A method, system and storage medium for controlling mixed traffic flow of cav and hv vehicles

Abstract

The application relates to the technical field of traffic transportation, in particular to a CAV and HV vehicle mixed traffic flow control method and system and a storage medium, the CAV and HV vehicle mixed traffic flow control method comprising the following steps: S2, a traffic flow control model is established, which introduces both the preceding vehicle speed effect and the lane-changing strategy; S2, related parameters of the traffic flow control model are acquired; and S3, the related parameters acquired in the step S2 are input into the traffic flow control model established in the step S1, and simulation results describing the traffic control results are output; according to the road conditions, the CAV vehicle queuing conditions of front and rear vehicles can be acquired, the homogenous vehicle aggregation condition is further considered during the lane-changing decision, more CACC following modes appear on the road, the road traffic flow efficiency is further improved, the road safety is further improved due to the high interconnection of the CAV vehicles, and the mutual interference between the CAV vehicles and the HV vehicles is reduced.

Description

Technical Field

[0001] This invention relates to the field of transportation technology, specifically to a method, system, and storage medium for controlling traffic flow when CAV and HV vehicles are mixed. Background Technology

[0002] In the field of transportation, while the basic NaSch model can map some simple traffic phenomena, further simulation of complex traffic flows requires the introduction of additional rules. Traditional NaSch models, to ensure car-following distances, set the distance traveled by a vehicle at a given simulation step size to be less than the headway, without considering the speed of the vehicle in front. Therefore, to further refine the safety distance, those skilled in the art have introduced the Gipps safety distance rule based on the traditional NaSch model. Simultaneously, in formulating the car-following rule, the vehicle in front is no longer treated as a simple stationary particle, but its speed effect is considered. Furthermore, due to the high interconnectivity of Connected and Autonomous Vehicles (CAVs), the information exchange and processing capabilities between vehicles are greatly enhanced, and applying the technology considering the speed effect of the vehicle in front to CAVs will better leverage their advantages. In addition, those skilled in the art have further significantly improved road traffic efficiency by adding lane-changing rules to the above solutions.

[0003] However, the current problem is that although the existing technology has introduced the Gipps safe distance rule, the preceding vehicle speed effect and the lane-changing rule, it is still difficult to accurately simulate the lane-changing intentions, lane-changing timing and the interaction between vehicles during the lane-changing process in situations such as congestion or multi-lane traffic, resulting in low efficiency of road traffic communication. Summary of the Invention

[0004] Therefore, the technical problem to be solved by the present invention is to overcome the defects existing in the prior art, thereby providing a method, system and storage medium for controlling traffic flow when CAV and HV vehicles are mixed.

[0005] A method for controlling traffic flow when CAVs and HVs are mixed includes the following steps:

[0006] S1. Establish a traffic flow control model that incorporates both the speed effect of the vehicle in front and the lane-changing strategy.

[0007] S2. Obtain relevant parameters for the traffic flow control model;

[0008] S3. Input the relevant parameters obtained in step S2 into the traffic flow control model established in step S1, and output the simulation results describing the traffic control results;

[0009] The traffic flow control model is based on the simulated cellular automaton model of CAV vehicles; the lane-changing model in the simulated cellular automaton model is replaced with a clustered lane-changing model; and the safe perception distance of CAV vehicles and the safe perception distance of HV vehicles obtained based on the pre-built preceding vehicle speed effect model are applied to the rules of the simulated cellular automaton model.

[0010] The expression for the safe perception distance of a CAV vehicle is:

[0011] ;

[0012] In the formula, This indicates the safe perceived distance when the vehicle is a CAV (Container-Aided Vehicle). This indicates the actual physical distance between the vehicle and the vehicle in front. This indicates the speed of the vehicle in front as perceived by the vehicle itself; Indicates the safe distance between your vehicle and the vehicle in front; Indicates time step, Indicates the vehicle's registration number;

[0013] The expression for the safe perception distance of an HV vehicle is:

[0014] ;

[0015] In the formula, This indicates the safe perceived distance when the vehicle is an HV vehicle.

[0016] Preferably, the preceding vehicle speed effect model is a distance prediction model that takes into account the preceding vehicle speed and the distance between vehicles during acceleration.

