CAV and HV mixed traffic flow control method and system and storage medium

Abstract

The invention 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, and the CAV and HV vehicle mixed traffic flow control method comprises the steps: S2, building a traffic flow control model which introduces a front vehicle speed effect and also introduces a gathering lane changing strategy; s2, obtaining related parameters of the traffic flow control model; s3, inputting the related parameters obtained in the step S2 into the traffic flow control model established in the step S1, and outputting a simulation result describing a traffic control result; compared with the prior art, the method has the advantages that according to road conditions, CAV queuing conditions of front and back vehicles can be acquired, and the gathering condition of homogeneous vehicles is further considered during lane changing decision making, so that more CACC car-following modes appear on a road, the road traffic flow efficiency is further improved, meanwhile, due to the high interconnection of the CAVs, the road safety is further improved, and the road traffic flow efficiency is improved. And the mutual interference between the CAV vehicle and the HAV vehicle is reduced.

Description

Technical Field

[0001] The present invention relates to the field of transportation technology, and in particular to a method, system and storage medium for controlling mixed traffic flow of CAV and HV vehicles. Background Art

[0002] In the field of transportation, although the basic NaSch model can map some simple traffic phenomena, further simulation of complex traffic flows requires the introduction of additional rules. To ensure the following distance between vehicles, the traditional NaSch model sets the distance traveled by the corresponding simulation step length to be less than the distance between the front vehicles, without considering the speed of the leading vehicle. Therefore, in order to further refine the safety distance, those skilled in the art have introduced the Gipps safety distance rule based on the traditional NaSch model. At the same time, in the formulation of vehicle following rules, the leading vehicle is no longer regarded as a simple stationary particle, but the speed effect of the leading vehicle is taken into account. At the same time, due to the high degree of connectivity between Connected and Autonomous Vehicles (CAVs), the information exchange and information processing capabilities between vehicles will be greatly improved. Applying the technology that considers the speed effect of the leading vehicle to CAVs will further leverage the advantages of CAVs. In addition, those skilled in the art have further significantly improved road traffic efficiency by adding lane change rules on the basis of the above solution.

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

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

[0005] A method for controlling mixed traffic flow of CAVs and HVs comprises the following steps: S1. Establish a traffic flow control model that incorporates both the preceding vehicle speed effect and the convergence lane-changing strategy. S2. Obtain relevant parameters of the traffic flow control model; S3. The relevant parameters obtained in step S2 are input into the traffic flow control model established in step S1, and the simulation results describing the traffic control results are output; The traffic flow control model is based on a simulated cellular automaton model of CAV vehicles. The lane-changing model in the simulated cellular automaton model is replaced with an aggregated lane-changing model. The safety perception distances of CAV vehicles and HV vehicles, obtained based on a pre-built leading vehicle speed effect model, are applied to the rules of the simulated cellular automaton model. The expression of CAV vehicle safety perception distance is: ; Where, Indicates the safety perception distance when the ego vehicle is a CAV vehicle; Indicates the actual physical distance between the vehicle and the preceding vehicle; Indicates the speed of the preceding vehicle as perceived by the vehicle; Indicates the safe distance between the vehicle and the vehicle ahead; represents the time step, Indicates the vehicle number of the vehicle; The expression of the safety perception distance of HV vehicles is: ; Where, Indicates the safe perception distance when the own vehicle is a HV vehicle.

[0006] Preferably, the preceding vehicle speed effect model is a vehicle-to-vehicle distance estimation model that takes into account the preceding vehicle speed and the acceleration of the preceding vehicle.

