous vehicle group at all times; , and They represent the vehicles in front. exist The location, speed, and acceleration at any given moment; whether the vehicle in front is a connected autonomous vehicle or a traditional human-driven vehicle. , and These represent the position, velocity, and acceleration of the lead vehicle in a heterogeneous vehicle group, respectively. For communication delay, For vehicle length, This is the standard safety potential field value; , , and These are the gain coefficients for the position, speed, acceleration, and safety potential field of the connected autonomous vehicle, respectively.

[0106] In the warning state, the controller of the connected autonomous vehicle is:

[0107]

[0108]

[0109]

[0110]

[0111] In the formula, It serves as an early warning factor. , , and These are the gain coefficients of the connected autonomous vehicle based on the warning level, including position, speed, acceleration, and safety potential field. This refers to the timeframe from the triggering of a warning to its termination for the connected autonomous vehicle. As the warning level increases, the corresponding warning factor also increases. A decay function is used to reduce the overall gain coefficient as the warning level increases, resulting in a smoother control input and consistent vehicle state. When there is no warning, ,but This corresponds to the above-mentioned control method for connected automated vehicles in the absence of warning.

[0112] Fourth, a collaborative consistency control method for heterogeneous vehicle groups is established based on vehicle speed and inter-vehicle distance. After calculating and analyzing the state information of the entire vehicle group through the roadside sensing unit, commands are issued to the connected autonomous vehicles to control their movement while indirectly controlling the traditional human-driven vehicles in the heterogeneous vehicle group, thereby constraining the speed, inter-vehicle distance, and safety potential field of the entire heterogeneous vehicle group.

[0113] 1. Safety vehicle spacing constraints Set vehicle spacing constraints; when a heterogeneous vehicle group reaches a stable state, if the vehicles... For traditional drivers, the distance between them and the vehicle in front tends to be close to... If the vehicle For connected autonomous vehicles, the distance to the vehicle in front tends to be close to... The vehicle in front can be either a traditional human-driven vehicle or a connected autonomous vehicle.

[0114]

[0115] 2. Safe speed constraints: Guide the actual lead vehicle to steadily move towards the desired vehicle spacing. and expected speed To achieve a steady state, the speed difference between adjacent vehicles must be zero, as shown in the following formula:

[0116] 3. Standard safety potential field constraint: To guide the entire heterogeneous vehicle group to travel stably at the target speed, the desired vehicle speed is used. and expected vehicle spacing Calculate the standard safety potential field .like Figure 4 The principle behind this is to establish a virtual navigator vehicle located in front of the actual navigator vehicle, which operates at a predetermined speed. The system operates by applying a unidirectional effect only to the actual lead vehicle in the convoy, thus providing a uniform reference benchmark for the entire convoy. The standard safety potential field is represented as:

[0117] When the entire heterogeneous vehicle group reaches uniformity, over time, the difference between the safety potential field of any vehicle and the standard safety potential field approaches zero, as shown in the following equation:

[0118] In the formula, Indicates vehicle The safety potential field value in a heterogeneous vehicle group, vehicle It can be used for connected autonomous vehicles or traditional human-driven vehicles.

[0119] like Figure 3 As shown, after experiencing the disturbance of a sudden event, the entire heterogeneous vehicle fleet can be controlled according to a predetermined model by the connected autonomous vehicles and the traditional human-driven vehicles, and can quickly restore the consistency of the entire fleet, realizing the transition from the initial disordered state to the final consistent state.

[0120] Finally, 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 it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A heterogeneous vehicle group cooperative consistency control method based on a collision warning mechanism, characterized in that, Set up a single-lane traffic flow scenario on an urban road with mixed traffic of different types of vehicles, in which connected autonomous vehicles and traditional human-driven vehicles are mixed in a random proportion and drive in a platoon. Roadside sensing units are deployed on both sides of the road to collect vehicle status information. Combined with vehicle distance and collision time, a collision warning mechanism based on dual-indicator fusion is constructed. A longitudinal kinematic model of the vehicle is established based on the vehicle's kinematic characteristics, and a traditional human-vehicle cooperative control model and a connected autonomous vehicle cooperative control model based on collision warning levels are constructed by combining the vehicle information topology. A collaborative consistency control method for heterogeneous vehicle groups is established based on vehicle speed and inter-vehicle distance, and consistency control is performed on the entire heterogeneous vehicle group.