[0017] Preferably, the expression for the front vehicle speed effect model is:

[0018] ;

[0019] ;

[0020] In the formula, This indicates that the vehicle estimates the speed of the vehicle in front of it at the next moment by receiving information from the vehicle in front of it. Indicates maximum speed; This indicates that, to ensure safety, the vehicle speed cannot be increased to the maximum speed, hence the minus 1; Indicates the speed of the preceding vehicle in relation to the time step; Indicates the acceleration of the vehicle in front; This indicates that the vehicle's sensing distance cannot be lower than 0; Indicates the safe perceived distance to the vehicle ahead;

[0021] Indicate the safe speed of the vehicle in front; Indicates the safe distance from the vehicle in front; It indicates the distance between the vehicles in front and the vehicles in front of the vehicles in front of each other.

[0022] Preferably, the rules for applying the CAV vehicle safety perception distance and HV vehicle safety perception distance obtained from the forward vehicle speed effect model to the simulation cellular automata model include:

[0023] Acceleration rules: When hour:

[0024] ;

[0025] The deceleration rule expression is: when hour:

[0026] ;

[0027] The expression for the uniform velocity rule is: when hour:

[0028] ;

[0029] Location update rule expression:

[0030] ;

[0031] In the formula, Indicates the speed of the vehicle at the next time step; This indicates the vehicle's current speed; These represent the vehicle's acceleration, maximum speed, and safe distance, respectively. Indicates the vehicle's safe perception distance Determine based on vehicle type; Indicates a safe distance;

[0032] Indicates the vehicle's deceleration; This means taking the minimum of the vehicle's speed and safe distance; Indicates the coordinates of the vehicle at the next time step; This represents the coordinates of the vehicle at this moment.

[0033] Preferably, the simulated cellular automata model also incorporates the random slowdown probability of HV vehicles, expressed as:

[0034] ;

[0035] ;

[0036] The speed update formula after a slow start is: ;

[0037] In the formula, Represents the probability of random slowing down; This represents the probability that the vehicle's speed is greater than the speed of the vehicle in front. Indicates the vehicle's speed; Indicates the speed at the front; This represents the probability of random slowdown when the vehicle's speed is less than the speed of the vehicle in front. This represents the probability of slowing down when the vehicle's speed is 0. This represents the probability of slowing down when the vehicle's speed is greater than 0.

[0038] Preferably, the relevant parameters include:

[0039] CAV vehicles , , , , , ;

[0040] HV vehicles , , , , , ;

[0041] Indicates the vehicle's maximum speed; This indicates the maximum deceleration of the vehicle. Code indicating the time step.

[0042] Preferably, the relevant parameters are obtained by sequentially performing principal component analysis and k-means clustering analysis on the data in the public dataset.

[0043] A traffic flow control system for mixed traffic of CAV and HV vehicles, comprising:

[0044] Model building module: Establish a traffic flow control model that incorporates both the speed effect of the vehicle in front and the lane-changing strategy.

[0045] Data acquisition module: Acquires relevant parameters of the traffic flow control model;

[0046] Simulation Execution Module: Inputs the relevant parameters obtained by the data acquisition module into the traffic flow control model established by the model building module, and outputs simulation results describing the traffic control results;

[0047] The traffic flow control model is based on the simulated cellular automaton model of CAV vehicles; the lane-changing model in the simulated cellular automaton model is replaced with a clustered lane-changing model; and the safe perception distance of CAV vehicles and the safe perception distance of HV vehicles obtained based on the pre-built preceding vehicle speed effect model are applied to the rules of the simulated cellular automaton model.

[0048] The expression for the safe perception distance of a CAV vehicle is:

[0049] ;

[0050] In the formula, This indicates the safe perceived distance when the vehicle is a CAV (Container-Aided Vehicle). This indicates the actual physical distance between the vehicle and the vehicle in front. This indicates the speed of the vehicle in front as perceived by the vehicle itself; Indicates the safe distance between your vehicle and the vehicle in front; Indicates time step, Indicates the vehicle's registration number;

[0051] The expression for the safe perception distance of an HV vehicle is:

[0052] ;

[0053] In the formula, This indicates the safe perceived distance when the vehicle is an HV vehicle.

[0054] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a traffic flow control method for mixed traffic flow of CAV and HV vehicles.

