Confluence area vehicle control method and system in man-machine mixed driving environment

Abstract

The invention provides a confluence area vehicle control method and system in a man-machine mixed driving environment. The method comprises the following steps: collecting traffic data of a main road and an acceleration ramp of a highway confluence area in real time; taking a position at a certain distance from the convergence point at the upstream of the main road in the convergence area as a first control point, and taking a position at a certain distance from the convergence point at the upstream of the acceleration ramp as a second control point; the type combinations of two vehicles passing through the first control point of the main road of the confluence area are judged, and the optimal speed from the second control point of the acceleration ramp to the confluence point is calculated according to the different type combinations; selecting an optimal driving lane for the vehicle according to the traffic data of each lane of the main road; the driving conditions of the HV, the CAV and the CAV fleet are analyzed according to the traffic data collected in real time, the vehicle speeds of the CAV and the CAV fleet are controlled and adjusted, the optimal driving speed of the HV is prompted, and it is ensured that all vehicles can safely and rapidly pass through the confluence area.

Description

Technical Field

[0001] The present invention belongs to the technical field of mixed traffic flow merging area control, and in particular, relates to a merging area vehicle control method and system in a mixed human-machine traffic environment. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the development and popularization of connected and autonomous vehicles (CAVs), as well as the complexity of traffic environment and traffic demand, platooning has gradually become a new driving mode for CAVs, and mixed traffic flow with coexistence of CAVs and human vehicles (HVs) has become an inevitable trend in future traffic operation. In this context, the complexity and dynamic changes of highway merging areas, as key nodes of mixed traffic flow, have brought great challenges to traffic management and safety. In the merging area, due to the significant differences in speed, lane selection and driving strategies between CAV fleets and HVs, there are speed inconsistencies and different lane change timings among various types of vehicles, making it difficult for existing traffic management methods to cope with them. Existing technologies mainly rely on the subjective judgment of drivers and fixed speed limit signs. This static traffic management method is extremely inefficient and prone to safety hazards when facing complex vehicle interactions in the merging area. Therefore, the management of merging areas for new mixed traffic flows requires more flexible dynamic control strategies to improve traffic efficiency and safety.

[0004] In this case, merging control becomes the primary technical challenge. HV drivers have slow reaction speeds and inconsistent driving behaviors between different vehicles, resulting in frequent traffic conflicts during the merging process. CAVs and CAV fleets have stronger coordination capabilities through V2X communication technology, and can exchange information with other vehicles and road infrastructure in real time, and dynamically adjust speed and lane selection. However, existing management methods have failed to fully utilize this advantage of CAVs and CAV fleets. The defect of merging area traffic management is that there is no refined control of the differences between different types of vehicles (CAVs and CAV fleets and HVs) on the main road and ramps, and it is impossible to effectively alleviate vehicle conflicts and congestion during the merging process.

[0005] Secondly, on the basis of merging control, the optimal selection control of the main road lane in the merging area is also crucial. Factors such as vehicle density, lane occupancy rate, and driving speed in different lanes affect the overall traffic efficiency. Existing technologies are usually unable to provide vehicles with the best lane selection suggestions based on real-time lane conditions, resulting in low utilization of main road lanes. Especially during peak traffic hours, vehicles tend to concentrate in one lane, further exacerbating congestion problems.

[0006] In addition, existing speed control methods are difficult to adapt to the complex traffic needs in merging areas. Traditional fixed speed limit signs cannot be flexibly adjusted according to real-time traffic flow, speed differences and vehicle types. Although dynamic speed limits are improved compared to fixed speed limits, they still cannot fully meet the driving needs of different types of vehicles. Especially in merging areas, speed coordination between vehicles is crucial for traffic efficiency and safety. Dynamic control methods based on optimal driving speed can more effectively coordinate the driving needs of different types of vehicles. By collecting and analyzing data such as traffic flow, vehicle status and road conditions in real time, the target speed of the vehicle can be dynamically adjusted to reduce speed differences and optimize the merging time, thereby reducing the risk of congestion and traffic conflicts and improving the overall traffic operation efficiency and safety.

[0007] In summary, current traffic management in merging areas of highways faces many challenges, especially in the new mixed traffic flow environment where CAVs, CAV convoys and HVs coexist. The existing fixed speed limit signs and drivers' subjective decisions are difficult to meet the traffic efficiency and safety requirements of merging areas. Due to the significant differences in vehicle types and driving strategies between CAVs, CAV convoys and HVs, traditional traffic management methods are not flexible enough in merging areas and it is difficult to effectively coordinate the dynamic needs of these different types of vehicles. Especially in the merging process, inaccurate merging timing, unreasonable lane selection and inflexible speed control can easily lead to congestion and traffic conflicts, which are more serious during peak hours. Therefore, for this new mixed traffic flow environment, it is urgent to develop new dynamic control strategies to adapt to the coexistence of CAVs, CAV convoys and HVs, improve the traffic efficiency and safety of merging areas, and avoid the management limitations and potential risks under existing technologies. Summary of the invention

[0008] In order to overcome the deficiencies of the above-mentioned prior art, the present invention provides a method and system for controlling vehicles in a merging area in a mixed human-machine traffic environment. The method and system perform dynamic control according to the differences in vehicle types, including merging control, lane selection and optimal driving speed control according to the differences in vehicle types. The method and system can effectively solve the deficiencies in the management of mixed traffic flow merging areas in the prior art, reduce traffic conflicts, improve traffic efficiency, and improve road space utilization and traffic safety.