2. The method according to claim 1, characterized in that, By collecting vehicle status information of heterogeneous vehicle groups through roadside perception units, and combining it with inter-vehicle distance and collision time, a collision warning mechanism based on dual-indicator fusion is constructed. This includes setting the inter-vehicle distance as the distance between vehicles in the heterogeneous vehicle group. With the car in front Distance between: In the formula, For workshop distance, For vehicles At any moment Location, The length of the vehicle; Collision time Represented as: In the formula, For vehicles and vehicles relative velocity, For vehicles and vehicles The actual distance between them; At the time of collision Based on this, set a collision time based on a safe distance. : In the formula, This indicates the time required for the entire braking process; The time required from the discovery of an emergency to taking action; For time step; Set workshop safety distance threshold and collision time threshold based on safe distance : In the formula, , , , These correspond to the workshop distance thresholds for the respective risk levels, and ; , , , These correspond to the respective collision time thresholds based on safe distance for each risk level, and ; According to the threshold and Configure the four-level collision warning mechanism shown in the table below: Among them, risk level When in a state without warning .

3. The method according to claim 2, characterized in that, A longitudinal kinematic model of the vehicle is established based on its kinematic characteristics, and is expressed as follows: In the formula, , , They represent the first The vehicle is Position, velocity, and acceleration information at any given moment; Indicates the first The car is Time-based control input, Indicates the first in a heterogeneous vehicle group The vehicle's jerk, or the rate of change of acceleration; For traditional human drivers, a segmented car-following model based on warning level is used to characterize the driving behavior of traditional human drivers under the action of intelligent warning system. An improved linear car-following model is used in the absence of warning, and a restricted response model based on safety level is used in the presence of warning. For connected autonomous vehicles, a collaborative control framework that integrates the effects of the vehicle's potential field, communication latency, and safety warning thresholds is adopted to adjust the vehicle's control input.