[0007] Preferably, the preceding vehicle speed effect model expression is: ; ; Where, It indicates the vehicle's estimate of the preceding vehicle's speed at the next moment based on the information received from the preceding vehicle. Indicates the maximum speed; To ensure safety, the vehicle speed is not allowed to reach the maximum speed, so it is reduced by 1; represents the speed of the preceding vehicle with respect to the time step; Indicates the acceleration of the preceding vehicle; Indicates that the vehicle's perception distance cannot be lower than 0; Indicates the safe perception distance of the ego vehicle; Indicates the safe speed of the vehicle ahead; Indicates the safe distance to the vehicle ahead; Indicates the distance between the vehicle in front and the vehicle in front of it.

[0008] Preferably, the rules for applying the CAV vehicle safety perception distance and the HV vehicle safety perception distance obtained by the preceding vehicle speed effect model to the simulation cellular automaton model include: Acceleration rules: When hour: ; The deceleration rule expression is: hour: ; The uniform speed rule expression is: hour: ; Position update rule expression: ; Where, Indicates the speed of the vehicle in the next time step; Indicates the current speed of the vehicle; They represent the acceleration of the vehicle, the maximum speed of the vehicle, and the safety distance respectively; Indicates the vehicle's safety perception distance Determine based on the type of vehicle; Indicates safe distance; Indicates the vehicle's deceleration; Indicates that the vehicle speed and safety distance are minimized; Represents the coordinates of the vehicle at the next time step; Indicates the coordinate value of the vehicle at this moment.

[0009] Preferably, the random slowing probability of HV vehicles is also introduced into the simulation cellular automaton model, and the expression is: ; ; The speed update formula after the vehicle slow start is: ; Where, represents the random slowing-down probability; represents the probability when the vehicle's speed is greater than the preceding vehicle's speed; Indicates the vehicle speed; Indicates the front speed; represents the probability of random slowing down when the vehicle speed is less than that of the preceding vehicle; represents the probability of slowing down when the vehicle speed is 0; Indicates the slowing probability when the vehicle speed is greater than 0.

[0010] Preferably, the relevant parameters include: CAV vehicles 、 、 、 、 、 ; HV vehicles 、 、 、 、 、 ; Indicates the maximum speed of the vehicle; Indicates the maximum deceleration of the vehicle; Represents the time step code.

[0011] Preferably, the relevant parameters are obtained by sequentially performing principal component analysis and k-means cluster analysis on the data in the public dataset.

[0012] A traffic flow control system for mixed CAV and HV vehicles, comprising: Model building module: Build a traffic flow control model that incorporates both the preceding vehicle speed effect and the converged lane-changing strategy; Data acquisition module: obtain relevant parameters of traffic flow control model; Simulation operation module: inputs the relevant parameters obtained by the data acquisition module into the traffic flow control model established by the model construction module, and outputs simulation results describing the traffic control results; The traffic flow control model is based on a simulated cellular automaton model of CAV vehicles. The lane-changing model in the simulated cellular automaton model is replaced with an aggregated lane-changing model. The safety perception distances of CAV vehicles and HV vehicles, obtained based on a pre-built leading vehicle speed effect model, are applied to the rules of the simulated cellular automaton model. The expression of CAV vehicle safety perception distance is: ; Where, Indicates the safety perception distance when the ego vehicle is a CAV vehicle; Indicates the actual physical distance between the vehicle and the preceding vehicle; Indicates the speed of the preceding vehicle as perceived by the vehicle; Indicates the safe distance between the vehicle and the vehicle ahead; represents the time step, Indicates the vehicle number of the vehicle; The expression of the safety perception distance of HV vehicles is: ; Where, Indicates the safe perception distance when the own vehicle is a HV vehicle.

[0013] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for controlling mixed traffic flow of CAV and HV vehicles.