[0009] To achieve the above objectives, one or more embodiments of the present invention provide the following technical solutions:

[0010] In a first aspect, a method for controlling vehicles in a merging area in a mixed human-machine traffic environment is disclosed, comprising:

[0011] Step 1: Data collection and location determination step: Real-time collection of traffic data on the main road and acceleration ramp in the merging area of ​​the expressway;

[0012] The position of the vehicle to be controlled is determined based on the collected data. If the vehicle is on the ramp and needs to merge, the process proceeds to step 2, the merging control step; if the vehicle is on the main road lane, the process directly proceeds to step 3, the lane selection step; if the vehicle is already on the optimal driving lane, the process directly proceeds to step 4, the optimal driving speed control step;

[0013] Step 2: merging control step: a position a certain distance upstream from the merging point on the main road of the merging area is used as the first control point, and a position a certain distance upstream from the merging point on the acceleration ramp is used as the second control point;

[0014] Determine the type combination of the two vehicles before and after the first control point on the main road of the merging area, calculate the optimal speed from the second control point of the acceleration ramp to the merging point according to different type combinations, and the vehicle to be controlled merges and changes lanes on the acceleration ramp according to the optimal speed from the second control point to the merging point;

[0015] Step 3: Lane selection step: After the controlled vehicle enters the main road, the optimal driving lane is selected for the vehicle according to the traffic data of each lane of the main road;

[0016] Step 4: Optimal driving speed control step: After the controlled vehicle enters the optimal driving lane, the driving conditions of the HV, CAV and CAV fleet are analyzed according to the real-time collected traffic data, the speed of the CAV and CAV fleet is controlled and adjusted, and the optimal driving speed of the HV is prompted.

[0017] The above-mentioned merging control, lane selection and optimal driving speed together constitute the control scheme proposed by the present invention. When the traffic environment in which the vehicle is driving meets one or several of the three starting conditions, the system automatically starts one or several controls in the control system.

[0018] As a further technical solution, the real-time collection of traffic data at the main road and acceleration ramp of the expressway merging area includes at least: vehicle speed, vehicle passing time, vehicle acceleration, traffic density, traffic flow and vehicle type.

[0019] As a further technical solution, the combination of the types of two vehicles before and after the first control point of the main road of the merging area is determined, wherein, if the first vehicle passing the first control point of the main road of the merging area is a manually driven vehicle and the second vehicle is a manually driven vehicle, the travel time of the first vehicle from the first control point to the merging point is calculated by updating the acceleration in real time according to a certain time step;

[0020] According to the travel time of the first vehicle from the first control point to the merging point, calculate the distance traveled by the second vehicle during this time;

[0021] Compare the distance between the first car on the main road and the second car on the main road with the safe distance required to merge with the first car on the ramp;

[0022] If the distance between the first car on the main road and the second car on the main road is greater than the safety distance of the first car on the ramp, they can merge. If the distance is less than the safety distance, they cannot merge at this time.

[0023] As a further technical solution, the acceleration is updated in real time according to a certain time step to calculate the travel time of the first vehicle from the first control point to the merging point. The specific steps are as follows:

[0024] The speed of the first vehicle passing the first control point on the main road is taken as the initial speed, the first control point is taken as the starting position, and the acceleration passing the starting position is taken as the starting acceleration. In each time step, the speed of the vehicle is updated according to the current acceleration;

[0025] Assuming that the acceleration remains constant within the time step, the vehicle velocity update formula is obtained;

[0026] In the same time step, the displacement of the vehicle will also change according to the velocity and acceleration, and the updated formula of displacement is obtained;

[0027] In the process from the first control point to the confluence point, the above speed and displacement update formula is continuously iterated until the displacement of the vehicle reaches the total distance from the first control point to the confluence point;

[0028] The travel time of the first vehicle on the main road from the first control point to the merging point is the accumulation of each time step.

[0029] As a further technical solution, it is determined whether the distance between the second vehicle on the main road and the first vehicle on the main road can meet the merging requirements. If so, the average driving speed of the first vehicle on the ramp from the second control point to the merging point is calculated based on the driving time of the first vehicle on the main road from the first control point to the merging point. If not, the second vehicle on the main road is taken as the initial vehicle, and the speed update formula and the displacement update formula are used to calculate the distance between the second vehicle and the vehicle behind it.

[0030] As a further technical solution, the combination of the types of two vehicles before and after the first control point of the main road of the merging area is determined. If the first vehicle passing the first control point of the main road of the merging area is a manually driven vehicle, and the second vehicle is an intelligent networked vehicle or a fleet of intelligent networked vehicles, the acceleration is updated in real time at a certain time step to calculate the travel time of the first vehicle from the first control point to the merging point;

[0031] If you want the first car on the ramp to merge safely, the distance between the first car on the main road and the second car on the main road must be greater than the safe merging gap of the first car on the ramp. The position of the second car on the main road at this time is calculated as: the distance between the first control point on the main road and the merging point minus the safe merging gap of the first car on the ramp;

[0032] When the distance between the second vehicle on the main road and the first vehicle meets the merging gap condition of the first vehicle on the ramp, the required average speed of the second vehicle on the main road is calculated;

[0033] Then, the average driving speed required for the first vehicle to merge on the ramp is calculated according to the driving time of the first vehicle from the first control point to the merging point.

[0034] As a further technical solution, the combination of the types of two vehicles before and after the first control point on the main road of the merging area is determined, wherein if the first vehicle passing the first control point on the main road of the merging area is an intelligent networked vehicle or an intelligent networked vehicle fleet, and the second vehicle is a manually driven vehicle;

[0035] According to the minimum merging gap of the first car on the ramp, calculate the maximum travel distance of the second car on the main road in order to allow the first car on the ramp to merge smoothly. The point with the maximum travel distance is recorded as point C, and the distance from the maximum travel distance to the first control point is recorded as X. 最远 , X 最远 =X 控制 -s safe Then, the acceleration is updated in real time according to a certain time step to calculate the driving time of the second vehicle on the main road from the first control point to point C. The speed and position changes of the second vehicle on the main road from the first control point to point C are calculated step by step until it reaches point C. At this time, the driving time t total , according to t total Calculate the optimal driving speeds of the first car on the ramp and the first car on the main road so that the first car on the ramp can merge smoothly.

[0036] As a further technical solution, the combination of the types of two vehicles before and after the first control point on the main road of the merging area is determined, wherein if the first vehicle passing the first control point on the main road of the merging area is an intelligent connected vehicle or an intelligent connected vehicle fleet, and the second vehicle is an intelligent connected vehicle or an intelligent connected vehicle fleet;

[0037] First, calculate the time t required for the first vehicle on the main road to reach the merging point at a certain average speed total ;

[0038] If you want the first car on the ramp to merge safely, the distance between the first car on the main road and the second car on the main road needs to be greater than the safe merging gap of the first car on the ramp. Calculate the position of the second car on the main road at this time.

[0039] When the distance between the second vehicle on the main road and the first vehicle meets the merging gap condition of the first vehicle on the ramp, the required average speed of the second vehicle on the main road is calculated;

[0040] According to t total Calculate the average speed required for the first vehicle on the ramp to merge smoothly.