[0055] The technical solution of this invention has the following advantages:

[0056] Existing autonomous driving decision-making systems primarily rely on their own vehicle's radar and other sensors to identify road conditions and consider the driver's desired speed and the likelihood of collision with vehicles ahead when making lane-changing decisions. They quantify the driver's lane-changing intention by measuring speed dissipation and cumulative speed dissipation; a lane-changing intention is considered to have occurred when the cumulative speed dissipation exceeds a certain threshold. Simultaneously, radar detects the relative distance and speed of vehicles ahead and behind, predicting safe zones ahead and behind to assess the safety of the lane change. However, existing lane-changing decisions are based solely on the vehicle's own capabilities and do not consider the overall road traffic flow. Due to their limitations, both the vehicle itself and the human driver cannot recognize that localized clustering of similar vehicles can improve overall traffic flow efficiency and safety. This invention, however, considers the queuing situation of CAVs (Caravans and Aerial Vehicles) based on road conditions and further considers the clustering of similar vehicles during lane-changing decisions. This leads to more CACC (Caravan and Aerial Vehicle) following patterns on the road, further improving road traffic flow efficiency. Simultaneously, due to the high interconnectivity of CAVs, road safety is further enhanced, and mutual interference between CAVs and HAVs (High-speed Aerial Vehicles) is reduced. Attached Figure Description

[0057] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0058] Figure 1 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0059] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for 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, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0061] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0062] Furthermore, the technical features involved in the different embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0063] Example 1

[0064] like Figure 1 This embodiment discloses a traffic flow control method for mixed traffic of CAV and HV vehicles, including the following steps:

[0065] S1. Establish a traffic flow control model that incorporates both the speed effect of the vehicle in front and the lane-changing strategy.

[0066] S2. Obtain relevant parameters for the traffic flow control model;

[0067] S3. Input the relevant parameters obtained in step S2 into the traffic flow control model established in step S1, and output the simulation results describing the traffic control results;

[0068] The traffic flow control model is based on the simulated cellular automaton model of CAV vehicles; the lane-changing model in the simulated cellular automaton model is replaced with a clustered lane-changing model; and the safe perception distance of CAV vehicles and the safe perception distance of HV vehicles obtained based on the pre-built preceding vehicle speed effect model are applied to the rules of the simulated cellular automaton model.

[0069] Specifically:

[0070] Simulations were performed on a road surface with periodic boundaries using MATLAB, with each cell being 1.5 meters, each vehicle defined as 5 cells, and the road length being 2000 cells.

[0071] The simulation environment is established using the environment creation function, and random vehicles are assigned to the environment using the vehicle assignment function. Specifically: the `plaza_create` function creates a plaza matrix of size (B+2)*length, where B is the lane width (plus 2 due to side boundaries), and length is the set road length. The two sides of the plaza matrix serve as the upper and lower boundaries of the simulated road, and these boundaries are circular. The `create_car` function randomly assigns vehicles to the plaza matrix created by `plaza_create`, ensuring that the interval between vehicles is no less than the vehicle's length. The `gap_sta` interval calculation function calculates the distance between vehicles using the price at the vehicle's front coordinates and the vehicle's length.

[0072] A velocity adaptive function is established by combining a cellular automata model to assign velocity to the simulated random vehicle;

[0073] Furthermore, a two-lane lane-changing model is introduced, and a stochastic slowdown function is added to simulate the driving behavior of real drivers.

[0074] A clustering lane-changing strategy is further introduced into the two-lane lane-changing model.

[0075] Define the update rules for the traffic flow control model:

[0076] First, existing technologies, when establishing traffic flow models, do not treat the vehicle in front as a stationary individual. When calculating the distance to the vehicle in front, they consider the interconnectivity of CAVs (Carrier Aerial Vehicles) and take the upcoming speed of the vehicle in front into account. Therefore, the distance between a vehicle and the vehicle in front at any given moment should take into account the speed of the vehicle in front at the next moment, thus shortening the following distance between vehicles. This embodiment, based on the above ideas, establishes a vehicle-to-vehicle distance prediction model that considers the speed and acceleration of the vehicle in front.