4. The method according to claim 3, characterized in that, The segmented following-the-line model based on early warning levels includes: Set the desired vehicle spacing for drivers : In the formula, Indicates the total safe time. This indicates the driver's reaction time under warning conditions. Indicates the time step. Indicates the length of the vehicle; In the absence of warnings, control is achieved using the following linear car-following model: In the formula, Driving for traditional people Control input; , , Traditional human driving The current position, speed, and acceleration of the vehicle. For the desired vehicle speed; Indicates the vehicle in front exist The acceleration is constant, with the vehicle in front either a connected autonomous vehicle or a traditional human-driven vehicle; To assess the driver's sensitivity to deviations in distance between the vehicle and the vehicle, The driver's sensitivity to the deviation between their own speed and the desired speed. The driver's sensitivity to acceleration deviations of heterogeneous vehicles; In the absence of warning, the traditional acceleration update rule for human drivers is as follows: In the formula, The maximum allowable acceleration for heterogeneous vehicles; In the absence of warning, the traditional speed update rule for human drivers is as follows: In the formula, The maximum speed allowed for heterogeneous vehicles; In the absence of warning, the traditional rule for updating the location of a person driving a vehicle is as follows: Under warning conditions, the traditional human-driven vehicle control model based on the warning level is as follows: The model under early warning status is based on the risk level. Based on; for Warning factors at all times; for The level of warning at any time, Number of warning levels; They represent the vehicles in front. exist The location at any given moment, and whether the vehicle in front is a connected autonomous vehicle or a traditional human-driven vehicle; For traditional human-driven vehicles, dynamic safety distance; For the dynamic expected vehicle speed; , These are the gain coefficients of the position and speed of a traditional driver on the warning level; Under warning conditions, the acceleration update rule is as follows: Under alert status, the speed update rule is as follows: Under alert status, the location update rule is as follows: 。 5. The method according to claim 3, characterized in that, Adjusting the control input of connected automated vehicles using a collaborative control framework that integrates workshop potential field effects, communication latency, and safety warning thresholds includes, firstly, establishing a workshop safety potential field model: In the formula, Indicates the actual mass of the vehicle; The higher-order power terms representing velocity reflect the nonlinear amplification effect of velocity on inertia; is the scaling factor for higher-order power terms; The factor is constant to ensure that the correction factor is constant at the speed of the vehicle ahead. It remains positive even when it approaches zero; This represents the potential field gain coefficient; These are speed-related parameters; For vehicles At any moment speed; For vehicles At any moment Location; Based on a safety potential field model and a vehicle motion model, the system shares and analyzes the surrounding vehicle state information of the connected autonomous vehicle through a roadside perception unit, and considers dynamic control gain based on a collision warning mechanism and time delay in vehicle communication. Based on the early warning threshold mechanism of the safety potential field, a controller for connected autonomous vehicles is constructed. Setting the desired vehicle spacing for connected autonomous vehicles : In the formula, Indicates the total safe time. This indicates the response time of a connected autonomous vehicle under warning conditions. Indicates the time step. Indicates the length of the vehicle; In the absence of warnings, the controller of the connected autonomous vehicle is: In the formula, For connected autonomous vehicles At any moment The integrated control input, , , and Connected autonomous vehicles exist The position, velocity, acceleration, and potential field value of the vehicle in the heterogeneous vehicle group at all times; , and They represent the vehicles in front. exist The location, speed, and acceleration at any given moment; whether the vehicle in front is a connected autonomous vehicle or a traditional human-driven vehicle. , and These represent the position, velocity, and acceleration of the lead vehicle in a heterogeneous vehicle group, respectively. For communication delay, For vehicle length, This is the standard safety potential field value; , , and These are the gain coefficients for the position, speed, acceleration, and safety potential field of the connected autonomous vehicle, respectively. In the warning state, the controller of the connected autonomous vehicle is: In the formula, As an early warning factor; , , and These are the gain coefficients of the connected autonomous vehicle based on the warning level, including position, speed, acceleration, and safety potential field. This refers to the timeframe from the triggering of the warning to its termination for the connected autonomous vehicle.

6. The method according to claim 3, characterized in that, Based on the vehicle speeds and inter-vehicle distances detected by the roadside sensing unit, a collaborative consistency control method for heterogeneous vehicle groups is established to constrain the speed, inter-vehicle distances, and safety potential fields of the entire heterogeneous vehicle group.

7. The method according to claim 6, characterized in that, Methods for establishing collaborative consistency control for heterogeneous vehicle groups include: Set vehicle spacing constraints; when a heterogeneous vehicle group reaches a stable state, if the vehicles... For traditional drivers, the distance between them and the vehicle in front tends to be close to... If the vehicle For connected autonomous vehicles, the distance to the vehicle in front tends to be close to... The vehicle in front is either a traditional human-driven vehicle or a connected autonomous vehicle; the vehicle spacing constraint is expressed as: A speed difference constraint is set so that when the heterogeneous vehicle group reaches a steady state, the speed difference between adjacent vehicles is zero, as shown in the following formula: Setting up standard safety potential field constraints, firstly through the desired vehicle speed... and expected vehicle spacing Calculate the standard safety potential field : When the entire heterogeneous vehicle group reaches uniformity, the difference between the safety potential field of any vehicle and the standard safety potential field approaches zero, as shown in the following equation: In the formula, Indicates vehicle The safety potential field value in a heterogeneous vehicle group, vehicle For connected autonomous vehicles or traditional human-driven vehicles.

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