[0041] As a further technical solution, when selecting the optimal driving lane for the vehicle based on the traffic data of each lane of the main road, the set indicators include: lane density, lane occupancy rate, speed conditions;

[0042] Based on the above indicators, a multi-objective optimization algorithm is established, each lane is evaluated based on the multi-objective optimization algorithm, and the optimal driving lane is recommended for the vehicle based on the evaluation results.

[0043] In a second aspect, a vehicle control system for a merging area in a mixed human-machine traffic environment is disclosed, comprising:

[0044] The data collection and location determination module is configured to: collect traffic data on the main road and acceleration ramp of the expressway merging area in real time;

[0045] The position of the vehicle to be controlled is determined based on the collected data. If the vehicle is on the ramp and needs to merge, the merging control module is entered. If the vehicle is on the main road lane, the lane selection module is directly executed. If the vehicle is already in the optimal driving lane, the optimal driving speed control module is directly executed.

[0046] The merging control module is configured to: use a position a certain distance from the merging point upstream of the main road of the merging area as a first control point, and a position a certain distance from the merging point upstream of the acceleration ramp as a second control point;

[0047] Determine the type combination of the two vehicles before and after the first control point on the main road of the merging area, calculate the optimal speed from the second control point of the acceleration ramp to the merging point according to different type combinations, and the vehicle to be controlled merges and changes lanes on the acceleration ramp according to the optimal speed from the second control point to the merging point;

[0048] The lane selection module is configured to: after the controlled vehicle enters the main road, select the optimal driving lane for the vehicle according to the traffic data of each lane of the main road;

[0049] The optimal driving speed control module is configured as follows: after the controlled vehicle enters the optimal driving lane, the driving conditions of the HV, CAV and CAV fleet are analyzed according to the real-time collected traffic data, the speed of the CAV and CAV fleet is controlled and adjusted, and the optimal driving speed of the HV is prompted.

[0050] One or more of the above technical solutions have the following beneficial effects:

[0051] The technical solution of the present invention is equipped with a lane selection module, which dynamically selects the optimal lane for the vehicle according to the density, occupancy rate and speed conditions of each lane, thereby improving the lane utilization rate of the main road in the merging area. At the same time, the optimal driving speed control module automatically adjusts the driving speed of the three types of vehicles according to the traffic flow and traffic density in the merging area to ensure that CAV, CAV fleet and HV can coordinate operation under different traffic conditions, further improving traffic safety and fluidity.

[0052] The technical solution of the present invention monitors the traffic conditions in the merging area in real time and performs dynamic speed control according to the vehicle type (CAV and CAV fleet or HV). For CAV and CAV fleet, the present invention directly sends speed and lane adjustment instructions to ensure that the CAV fleet can optimize the merging speed and reduce the speed difference and conflict with the HV according to the changes in real-time traffic flow. For HV, the present invention helps the driver make reasonable decisions through dynamic recommended speed signs and lane selection instructions to improve the overall merging efficiency.

[0053] Through the comprehensive application of the merging control, lane selection control and optimal driving speed control of the technical solution of the present invention, the deficiencies in the management of mixed traffic flow merging areas in the prior art can be effectively solved, traffic conflicts can be reduced, traffic efficiency can be improved, and the space utilization and traffic safety of roads can be improved.

[0054] Advantages of additional aspects of the present invention will be given in part in the following description, and in part will become obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The accompanying drawings in the specification, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0056] Figure 1 Schematic diagram of various points in the confluence area according to an embodiment of the present invention;

[0057] Figure 2 This is a schematic diagram of confluence control data collection according to an embodiment of the present invention;

[0058] Figure 3 This is a schematic diagram of confluence control according to an embodiment of the present invention;

[0059] Figure 4 A schematic diagram of lane selection control according to an embodiment of the present invention;

[0060] Figure 5 This is a schematic diagram of the optimal driving speed according to an embodiment of the present invention;

[0061] Figure 6 1 is a system block diagram of an embodiment of the present invention. DETAILED DESCRIPTION

[0062] It should be noted that the following detailed descriptions are exemplary and are intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which the present invention belongs.

[0063] It should be noted that the terms used herein are for describing specific embodiments only and are not intended to be limiting of exemplary embodiments according to the present invention.

[0064] In the absence of conflict, the embodiments of the present invention and the features of the embodiments may be combined with each other.

[0065] Glossary:

[0066] V2X is the abbreviation of Vehicle-to-Everything in Chinese, which means "vehicle-to-everything communication", including V2V (vehicle-to-vehicle communication), V2I (vehicle-to-infrastructure communication), V2P (vehicle-to-pedestrian communication), and V2N (vehicle-to-network communication); CAV obtains the current traffic environment conditions through vehicle-to-vehicle technology and receives system control information through vehicle-to-vehicle technology.

[0067] Through sensors and V2X communication technology, the traffic conditions in the merging area are monitored in real time, and dynamic speed control is performed according to the vehicle type (CAV and CAV fleet or HV). For CAV and CAV fleet, the present invention directly sends speed and lane adjustment instructions through V2X technology to ensure that the CAV fleet can optimize the merging speed and reduce the speed difference and conflict with HV according to the changes in real-time traffic flow.

[0068] Embodiment 1

[0069] This embodiment discloses a method for controlling vehicles in a merging area in a mixed human-machine traffic environment, comprising the following steps:

[0070] Step 1: Data collection and location determination steps:

[0071] See attached Figure 2 As shown in FIG, a considerable number of sensors (such as lidar, cameras, traffic induction coils, etc.) are arranged on the main road and acceleration ramp of the highway merging area to collect traffic data in real time, including vehicle speed (v), vehicle passing time, vehicle acceleration, traffic density (ρ), traffic flow (q), vehicle type and other information.