[0014] The technical solution of the present invention has the following advantages: Existing autonomous driving decision-making systems primarily use radar and other sensors on the vehicle itself to identify lane change conditions and consider the driver's desired speed and the potential for collision with the vehicle ahead. They quantify the driver's lane change intention using speed dissatisfaction and accumulated speed dissatisfaction. When the accumulated speed dissatisfaction exceeds a certain threshold, a lane change intention is considered. Simultaneously, radar detects the relative distance and speed of the preceding and following vehicles, predicting safety zones ahead and behind, and assessing the safety of the vehicle's lane change. However, existing lane change decisions are primarily based on the vehicle itself, rather than considering the overall traffic flow. Due to their limitations, vehicles, including human drivers, are unaware that localized clustering of homogeneous vehicles can improve overall traffic flow efficiency and safety. The present invention, however, not only detects the CAV (Computer Aided Vehicle) (CAAV) queuing situation based on road conditions, but also considers this clustering when making lane change decisions. This allows for more CACC (Computer Aided Vehicle Accumulation) following modes on the road, further improving traffic flow efficiency. Furthermore, due to the high interconnectivity of CAVs, road safety is further enhanced, while interference between CAVs and HAVs is reduced. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0016] Figure 1 Schematic diagram of the method of the present invention. DETAILED DESCRIPTION

[0017] The technical solution of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the embodiments described are only some embodiments of the present invention, not all embodiments. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0018] In the description of the present invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicating orientations or positional relationships, are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present 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.

[0019] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed, detachable, or integral connections; mechanical or electrical connections; direct or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0020] In addition, 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.

[0021] Example 1 like Figure 1 This embodiment discloses a method for controlling mixed traffic flow of CAVs and HVs, including the following steps: S1. Establish a traffic flow control model that incorporates both the preceding vehicle speed effect and the convergence lane-changing strategy. S2. Obtain relevant parameters of the traffic flow control model; S3. The relevant parameters obtained in step S2 are input into the traffic flow control model established in step S1, and the simulation results describing the traffic control results are output; The traffic flow control model is based on a simulated cellular automaton model of CAV vehicles. The lane-changing model in the simulated cellular automaton model is replaced with an aggregated lane-changing model. The safety perception distances of CAV vehicles and HV vehicles, obtained based on a pre-built leading vehicle speed effect model, are applied to the rules of the simulated cellular automaton model. Specifically: MATLAB is used to simulate the road surface with periodic boundaries. Each cell is 1.5 meters, each vehicle is defined as 5 cells, and the road length is 2000 cells.

[0022] Use the environment creation function to establish the simulation environment, and use the vehicle assignment function to assign random vehicles to the environment. Specifically, use the plaza_create environment creation function to create a plaza matrix of (B+2)*length, where B is the lane width (+2 for the lanes, as there are borders on both sides), 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 the upper and lower boundaries are circular. The create_car function randomly assigns vehicles to the plaza matrix created by the plaza_create function, with the spacing between vehicles not less than the vehicle body length. The gap_sta interval statistics function calculates the distance between vehicles using the vehicle head coordinates and the vehicle body length.

[0023] Combined with the cellular automaton model, a speed adaptive function is established to assign speeds to the simulated random vehicles. We further introduce a two-lane lane-changing model and add a random slowing function to simulate the driving behavior of real drivers. The cluster lane-changing strategy is further introduced into the two-lane lane-changing model.

[0024] Set the update rules of the traffic flow control model: First, existing technologies fail to consider the preceding vehicle as a stationary entity when building traffic flow models. Instead, the distance calculation between the preceding vehicle and the vehicle in front takes into account the interconnectedness of CAVs and the preceding vehicle's subsequent speed. The distance between the preceding vehicle and the preceding vehicle at one moment should take into account the preceding vehicle's speed at the next moment, thereby shortening the following distance between vehicles. Based on this concept, this embodiment establishes a vehicle-to-vehicle distance estimation model that considers the preceding vehicle's speed and acceleration. The model expression of the preceding vehicle speed effect is: ; ; Where, It indicates the vehicle's estimate of the preceding vehicle's speed at the next moment based on the information received from the preceding vehicle. Indicates the maximum speed; To ensure safety, the vehicle speed is not allowed to reach the maximum speed, so it is reduced by 1; represents the speed of the preceding vehicle with respect to the time step; Indicates the acceleration of the preceding vehicle; Indicates that the vehicle's perception distance cannot be lower than 0; Indicates the safe perception distance of the ego vehicle; Indicates the safe speed of the vehicle ahead; Indicates the safe distance to the vehicle ahead; Indicates the distance between the vehicle in front and the vehicle in front of it.