[0077] The expression for the front vehicle speed effect model is:

[0078] ;

[0079] ;

[0080] In the formula, This indicates that the vehicle estimates the speed of the vehicle in front of it at the next moment by receiving information from the vehicle in front of it. Indicates maximum speed; This indicates that, to ensure safety, the vehicle speed cannot be increased to the maximum speed, hence the minus 1; Indicates the speed of the preceding vehicle in relation to the time step; Indicates the acceleration of the vehicle in front; This indicates that the vehicle's sensing distance cannot be lower than 0; Indicates the safe perceived distance to the vehicle ahead;

[0081] Indicate the safe speed of the vehicle in front; Indicates the safe distance from the vehicle in front; It indicates the distance between the vehicles in front and the vehicles in front of the vehicles in front of each other.

[0082] The safe perception distance of the vehicle in the rules of the simulated cellular automata model It is the vehicle's judgment of the safe distance it can travel ahead in the next moment based on the information it can receive and process. Because CAV vehicles and HV vehicles have many differences, therefore... The calculation methods are different.

[0083] The expression for the safe perception distance of a CAV vehicle is:

[0084] ;

[0085] In the formula, This indicates the safe perceived distance when the vehicle is a CAV (Container-Aided Vehicle). This indicates the actual physical distance between the vehicle and the vehicle in front. This indicates the speed of the vehicle in front as perceived by the vehicle itself; Indicates the safe distance between your vehicle and the vehicle in front; Indicates time step, Indicates the vehicle's registration number;

[0086] The expression for the safe perception distance of an HV vehicle is:

[0087] ;

[0088] In the formula, This indicates the safe perceived distance when the vehicle is an HV vehicle.

[0089] It should be noted that since CAV vehicles are not widely used in real life, simulation methods were used in the research on CAV vehicles. Cellular automata models are commonly used in this field. The following are the four rules of the cellular automata model used in this embodiment for CAV vehicles and the proposed front vehicle speed effect:

[0090] Acceleration rules: When hour:

[0091] ;

[0092] The deceleration rule expression is: when hour:

[0093] ;

[0094] The expression for the uniform velocity rule is: when hour:

[0095] ;

[0096] Furthermore, to simulate the behavior of human drivers, this embodiment assumes that the stop-and-go phenomenon is more pronounced in synchronous flow conditions. Therefore, the random slowdown probability of HV vehicles is set as follows:

[0097] ;

[0098] ;

[0099] Random slowing probability When the vehicle's speed is greater than the speed of the vehicle in front. This indicates that when a vehicle is adapting to the speed of the vehicle in front, the probability of the vehicle randomly slowing down is greater. When the vehicle speed is 0 ,and This reflects the phenomenon of slow vehicle starts in real life. The speed update formula after a slow vehicle start is: ;

[0100] In the formula, Represents the probability of random slowing down; This represents the probability that the vehicle's speed is greater than the speed of the vehicle in front. Indicates the vehicle's speed; Indicates the speed at the front; This represents the probability of random slowdown when the vehicle's speed is less than the speed of the vehicle in front. This represents the probability of slowing down when the vehicle speed is 0. This represents the slowdown probability when the vehicle value is greater than 0.

[0101] Location update rule expression:

[0102] ;

[0103] In the formula, Indicates the speed of the vehicle at the next time step; This indicates the vehicle's current speed; These represent the vehicle's acceleration, maximum speed, and safe distance, respectively. Indicates the vehicle's safe perception distance Determine based on vehicle type; Indicates a safe distance;

[0104] Indicates the vehicle's deceleration; This means that the vehicle speed and safe distance are minimized. Due to the specificity of this field, in this embodiment, the appearance of speed is equivalent to the distance traveled by the vehicle in the next time step. Indicates the coordinates of the vehicle at the next time step; This represents the coordinates of the vehicle at this moment.

[0105] Of the four main rules All are derived from the forward vehicle speed effect model;

[0106] In this embodiment, the relevant parameters are obtained by sequentially performing principal component analysis and k-means clustering analysis on the data in the public dataset. Specifically:

[0107] This embodiment uses data from the 7:50am-8:05am time period in the US-101 dataset of the NGSIM database as a foundation to extract vehicle driving data. The NGSIM database is a high-resolution traffic trajectory database established by the Federal Highway Administration (FHWA) of the United States, storing a wealth of vehicle driving information, including speed, acceleration, lane-changing behavior, headway, and time headway. In this example, 80% of the data from the NGSIM database for the corresponding road time period is used for driving data determination and parameter calibration, while the remaining 20% ​​is used for model validation.