[0072] The position of the vehicle to be controlled is determined based on the collected data. If the vehicle is on the ramp and needs to merge, the process proceeds to step 2, the merging control step; if the vehicle is on the main road lane, the process directly proceeds to step 3, the lane selection step; if the vehicle is already on the optimal driving lane, the process directly proceeds to step 4, the optimal driving speed control step;

[0073] See attached Figure 1 As shown in the figure, the intersection point of the main road and the acceleration ramp is taken as the merging point, recorded as point O; the upstream distance of the main road in the merging area from the merging point is X. 控制 The position of the first car Car 主路1 The time when the vehicle passes through point A is the start time of the entire control system, recorded as time t1, and the speed v when the vehicle passes through point A is recorded at the same time. A1 , acceleration a A1 , and determine the vehicle type; record the first vehicle Car that passes the acceleration ramp control point B at the beginning of the control process 匝道1 , record the time t2 and driving speed v B1 and acceleration a B1 ; Record the second car Car passing through control point A 主路2 The passing time t3 and the driving speed v B1 and acceleration a B1 And determine the vehicle type.

[0074] Step 2: Confluence control steps: see attached Figure 3 As shown:

[0075] 2-1): Car 主路1 For HV, Car 主路2 Also when HV;

[0076] It is proposed to use the IDM model to update the acceleration in real time at a certain time step to calculate Car 主路1 The driving time from point A to point O cannot be directly calculated using traditional kinematic formulas because the acceleration changes in real time. Therefore, we choose to use numerical integration methods, such as the Euler method, with a certain time step, which can be set to a small value, such as 0.1 seconds. In each set time step, the IDM model is used to update the vehicle's position and speed. In this case, the vehicle Car is first updated step by step. 主路1 The speed and position change from point A to point O until reaching point O, at which point the travel time t is obtained total Then, the Car 主路2 The driving distance between the two cars is calculated as ΔX 1-2 , determine the Car on the acceleration ramp 匝道1Can the flow merge safely in this gap? If the merging gap condition is met, then according to the above time t total Calculate Car 匝道1 Optimal driving speed (to make Car 主路1 When arriving at point O, Car 匝道1 It happens to also reach point O, based on this critical point, it just arrives but does not collide); Car 匝道1 For CAV and CAV convoy, control is directly carried out through V2I in V2X; if it is HV, multiple reminders are given through prompt signs to successfully complete the control process this time; if the merging gap condition is not met, the distance between the second and third vehicles will continue to be calculated, and it will be determined whether the vehicles on the acceleration ramp can merge safely in this gap, and the optimal driving speed will be calculated.

[0077] More specifically, the following is a detailed calculation process using the IDM model:

[0078] 2-1-1) First calculate Car 主路1 The travel time from point A to point O is t total :

[0079] Acceleration update formula:

[0080] Among them: a IDM (t) is the acceleration calculated according to the IDM model at time t, a max is the maximum acceleration; v(t) is the speed of the vehicle at time t, v0 is the expected speed, and δ is the acceleration exponent, which is usually 4.

[0081]

[0082] Δv is the relative speed, that is, the speed difference between the current vehicle and the vehicle in front, T is the reaction time, s0 is the minimum vehicle distance, b is the comfortable deceleration; s(t) is the following distance of the current vehicle at time t, which is the expected safe vehicle distance.

[0083] Car 主路1 The speed passing through point A is the initial speed, point A is the starting position, and the acceleration passing through point A is the starting acceleration. In each time step Δt, the vehicle's speed is updated according to the current acceleration a(t). Assuming that the acceleration remains unchanged within Δt, the vehicle's speed update formula is:

[0084] v(t+Δt)=v(t)+a IDM (t)·Δt

[0085] Where: v(t) is the velocity at time t; a IDM (t) is the acceleration at time t, calculated by the IDM model.

[0086] In the same time step Δt, the vehicle's displacement x(t) will also change according to the speed and acceleration. The displacement update formula is:

[0087]

[0088] Where: x(t) is the position of the vehicle at time t; v(t) and a IDM (t) are the velocity and acceleration at time t respectively.

[0089] During the entire process from point A to point O, the above speed and displacement update formula is continuously iterated until the vehicle's displacement reaches the total distance X from point A to point O. 控制 .

[0090] At each time step Δt, the following steps are used to update:

[0091] 1. Acceleration update: Calculate the vehicle's current acceleration a based on the IDM model IDM (t);

[0092] 2. Speed ​​update: Use the speed update formula to calculate the vehicle's speed v(t+Δt) at the next time step;

[0093] 3. Position update: Use the displacement update formula to calculate the vehicle's displacement x(t+Δt) at the next time step;

[0094] 4. Determine whether it has reached point O: If x(t+Δt)≥X 控制 , then stop the iteration, otherwise continue;

[0095] Total driving time t total It is the accumulation of each time step Δt until the current position x(t) of the vehicle reaches the position X at point O. 控制 :

[0096] t total =n·Δt

[0097] Where n is the number of iterations.

[0098] Later, according to Car 主路1 The travel time t total , calculate Car 主路2 Distance travelled during this time:

[0099] According to the above formula, the time t can be calculated total +t1, Car 主路2 The location (i.e. using the IDM model from Car 主路2 Starting from the time t3 when the control point is passed, to t total +t1 Stop Car 主路2Distance travelled):

[0100]

[0101] Then calculate Car 主路1 With Car 主路2 Distance between:

[0102]

[0103] Finally compare Car 主路1 With Car 主路2 The distance between 匝道1 The safe distance required for merging:

[0104] If Δs ≥ s safe , then the opportunity can merge, and according to t total Calculate Car 匝道1 Average driving speed required for merging:

[0105]

[0106] If Δs<s safe , then the opportunity cannot merge, with Car 主路2 Calculate Car as the initial vehicle 主路2 The distance between the vehicle behind and the vehicle behind, if the vehicle behind is a HV, that is, Car 主路1 For HV, Car 主路2 If the following vehicle is also an HV, then completely repeat the above step 2-1); if the following vehicle is a CAV or a CAV convoy, refer to the second control case, i.e., step 2-2).