[0025] Safety perception distance of the ego vehicle in the rules of the simulation cellular automaton model It is the vehicle's judgment on the safe distance ahead at the next moment based on the information it can receive and process. Due to the many differences between CAV vehicles and HV vehicles, The calculation method is different.

[0026] The expression of CAV vehicle safety perception distance is: ; Where, Indicates the safety perception distance when the ego vehicle is a CAV vehicle; Indicates the actual physical distance between the vehicle and the preceding vehicle; Indicates the speed of the preceding vehicle as perceived by the vehicle; Indicates the safe distance between the vehicle and the vehicle ahead; represents the time step, Indicates the vehicle number of the vehicle; The expression of the safety perception distance of HV vehicles is: ; Where, Indicates the safe perception distance when the own vehicle is a HV vehicle.

[0027] It should be noted that since CAVs are not yet widespread in real life, simulation methods are used in related research on CAVs. The cellular automaton model is a commonly used model in this field. The following are the four major rules of the cellular automaton model used in this embodiment for CAVs and the proposed leading vehicle speed effect: Acceleration rules: When hour: ; The deceleration rule expression is: hour: ; The uniform speed rule expression is: hour: ; In addition, in order to simulate the behavior habits of human drivers, this embodiment considers that the stop-and-go phenomenon of vehicles in the synchronous flow state is more obvious, so the random slowing probability of HV vehicles is set as follows: ; ; Random slowdown probability When the vehicle speed is greater than the vehicle ahead, , indicating that when the ego vehicle adapts to the speed of the preceding vehicle, the probability of random deceleration will be greater. When the vehicle speed is 0 ,and , which reflects the slow start phenomenon of vehicles in real life. The speed update formula after the vehicle slow start is: ; Where, represents the random slowing-down probability; represents the probability when the vehicle's speed is greater than the preceding vehicle's speed; Indicates the vehicle speed; Indicates the front speed; represents the probability of random slowing down when the vehicle speed is less than that of the preceding vehicle; Indicates the probability of slowing down when the vehicle speed is 0; Indicates the probability of slowing down of the vehicle when it is greater than 0.

[0028] Position update rule expression: ; Where, Indicates the speed of the vehicle in the next time step; Indicates the current speed of the vehicle; They represent the acceleration of the vehicle, the maximum speed of the vehicle, and the safety distance respectively; Indicates the vehicle's safety perception distance Determine based on the type of vehicle; Indicates safe distance; Indicates the vehicle's deceleration; Indicates that the vehicle speed and the safety distance are minimized. Due to the particularity of this field, in this embodiment, the appearance of speed is equivalent to the distance traveled by the vehicle in the next time step; Represents the coordinates of the vehicle at the next time step; Indicates the coordinate value of the vehicle at this moment.

[0029] Among the four major rules, All are derived from the preceding vehicle speed effect model; In this embodiment, the relevant parameters are obtained by performing principal component analysis and k-means cluster analysis on the data in the public dataset. Specifically: Vehicle driving data was extracted based on data from the US-101 dataset in the US NGSIM database, which covers the time period from 7:50 AM to 8:05 AM. The NGSIM database, a high-resolution traffic trajectory database established by the US Federal Highway Administration (FHWA), stores a wide range of vehicle driving information, including speed, acceleration, lane change behavior, headway, and headway time. This example uses 80% of the data from the NGSIM database for the corresponding road time period for driving data determination and parameter calibration, while the remaining 20% ​​is used for model validation.