[0108] In this embodiment, the relevant parameters obtained after principal component analysis and k-means clustering analysis include:

[0109] CAV vehicles , , , , , ;

[0110] HV vehicles , , , , , ;

[0111] in, Indicates the vehicle's maximum speed; This indicates the maximum deceleration of the vehicle. Code indicating the time step.

[0112] Table 1 shows an example of data from the actual simulation run.

[0113] Table 1 Vehicle simulation parameters

[0114]

[0115] Example 2

[0116] A traffic flow control system for mixed traffic of CAV and HV vehicles, comprising:

[0117] Model building module: Establish a traffic flow control model that incorporates both the speed effect of the vehicle in front and the lane-changing strategy.

[0118] Data acquisition module: Acquires relevant parameters of the traffic flow control model;

[0119] Simulation Execution Module: Inputs the relevant parameters obtained by the data acquisition module into the traffic flow control model established by the model building module, and outputs simulation results describing the traffic control results;

[0120] The traffic flow control model is based on the simulated cellular automaton model of CAV vehicles; the lane-changing model in the simulated cellular automaton model is replaced with a clustered lane-changing model; and the safe perception distance of CAV vehicles and the safe perception distance of HV vehicles obtained based on the pre-built preceding vehicle speed effect model are applied to the rules of the simulated cellular automaton model.

[0121] The expression for the safe perception distance of a CAV vehicle is:

[0122] ;

[0123] In the formula, This indicates the safe perceived distance when the vehicle is a CAV (Container-Aided Vehicle). This indicates the actual physical distance between the vehicle and the vehicle in front. This indicates the speed of the vehicle in front as perceived by the vehicle itself; Indicates the safe distance between your vehicle and the vehicle in front; Indicates time step, Indicates the vehicle's registration number;

[0124] The expression for the safe perception distance of an HV vehicle is:

[0125] ;

[0126] In the formula, This indicates the safe perceived distance when the vehicle is an HV vehicle.

[0127] Example 3

[0128] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of a traffic flow control method for mixed CAV and HV vehicles according to Embodiment 1.

[0129] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for controlling traffic flow when CAVs and HVs travel together, characterized in that, Includes the following steps: S1. Establish a traffic flow control model that incorporates both the speed effect of the vehicle in front and the lane-changing strategy. S2. Obtain relevant parameters for the traffic flow control model; S3. Input the relevant parameters obtained in step S2 into the traffic flow control model established in step S1, and output the simulation results describing the traffic control results; The traffic flow control model is based on the simulated cellular automaton model of CAV vehicles; the lane-changing model in the simulated cellular automaton model is replaced with a clustered lane-changing model; and the safe perception distance of CAV vehicles and the safe perception distance of HV vehicles obtained based on the pre-built preceding vehicle speed effect model are applied to the rules of the simulated cellular automaton model. The expression for the safe perception distance of a CAV vehicle is: ； In the formula, This indicates the safe perceived distance when the vehicle is a CAV (Container-Aided Vehicle). This indicates the actual physical distance between the vehicle and the vehicle in front. This indicates the speed of the vehicle in front as perceived by the vehicle itself; Indicates the safe distance between your vehicle and the vehicle in front; Indicates time step, Indicates the vehicle's registration number; The expression for the safe perception distance of an HV vehicle is: ； In the formula, This indicates the safe perceived distance when the vehicle is an HV (High-Vehicle) vehicle. The preceding vehicle speed effect model is a distance prediction model that takes into account the preceding vehicle speed and the acceleration of vehicles. The expression for the front vehicle speed effect model is: ； ； In the formula, This indicates that the vehicle estimates the speed of the vehicle in front of it at the next moment by receiving information from the vehicle in front of it. Indicates maximum speed; This indicates that, to ensure safety, the vehicle speed cannot be increased to the maximum speed, hence the minus 1; Indicates the speed of the preceding vehicle in relation to the time step; Indicates the acceleration of the vehicle in front; This indicates that the vehicle's sensing distance cannot be lower than 0; Indicates the safe perceived distance to the vehicle ahead; Indicate the safe speed of the vehicle in front; Indicates the safe distance from the vehicle in front; It indicates the distance between the vehicles in front and the vehicles in front of the vehicles in front of each other.