[0107] 2-2).Car 主路1 For HV, Car 主路2 For CAV or CAV fleet

[0108] Combined with the IDM model, the acceleration is updated in real time at a certain time step to calculate Car 主路1 The driving time from point A to point O cannot be directly calculated using traditional kinematic formulas because the acceleration changes in real time. Therefore, we choose to use numerical integration methods, such as the Euler method, with a certain time step, which can be set to a small value, such as 0.1 seconds. In each set time step, the IDM model is used to update the vehicle's position and speed. In this case, the vehicle Car is first updated step by step. 主路1 The speed and position change from point A to point O until reaching point O, at which point the travel time t is obtained total ;

[0109] According to Car 匝道1The minimum merging gap is calculated to make Car 匝道1 Car 主路2 The longest driving distance is recorded as point C, and the distance from point A to the longest driving distance is recorded as X 最远 (X 最远 =X 控制 -s safe ), so that Car 匝道1 can merge smoothly; according to t total Calculate Car 匝道1 Optimal driving speed (to make Car 主路1 When arriving at point O, Car 匝道1 Coincidentally, it also arrives at point O; Car 匝道1 For CAV and CAV fleet, it is directly controlled through V2X, and for HV, it is reminded multiple times through the sign);

[0110] Calculate t total The method is the same as in step 2-1).

[0111] If you want to make Car 匝道1 Can merge safely, then Car 主路1 With Car 主路2 The distance between them needs to be greater than Car 匝道1 The safe confluence gap is Δs>s safe , calculate Car 主路2 Location:

[0112] X 最远 =X 控制 -s safe

[0113] According to Car 主路2 Position X 最远 ,t total Calculate the Car 匝道1 When the merging condition occurs, Car 主路2 Required average driving speed:

[0114]

[0115] Then according to t total Calculate Car 匝道1 Average driving speed required for merging:

[0116]

[0117] 2-3)Car 主路1 For CAV or CAV fleet, Car 主路2 When HV:

[0118] First, according to Car匝道1 The minimum merging gap is calculated to make Car 匝道1 Car 主路2 The longest driving distance is recorded as point C, and the distance from point A to the longest driving distance is recorded as X 最远 (X 最远 =X 控制 -s safe ), and then use the IDM model to update the acceleration in real time at a certain time step to calculate Car 主路2 The driving time from point A to point C cannot be directly calculated using traditional kinematic formulas because the acceleration changes in real time. Therefore, numerical integration methods, such as the Euler method, are used to calculate the vehicle Car step by step using a certain time step, which can be set to a small value, such as 0.1 seconds. 主路2 The speed and position change from point A to point C until reaching point C, at which point the travel time t is obtained total , according to t total Calculate Car 匝道1 and Car 主路1 Optimal driving speed (to make Car 主路2 When reaching point C, Car 匝道1 and Car 主路1 Just arrived at point O; Car 匝道1 For CAV and CAV fleet, it is directly controlled by V2X, and for HV, it is reminded multiple times through the reminder sign). 匝道1 Can merge smoothly.

[0119] The same method as the first case is used to calculate the vehicle Car 主路2 The time from point A to point C total The difference is that this time with Car 主路2 The time when Car passes point A is the starting time. 主路2 The speed and acceleration passing through point A are the initial speed and initial acceleration respectively. In addition, in step update, "4. Determine whether it has reached point O: If x(t+Δt)≥X 控制 , then stop the iteration, otherwise continue" is changed to "4. Determine whether it reaches point C: If x(t+Δt)≥X 最远 , then stop the iteration, otherwise continue"; then according to t total Calculate Car 主路1 Average driving speed:

[0120]

[0121] Then according to t total Calculate Car 匝道1 Average driving speed required for merging:

[0122]

[0123] 2-4)Car 主路1 For CAV or CAV fleet, Car 主路2 Also for CAV or CAV fleet

[0124] Now assume that Car 主路1 Arriving at point O at a certain average speed, calculate t according to the formula total ; Calculate the amount of time it takes to make Car 匝道1 Car 主路2 The longest driving distance is recorded as point C, and the distance from point A to the longest driving distance is recorded as X 最远 (X 最远 =X 控制 -s safe ), according to t total Calculate Car 主路2 Optimum driving speed; according to t total Calculate Car 匝道1 Optimal driving speed (to make Car 主路1 When arriving at point O, Car 匝道1 Coincidentally, it also arrives at point O; Car 匝道1 For CAV and CAV fleet, it is directly controlled by V2X. For HV, it is reminded multiple times through the reminder sign. 匝道1 Can merge smoothly.

[0125] First calculate Car 主路1 The time t required to reach point O at a certain average speed total :

[0126]

[0127] If you want to make Car 匝道1 Can merge safely, then Car 主路1 With Car 主路2 The distance between them needs to be greater than Car 匝道1 The safe confluence gap is Δs>s safe , calculate Car 主路2 Location:

[0128] X 最远 =X 控制 -s safe

[0129] According to Car 主路2 Position X 最远 ,t total Calculate the Car 匝道1 When the merging condition occurs, Car 主路2Required average driving speed:

[0130]

[0131] According to t total Calculate Car 匝道1 The average speed required to merge smoothly:

[0132]

[0133] Step 3: Lane selection steps: See attached Figure 4 As shown:

[0134] This step aims to dynamically select the best driving lane for the vehicle based on the real-time traffic conditions of each lane on the main road collected in step 1 (including lane density, lane occupancy, speed, etc.), optimize the distribution of vehicles in the merging area, reduce traffic congestion and improve lane utilization. Specifically, this step determines whether the vehicle needs to change lanes on the main road based on real-time data. Especially for CAVs and CAV fleets, lane selection can be automatically performed through V2X technology, and HV prompts the driver to select the best lane.

[0135] Lane selection is based on lane density, lane occupancy and lane speed conditions:

[0136] 1. Lane density ρ lane : Indicates the number of vehicles per unit length. The higher the density, the more congested the lane is and the less suitable it is to enter.

[0137]

[0138] Where N is the number of vehicles in the lanes within the study area, L lane is the length of lanes in the study area.

[0139] 2. Lane occupancy rate Occ lane : It is a ratio between the space occupied by vehicles in a lane and the total available space in the lane, which is used to evaluate the congestion of the lane.

[0140]

[0141] Among them, Occ lane is the lane occupancy rate, ranging from 0 to 1, N is the number of vehicles in the lane in the study area, L vehiclei is the occupied length of the i-th vehicle, L lane is the length of lanes in the study area.

[0142] 3. Speed ​​status AvgSpeed lane: The lane's smoothness is assessed by calculating the average speed of all vehicles on the lane. The faster the vehicle speed, the smoother the lane.

[0143]

[0144] Among them, AvgSpeed lane is the average speed of vehicles on the lane, N is the number of vehicles in the lane in the study area, v i is the speed of the ith vehicle.