[0030] In this embodiment, the relevant parameters obtained after principal component analysis and k-means cluster analysis include: CAV vehicles 、 、 、 、 、 ; HV vehicles 、 、 、 、 、 ; in, Indicates the maximum speed of the vehicle; Indicates the maximum deceleration of the vehicle; Represents the time step code.

[0031] Table 1 shows an example of data from an actual simulation run.

[0032] Table 1 Vehicle simulation parameters

[0033] Example 2 A traffic flow control system for mixed CAV and HV vehicles, comprising: Model building module: Build a traffic flow control model that incorporates both the preceding vehicle speed effect and the converged lane-changing strategy; Data acquisition module: obtains relevant parameters of traffic flow control model; Simulation operation module: inputs the relevant parameters obtained by the data acquisition module into the traffic flow control model established by the model construction module, and outputs simulation results describing the traffic control results; The traffic flow control model is based on a simulated cellular automaton model of CAV vehicles. The lane-changing model in the simulated cellular automaton model is replaced with an aggregated lane-changing model. The safety perception distances of CAV vehicles and HV vehicles, obtained based on a pre-built leading vehicle speed effect model, are applied to the rules of the simulated cellular automaton model. The expression of CAV vehicle safety perception distance is: ; Where, Indicates the safety perception distance when the ego vehicle is a CAV vehicle; Indicates the actual physical distance between the vehicle and the preceding vehicle; Indicates the speed of the preceding vehicle as perceived by the vehicle; Indicates the safe distance between the vehicle and the vehicle ahead; represents the time step, Indicates the vehicle number of the vehicle; The expression of the safety perception distance of HV vehicles is: ; Where, Indicates the safe perception distance when the own vehicle is a HV vehicle.

[0034] Example 3 A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for controlling mixed traffic flow of CAV and HV vehicles in embodiment 1.

[0035] Obviously, the above embodiments are merely examples for clarity of explanation and are not intended to limit the implementation methods. Those skilled in the art will readily appreciate that other variations or modifications based on the above descriptions are possible. It is not necessary and impossible to enumerate all implementation methods here. Obvious variations or modifications arising therefrom remain within the scope of protection of the present invention.

Claims

1. A method for controlling mixed traffic flow of CAV and HV vehicles, characterized in that: The following steps are involved: S1. Establish a traffic flow control model that incorporates both the preceding vehicle speed effect and the convergence lane-changing strategy. S2. Obtain relevant parameters of the traffic flow control model; S3. The relevant parameters obtained in step S2 are input into the traffic flow control model established in step S1, and the simulation results describing the traffic control results are output; The traffic flow control model is based on a simulated cellular automaton model of CAV vehicles. The lane-changing model in the simulated cellular automaton model is replaced with an aggregated lane-changing model. The safety perception distances of CAV vehicles and HV vehicles, obtained based on a pre-built leading vehicle speed effect model, are applied to the rules of the simulated cellular automaton model. The expression of CAV vehicle safety perception distance is: ； Where, Indicates the safety perception distance when the ego vehicle is a CAV vehicle; Indicates the actual physical distance between the vehicle and the preceding vehicle; Indicates the speed of the preceding vehicle as perceived by the vehicle; Indicates the safe distance between the vehicle and the vehicle ahead; represents the time step, Indicates the vehicle number of the vehicle; The expression of the safety perception distance of HV vehicles is: ； Where, Indicates the safe perception distance when the own vehicle is a HV vehicle.

2. The method for controlling mixed traffic flow of CAV and HV vehicles according to claim 1, characterized in that: The preceding vehicle speed effect model is a vehicle-to-vehicle distance estimation model that takes into account the preceding vehicle speed and acceleration.