2. The traffic flow control method for mixed traffic of CAV and HV vehicles according to claim 1, characterized in that, The rules for applying the CAV (Carrier Availability) and HV (Hard Vehicle Availability) vehicle safety perception distances obtained from the forward vehicle speed effect model to the simulation cellular automata model include: Acceleration rules: When hour: ； The deceleration rule expression is: when hour: ； The expression for the uniform velocity rule is: when hour: ； Location update rule expression: ； In the formula, Indicates the speed of the vehicle at the next time step; This indicates the vehicle's current speed; These represent the vehicle's acceleration, maximum speed, and safe distance, respectively. Indicates the vehicle's safe perception distance Determine based on vehicle type; Indicates a safe distance; Indicates the vehicle's deceleration; This means taking the minimum of the vehicle's speed and safe distance; Indicates the coordinates of the vehicle at the next time step; This represents the coordinates of the vehicle at this moment.

3. The traffic flow control method for mixed traffic of CAV and HV vehicles according to claim 2, characterized in that, The simulated cellular automata model also introduces the random slowdown probability of HV vehicles, expressed as: ； ； The speed update formula after a slow start is: ; In the formula, Represents the probability of random slowing down; This represents the probability that the vehicle's speed is greater than the speed of the vehicle in front. Indicates the vehicle's speed; Indicates the speed at the front; This represents the probability of random slowdown when the vehicle's speed is less than the speed of the vehicle in front. This represents the probability of slowing down when the vehicle's speed is 0. This represents the probability of slowing down when the vehicle's speed is greater than 0.

4. The traffic flow control method for mixed traffic of CAV and HV vehicles according to claim 3, characterized in that, Relevant parameters include: CAV vehicles , , , , , ; HV vehicles , , , , , ; Indicates the vehicle's maximum speed; This indicates the maximum deceleration of the vehicle. Code indicating the time step.

5. A traffic flow control method for mixed traffic of CAV and HV vehicles according to claim 4, characterized in that, The relevant parameters were obtained by sequentially performing principal component analysis and k-means clustering analysis on the data in the public dataset.

6. A traffic flow control system for mixed traffic of CAV and HV vehicles, characterized in that, include: Model building module: Establish a traffic flow control model that incorporates both the speed effect of the vehicle in front and the lane-changing strategy. Data acquisition module: Acquires relevant parameters of the traffic flow control model; Simulation Execution Module: Inputs the relevant parameters obtained by the data acquisition module into the traffic flow control model established by the model building module, and outputs simulation results describing the traffic control results; The traffic flow control model is based on the simulated cellular automaton model of CAV vehicles; the lane-changing model in the simulated cellular automaton model is replaced with a clustered lane-changing model; and the safe perception distance of CAV vehicles and the safe perception distance of HV vehicles obtained based on the pre-built preceding vehicle speed effect model are applied to the rules of the simulated cellular automaton model. The expression for the safe perception distance of a CAV vehicle is: ； In the formula, This indicates the safe perceived distance when the vehicle is a CAV (Container-Aided Vehicle). This indicates the actual physical distance between the vehicle and the vehicle in front. This indicates the speed of the vehicle in front as perceived by the vehicle itself; Indicates the safe distance between your vehicle and the vehicle in front; Indicates time step, Indicates the vehicle's registration number; The expression for the safe perception distance of an HV vehicle is: ； In the formula, This indicates the safe perceived distance when the vehicle is an HV (High-Vehicle) vehicle. The preceding vehicle speed effect model is a distance prediction model that takes into account the preceding vehicle speed and the acceleration of vehicles. The expression for the front vehicle speed effect model is: ； ； In the formula, This indicates that the vehicle estimates the speed of the vehicle in front of it at the next moment by receiving information from the vehicle in front of it. Indicates maximum speed; This indicates that, to ensure safety, the vehicle speed cannot be increased to the maximum speed, hence the minus 1; Indicates the speed of the preceding vehicle in relation to the time step; Indicates the acceleration of the vehicle in front; This indicates that the vehicle's sensing distance cannot be lower than 0; Indicates the safe perceived distance to the vehicle ahead; Indicate the safe speed of the vehicle in front; Indicates the safe distance from the vehicle in front; It indicates the distance between the vehicles in front and the vehicles in front of the vehicles in front of each other.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by the processor, it implements the steps of the traffic flow control method for mixed CAV and HV vehicles as described in any one of claims 1 to 5.

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