[0145] Based on the above indicators, a multi-objective optimization algorithm is used to evaluate each lane and recommend the best driving lane for the vehicle. The decision-making process uses a weighted scoring method to score each lane, and the lane with a higher score is considered more suitable for the vehicle to enter:

[0146]

[0147] Among them, ω1, ω2, and ω3 are the weights of each parameter and can be adjusted according to actual needs.

[0148] For each vehicle or fleet, the system will recommend the lane with the highest score as its driving lane, and notify the driver through V2X technology or signs or automatically perform lane change operations.

[0149] Step 4: Optimal driving speed control steps: See attached Figure 5 As shown:

[0150] According to the data collected by the sensors in step 1, the driving conditions of the HV, CAV and CAV convoy are analyzed, the speeds of the CAV and CAV convoy are controlled and adjusted, and the HV is prompted with the optimal driving speed to ensure that the vehicle can pass through the merging area safely and quickly.

[0151] Use the following formula to determine whether the current driving speed needs to be optimized:

[0152]

[0153] J limit is the speed optimization judgment index. When it exceeds a certain set threshold, the system determines that the current speed needs to be optimized. ρ is the real-time traffic density, ρ max is the maximum allowed density. q is the real-time traffic flow, q max is the maximum flow rate. avg is the real-time average speed in the merging area, v free is the maximum speed in free flow conditions. is the weight, reflecting the impact of different factors on driving speed decision.

[0154] When J limitWhen the set threshold is exceeded, the system determines that the driving speed needs to be optimized.

[0155] The optimal driving speed control dynamically calculates and adjusts the speed of CAV and CAV fleet according to the driving status, traffic density and vehicle type of HV, CAV and CAV fleet, and gives the optimal driving speed prompt for HV. The calculation can calculate the optimal driving speed of CAV and CAV fleet according to the real-time traffic conditions. This speed can improve the overall traffic efficiency of the merging area and reduce traffic risks. In addition, the system also provides reasonable driving suggestions for HVs through dynamic signs and instructions. Since the optimal driving speeds calculated between different types of vehicles are inconsistent, in order to ensure the safety of driving between mixed vehicles in the merging area, the optimal driving speed of CAV is used as the benchmark, and the difference between the optimal driving speed of other types of vehicles and the optimal driving speed of CAV shall not exceed 5km / h.

[0156] 1. Optimal driving speed for CAVs

[0157] The optimal driving speed of a single CAV is based on traffic density and traffic flow, but is not affected by the size of the CAV fleet and the driver's reaction time. The calculation formula is as follows:

[0158]

[0159] 2. Optimal driving speed for CAV fleets

[0160] The optimal speed of a CAV fleet should consider not only traffic density and flow, but also the fleet size N. CAV Larger CAV convoys need to maintain lower speeds to ensure safe distances between vehicles.

[0161]

[0162] When |v CAV -v CAV车队 |≥5, v CAV车队 =v CAV -5, otherwise the calculated value is used directly.

[0163] 3. Optimal driving speed for HVs

[0164] The optimal driving speed control of HV vehicles should not only consider the traffic density and flow rate, but also the driver's reaction time t reaction-HV Make adjustments so that the reaction time is longer and the driving speed is lower.

[0165]

[0166] When |v CAV -v HV |≥5, v HV =vCAV -5, and the calculated value is used directly in other cases. The technical solution of the present invention significantly improves the driving efficiency and safety of different types of vehicles in the merging area through the proposed merging control, lane selection optimization and optimal driving speed management system. Compared with traditional traffic management methods, the use of V2X technology realizes dynamic and real-time control, solves many problems of existing technologies in merging area traffic management, and the specific beneficial effects are as follows:

[0167] Regarding merging control: For mixed traffic flow merging areas, the speed and merging timing of CAVs and CAV convoys are dynamically adjusted, and precise control of CAVs and CAV convoys is achieved through V2X technology to ensure that CAVs and CAV convoys can merge smoothly and safely under different types of merging gap conditions, reducing conflicts with HVs. At the same time, for HVs on ramps, dynamic recommended speed signs are set to prompt drivers to drive at a certain speed to quickly and safely merge.

[0168] Optimal lane selection control: Provides real-time lane selection suggestions for CAVs, CAV fleets, and HV vehicles. Based on lane density, speed conditions, occupancy rate, and other data, it optimizes vehicle lane selection, improves space utilization of main road lanes, and avoids traffic bottlenecks caused by unreasonable lane selection.

[0169] Optimal driving speed control management: According to different traffic conditions, the driving speed of CAVs, CAV fleets and HV vehicles is adjusted in real time to ensure that the speed differences of various types of vehicles are controlled during the merging process, reducing the risks and congestion problems caused by speed inconsistency.

[0170] Improvement of overall traffic fluidity and safety: Through the collaborative work of merging control, lane selection and optimal driving speed control modules, the present invention greatly improves the traffic management capabilities of the merging area. The system dynamically adjusts the driving behavior of CAVs, CAV fleets and HV vehicles by calculating the speed, lane conditions and traffic density of the vehicles in real time, avoiding traffic accidents caused by speed differences and unreasonable lane selection. In high-traffic conditions, the system can effectively optimize the operation of vehicles in the merging area, reduce the decision-making burden of the driver, and ensure smooth traffic and safety in the entire merging area.

[0171] More specifically, the technical solution of this embodiment can improve the accuracy and coordination of merging control: in the merging area, the existing static signs and the subjective judgment of the driver cannot flexibly adapt to the complex traffic conditions in the merging area, especially in the mixed traffic flow environment where CAVs, CAV fleets and HVs coexist, which can easily cause traffic conflicts and congestion. The present invention uses a merging control module to make refined speed and merging timing adjustments for CAVs and CAV fleets based on real-time collected data such as vehicle speed, acceleration, and vehicle distance, and uses V2X communication technology. At the same time, it provides the best recommended speed to HVs through variable signs to ensure that HVs, CAVs and CAV fleets can merge smoothly and safely under different types of merging gaps. The design of this module can reduce the problem of merging failures caused by speed differences or different reaction times between CAVs, CAV fleets and HVs, and effectively avoid safety hazards caused by human decision-making errors.