3. The method for controlling mixed traffic flow of CAV and HV vehicles according to claim 2, characterized in that: The model expression of the preceding vehicle speed effect is: ； ； Where, It indicates the vehicle's estimate of the preceding vehicle's speed at the next moment based on the information received from the preceding vehicle. Indicates the maximum speed; To ensure safety, the vehicle speed is not allowed to reach the maximum speed, so it is reduced by 1; represents the speed of the preceding vehicle with respect to the time step; Indicates the acceleration of the preceding vehicle; Indicates that the vehicle's perception distance cannot be lower than 0; Indicates the safe perception distance of the ego vehicle; Indicates the safe speed of the vehicle ahead; Indicates the safe distance to the vehicle ahead; Indicates the distance between the vehicle in front and the vehicle in front of it.

4. The method for controlling mixed traffic flow of CAV and HV vehicles according to claim 1, characterized in that: The rules for applying the safety perception distances of CAVs and HVs obtained from the preceding vehicle speed effect model to the simulation cellular automaton model include: Acceleration rules: When hour: ； The deceleration rule expression is: hour: ； The uniform speed rule expression is: hour: ； Position update rule expression: ； Where, Indicates the speed of the vehicle in the next time step; Indicates the current speed of the vehicle; They represent the acceleration of the vehicle, the maximum speed of the vehicle, and the safety distance respectively; Indicates the vehicle's safety perception distance Determine based on the type of vehicle; Indicates safe distance; Indicates the vehicle's deceleration; Indicates that the vehicle speed and safety distance are minimized; Represents the coordinates of the vehicle at the next time step; Indicates the coordinate value of the vehicle at this moment.

5. The method for controlling mixed traffic flow of CAV and HV vehicles according to claim 4, characterized in that: The random slowing probability of HV vehicles is also introduced into the simulation cellular automaton model, which is expressed as: ； ； The speed update formula after the vehicle slow start is: ; Where, represents the random slowing-down probability; represents the probability when the vehicle's speed is greater than the preceding vehicle's speed; Indicates the vehicle speed; Indicates the front speed; represents the probability of random slowing down when the vehicle speed is less than that of the preceding vehicle; represents the probability of slowing down when the vehicle speed is 0; Indicates the slowing probability when the vehicle speed is greater than 0.

6. The method for controlling mixed traffic flow of CAV and HV vehicles according to claim 1, characterized in that: Relevant parameters include: CAV vehicles 、 、 、 、 、 ; HV vehicles 、 、 、 、 、 ; Indicates the maximum speed of the vehicle; Indicates the maximum deceleration of the vehicle; Represents the time step code.

7. The method for controlling mixed traffic flow of CAV and HV vehicles according to claim 6, characterized in that: The relevant parameters are obtained by performing principal component analysis and k-means cluster analysis on the data in the public dataset.

8. A traffic flow control system for mixed CAV and HV vehicles, characterized in that: include: Model building module: Build a traffic flow control model that incorporates both the preceding vehicle speed effect and the converged lane-changing strategy; Data acquisition module: obtain relevant parameters of traffic flow control model; Simulation operation module: inputs the relevant parameters obtained by the data acquisition module into the traffic flow control model established by the model construction module, and outputs simulation results describing the traffic control results; The traffic flow control model is based on a simulated cellular automaton model of CAV vehicles. The lane-changing model in the simulated cellular automaton model is replaced with an aggregated lane-changing model. The safety perception distances of CAV vehicles and HV vehicles, obtained based on a pre-built leading vehicle speed effect model, are applied to the rules of the simulated cellular automaton model. The expression of CAV vehicle safety perception distance is: ； Where, Indicates the safety perception distance when the ego vehicle is a CAV vehicle; Indicates the actual physical distance between the vehicle and the preceding vehicle; Indicates the speed of the preceding vehicle as perceived by the vehicle; Indicates the safe distance between the vehicle and the vehicle ahead; represents the time step, Indicates the vehicle number of the vehicle; The expression of the safety perception distance of HV vehicles is: ； Where, Indicates the safe perception distance when the own vehicle is a HV vehicle.

9. 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 a method for controlling mixed traffic flow of CAV and HV vehicles according to any one of claims 1 to 7 are implemented.

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