[0172] Optimize lane utilization and improve main road traffic efficiency: In the prior art, drivers usually choose lanes based on personal experience, which often leads to excessive congestion in a certain lane and limits the traffic capacity of the entire main road. The lane selection control scheme proposed in the technical solution of the present invention monitors lane density, occupancy rate and vehicle speed in real time, combines intelligent algorithms to provide optimal lane selection suggestions to HV vehicles through variable signs, and directly controls CAVs and CAV fleets to change lanes to the optimal lane. This module makes reasonable allocations based on real-time lane conditions, avoids waste of lane resources, and prevents the formation of traffic bottlenecks. Especially during high-traffic periods, by dynamically adjusting the vehicle's lane selection, the utilization rate of the main road lanes is improved, and a more balanced lane flow distribution is achieved.

[0173] Enhanced flexibility of optimal driving speed reduces traffic conflicts: Traditional fixed speed limit signs cannot flexibly respond to real-time traffic flow changes in the merging area, resulting in speed differences between different vehicles exacerbating traffic conflicts. The optimal driving speed solution of the present invention dynamically adjusts the driving speed of CAVs, CAV fleets and HVs through real-time calculations based on real-time traffic flow, traffic density, average speed and vehicle type. The system can provide real-time driving speed recommendations based on the needs of different types of vehicles, so that CAVs, CAV fleets and HV vehicles can operate in a coordinated and consistent manner in the merging area. This flexible speed adjustment reduces traffic conflicts caused by speed differences and improves the smoothness of traffic in the merging area.

[0174] Embodiment 2

[0175] The purpose of this embodiment is to provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the above method when executing the program.

[0176] Embodiment 3

[0177] The purpose of this embodiment is to provide a computer-readable storage medium.

[0178] A computer-readable storage medium stores a computer program, which executes the steps of the above method when executed by a processor.

[0179] Embodiment 4

[0180] See attached Figure 6 As shown, the purpose of this embodiment is to provide a vehicle control system in a merging area in a mixed human-machine traffic environment, including:

[0181] The data collection and location determination module is configured to: collect traffic data on the main road and acceleration ramp of the expressway merging area in real time;

[0182] The position of the vehicle to be controlled is determined based on the collected data. If the vehicle is on a ramp and needs to merge, the merging control module is entered. If the vehicle is on the main road for merging, the lane selection module is directly executed. If the vehicle is already in the optimal driving lane, the optimal driving speed control module is directly executed.

[0183] The merging control module is configured to: use a position a certain distance from the merging point upstream of the main road of the merging area as a first control point, and a position a certain distance from the merging point upstream of the acceleration ramp as a second control point;

[0184] Determine the type combination of the two vehicles before and after the first control point on the main road of the merging area, calculate the optimal speed from the second control point of the acceleration ramp to the merging point according to different type combinations, and the vehicle to be controlled merges and changes lanes on the acceleration ramp according to the optimal speed from the second control point to the merging point;

[0185] The lane selection module is configured to: after the controlled vehicle enters the main road, select the optimal driving lane for the vehicle according to the traffic data of each lane of the main road;

[0186] The optimal driving speed control module is configured as follows: after the controlled vehicle enters the optimal driving lane, the driving conditions of the HV, CAV and CAV fleet are analyzed according to the real-time collected traffic data, the speed of the CAV and CAV fleet is controlled and adjusted, and the optimal driving speed of the HV is prompted.

[0187] Embodiment 5

[0188] The purpose of this embodiment is to provide a computer program product containing instructions, which, when running on a computer, enables the computer to execute the methods and functions involved in any of the above embodiments.

[0189] The steps involved in the apparatus of the above embodiment correspond to the method embodiment 1, and the specific implementation method can refer to the relevant description part of embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood to include any medium that can store, encode or carry an instruction set for execution by a processor and enable the processor to execute any method in the present invention.

[0190] Those skilled in the art should understand that the modules or steps of the present invention described above can be implemented by a general-purpose computer device, or alternatively, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0191] Although the above describes the specific implementation mode of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without creative work are still within the scope of protection of the present invention.

Claims

1. A vehicle control method in a merging area in a human-machine mixed traffic environment, characterized in that: include: Step 1: Data collection and location determination step: Real-time collection of traffic data on the main road and acceleration ramp in the merging area of ​​the expressway; The position of the vehicle to be controlled is determined based on the collected data. If the vehicle is on the ramp and needs to merge, the process proceeds to step 2, the merging control step; if the vehicle is on the main road lane, the process directly proceeds to step 3, the lane selection step; if the vehicle is already on the optimal driving lane, the process directly proceeds to step 4, the optimal driving speed control step; Step 2: merging control step: a position a certain distance upstream from the merging point on the main road of the merging area is used as the first control point, and a position a certain distance upstream from the merging point on the acceleration ramp is used as the second control point; Determine the type combination of the two vehicles before and after the first control point on the main road of the merging area, calculate the optimal speed from the second control point of the acceleration ramp to the merging point according to different type combinations, and the vehicle to be controlled merges and changes lanes on the acceleration ramp according to the optimal speed from the second control point to the merging point; Step 3: Lane selection step: After the controlled vehicle enters the main road, the optimal driving lane is selected for the vehicle according to the traffic data of each lane of the main road; Step 4: Optimal driving speed control step: After the controlled vehicle enters the optimal driving lane, the driving conditions of the HV, CAV and CAV fleet are analyzed according to the real-time collected traffic data, the speed of the CAV and CAV fleet is controlled and adjusted, and the optimal driving speed of the HV is prompted.

2. The method for controlling vehicles in a merging area in a mixed traffic environment of humans and machines as claimed in claim 1, characterized in that: The real-time collection of traffic data at the main road and acceleration ramp of the expressway merging area includes at least: vehicle speed, vehicle passing time, vehicle acceleration, traffic density, traffic flow and vehicle type.

3. The vehicle control method in a merging area in a human-machine mixed traffic environment as claimed in claim 1, characterized in that: Determine the type combination of two vehicles before and after the first control point of the main road of the merging area. If the first vehicle passing the first control point of the main road of the merging area is a manually driven vehicle and the second vehicle is a manually driven vehicle, calculate the travel time of the first vehicle from the first control point to the merging point by updating the acceleration in real time at a certain time step. According to the travel time of the first vehicle from the first control point to the merging point, calculate the distance traveled by the second vehicle during this time; Compare the distance between the first car on the main road and the second car on the main road with the safe distance required to merge with the first car on the ramp; If the distance between the first car on the main road and the second car on the main road is greater than the safety distance of the first car on the ramp, they can merge. If the distance is less than the safety distance, they cannot merge at this time.

4. The method for controlling vehicles in a merging area in a mixed traffic environment of humans and machines as claimed in claim 3, characterized in that: The acceleration is updated in real time at a certain time step to calculate the travel time of the first vehicle from the first control point to the confluence point. The specific steps are as follows: The speed of the first vehicle passing the first control point on the main road is taken as the initial speed, the first control point is taken as the starting position, and the acceleration passing the starting position is taken as the starting acceleration. In each time step, the speed of the vehicle is updated according to the current acceleration; Assuming that the acceleration remains constant within the time step, the vehicle velocity update formula is obtained; In the same time step, the displacement of the vehicle will also change according to the velocity and acceleration, and the updated formula of displacement is obtained; In the process from the first control point to the confluence point, the above speed and displacement update formula is continuously iterated until the displacement of the vehicle reaches the total distance from the first control point to the confluence point; The travel time of the first vehicle on the main road from the first control point to the merging point is the accumulation of each time step.

5. The method for controlling vehicles in a merging area in a mixed traffic environment of humans and machines as claimed in claim 3, characterized in that: Determine whether the distance between the second vehicle on the main road and the first vehicle on the main road meets the merging requirements. If so, calculate the average speed of the first vehicle on the ramp from the second control point to the merging point based on the travel time of the first vehicle on the main road from the first control point to the merging point. If it is not satisfied, the second vehicle on the main road is taken as the initial vehicle, and the speed update formula and the displacement update formula are used to calculate the distance between the second vehicle and the vehicle behind it.

6. The method for controlling vehicles in a merging area in a mixed traffic environment of humans and machines as claimed in claim 1, characterized in that: Determine the type combination of the two vehicles before and after the first control point on the main road of the merging area. If the first vehicle passing the first control point on the main road of the merging area is a manually driven vehicle and the second vehicle is an intelligent networked vehicle or a fleet of intelligent networked vehicles, calculate the travel time of the first vehicle from the first control point to the merging point by updating the acceleration in real time at a certain time step. If you want the first car on the ramp to merge safely, the distance between the first car on the main road and the second car on the main road must be greater than the safe merging gap of the first car on the ramp. The position of the second car on the main road at this time is calculated as: the distance between the first control point on the main road and the merging point minus the safe merging gap of the first car on the ramp; When the distance between the second vehicle on the main road and the first vehicle meets the merging gap condition of the first vehicle on the ramp, the required average speed of the second vehicle on the main road is calculated; Then, the average driving speed required for the first vehicle to merge on the ramp is calculated according to the driving time of the first vehicle from the first control point to the merging point.

7. The method for controlling vehicles in a merging area in a human-machine mixed traffic environment as claimed in claim 1, characterized in that: Determine the type combination of two vehicles before and after the first control point on the main road of the merging area, where if the first vehicle passing the first control point on the main road of the merging area is an intelligent connected vehicle or an intelligent connected vehicle fleet, and the second vehicle is a manually driven vehicle; According to the minimum merging gap of the first car on the ramp, calculate the maximum travel distance of the second car on the main road in order to allow the first car on the ramp to merge smoothly. The point with the maximum travel distance is recorded as point C, and the distance from the maximum travel distance to the first control point is recorded as X. 最远 , X 最远 =X 控制 -s safe Then, the acceleration is updated in real time according to a certain time step to calculate the driving time of the second vehicle on the main road from the first control point to point C. The speed and position changes of the second vehicle on the main road from the first control point to point C are calculated step by step until it reaches point C. At this time, the driving time t total , according to t total Calculate the optimal driving speeds of the first car on the ramp and the first car on the main road so that the first car on the ramp can merge smoothly.

8. The method for controlling vehicles in a merging area in a human-machine mixed traffic environment as claimed in claim 1, characterized in that: Determine the type combination of two vehicles before and after the first control point on the main road of the merging area, where if the first vehicle passing the first control point on the main road of the merging area is an intelligent connected vehicle or an intelligent connected vehicle fleet, and the second vehicle is an intelligent connected vehicle or an intelligent connected vehicle fleet; First, calculate the time t required for the first vehicle on the main road to reach the merging point at a certain average speed total ; If you want the first car on the ramp to merge safely, the distance between the first car on the main road and the second car on the main road needs to be greater than the safe merging gap of the first car on the ramp. Calculate the position of the second car on the main road at this time. When the distance between the second vehicle on the main road and the first vehicle meets the merging gap condition of the first vehicle on the ramp, the required average speed of the second vehicle on the main road is calculated; According to t total Calculate the average speed required for the first vehicle on the ramp to merge smoothly.

9. A vehicle control system in a merging area in a mixed traffic environment of humans and machines, characterized in that: include: The data collection and location determination module is configured to: collect traffic data on the main road and acceleration ramp of the expressway merging area in real time; The position of the vehicle to be controlled is determined based on the collected data. If the vehicle is on the ramp and needs to merge, the merging control module is entered. If the vehicle is on the main road lane, the lane selection module is directly executed. If the vehicle is already in the optimal driving lane, the optimal driving speed control module is directly executed. The merging control module is configured to: use a position a certain distance from the merging point upstream of the main road of the merging area as a first control point, and a position a certain distance from the merging point upstream of the acceleration ramp as a second control point; Determine the type combination of the two vehicles before and after the first control point on the main road of the merging area, calculate the optimal speed from the second control point of the acceleration ramp to the merging point according to different type combinations, and the vehicle to be controlled merges and changes lanes on the acceleration ramp according to the optimal speed from the second control point to the merging point; The lane selection module is configured to: after the controlled vehicle enters the main road, select the optimal driving lane for the vehicle according to the traffic data of each lane of the main road; The optimal driving speed control module is configured as follows: after the controlled vehicle enters the optimal driving lane, the driving conditions of the HV, CAV and CAV fleet are analyzed according to the real-time collected traffic data, the speed of the CAV and CAV fleet is controlled and adjusted, and the optimal driving speed of the HV is prompted.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method described in any one of claims 1 to 8 are performed.

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