Confluence area traffic management method, computer equipment and computer program product

Abstract

The embodiment of the invention discloses a confluence area traffic management method, computer equipment and a computer program product, and the method comprises the steps: determining the time headway between adjacent vehicles based on the driving information of at least one vehicle in a control area; the control area is an area within a preset length in front of the confluence area, and the driving information represents the current driving state of the corresponding vehicle; grouping the at least one vehicle based on the time headway between the adjacent vehicles to obtain a plurality of vehicle groups; simulating the time when each vehicle in the plurality of vehicle groups arrives at the confluence area based on the driving information of each vehicle in the plurality of vehicle groups, and determining the passing sequence of the plurality of vehicle groups; and controlling the at least one vehicle to run based on the passing sequence of the plurality of vehicle groups so as to merge the at least one vehicle into the converging area. Therefore, the method is not only suitable for pure network connection traffic flow, but also suitable for vehicle merging of entrance ramps of mixed traffic flow, and is wide in application range.

Description

Technical Field

[0001] The present application relates to, but is not limited to, the field of Internet of Things technology, and in particular to a merging area traffic management method, computer equipment, and computer program product. Background Art

[0002] On-ramp merging is a common traffic scenario. Due to the limited foresight and coordination between vehicles on the main road and on the ramp, traffic congestion and even accidents often occur. To improve merging safety and traffic efficiency, coordination and cooperation between vehicles is necessary.

[0003] Related technologies use the driving information of each connected vehicle in a purely connected environment to plan the passage order and movement trajectory of each connected vehicle. However, this method is not applicable to scenarios where connected vehicles and human-driven vehicles coexist, and its applicability is poor. Summary of the Invention

[0004] In view of this, embodiments of the present application at least provide a merging area traffic management method, a computer device, and a computer program product.

[0005] The technical solution of the embodiment of the present application is implemented as follows:

[0006] On the one hand, an embodiment of the present application provides a merging area traffic management method, the merging area traffic management method comprising: determining the headway time between adjacent vehicles based on the driving information of at least one vehicle in a control area; the control area is an area within a preset length located before the merging area, and the driving information represents the current driving status of the corresponding vehicle; grouping the at least one vehicle based on the headway time between the adjacent vehicles to obtain a plurality of vehicle groups; simulating the time for each vehicle in the plurality of vehicle groups to arrive at the merging area based on the driving information of each vehicle in the plurality of vehicle groups, and determining the passage order of the plurality of vehicle groups; controlling the driving of the at least one vehicle based on the passage order of the plurality of vehicle groups to merge the at least one vehicle into the merging area.

[0007] In some embodiments, the grouping of the at least one vehicle based on the headway time between the adjacent vehicles to obtain multiple vehicle groups includes: setting a vehicle grouping strategy that represents the intelligent connected vehicle as the first vehicle in the vehicle group and dividing manually driven vehicles that do not follow the intelligent connected vehicle into a separate group; identifying the at least one vehicle and determining the type of each vehicle; and grouping the at least one vehicle based on the vehicle grouping strategy, the type of each vehicle and the headway time between the adjacent vehicles to obtain the multiple vehicle groups.

[0008] In some embodiments, the grouping of the at least one vehicle based on the vehicle grouping strategy, the type of each vehicle and the headway time between the adjacent vehicles to obtain the multiple vehicle groups includes: numbering the at least one vehicle based on the time when each vehicle enters the control area and the position of each vehicle on the road to obtain the number of each vehicle; when the type of the vehicle numbered i is an intelligent connected vehicle and the headway time between the vehicle numbered i and the vehicle numbered i-1 is less than the grouping threshold, or when the type of the vehicle numbered i is a manually driven vehicle and the type of the vehicle numbered i-1 is an intelligent connected vehicle and the headway time between the vehicle numbered i and the vehicle numbered i-1 is less than the grouping threshold, The vehicle numbered i is divided into the vehicle group to which the vehicle numbered i-1 belongs; i is a positive integer greater than or equal to 2; when the type of the vehicle numbered i is an intelligent connected vehicle and the headway time between the vehicle numbered i and the vehicle numbered i-1 is greater than or equal to the grouping threshold, or when the types of the vehicle numbered i and the vehicle numbered i-1 are both manually driven vehicles, or when the vehicle numbered i is a manually driven vehicle and the type of the vehicle numbered i-1 is an intelligent connected vehicle, and the headway time between the vehicle numbered i and the vehicle numbered i-1 is greater than or equal to the grouping threshold, the vehicle numbered i is divided into a new vehicle group; when i is greater than the total number of vehicles in the control area, the multiple vehicle groups are obtained.

[0009] In some embodiments, the at least one vehicle is numbered based on the time when each vehicle enters the control area and the position of each vehicle on the road to obtain the number of each vehicle, including: determining the distance between each vehicle and the merging area based on the position of each vehicle on the road; and numbering the vehicles in the control area on the ramp first, and then numbering the vehicles in the control area on the main road in order of distance from near to far and time from early to late based on the time when each vehicle enters the control area and the distance between each vehicle and the merging area to obtain the number of each vehicle.

[0010] In some embodiments, the grouping threshold includes a first grouping threshold and a second grouping threshold, the first grouping threshold is the grouping threshold when the types of adjacent vehicles are all intelligent connected vehicles, and the second grouping threshold is the grouping threshold when the types of adjacent vehicles are different; when the type of the vehicle numbered i and the type of the vehicle numbered i-1 are both intelligent connected vehicles, the grouping threshold is the first grouping threshold; when the type of the vehicle numbered i is an intelligent connected vehicle and the type of the vehicle numbered i-1 is a manually driven vehicle, or when the type of the vehicle numbered i is a manually driven vehicle and the type of the vehicle numbered i-1 is an intelligent connected vehicle, the grouping threshold is the second grouping threshold.

[0011] In some embodiments, simulating the time when each vehicle in the multiple vehicle groups arrives at the merging area based on the driving information of each vehicle in the multiple vehicle groups to determine the passage order of the multiple vehicle groups includes: constructing a mathematical model for determining the passage order of the multiple vehicle groups and constraints on the mathematical model based on the driving information of each vehicle in the multiple vehicle groups; solving the mathematical model based on the constraints of the mathematical model to obtain the passage order of the multiple vehicle groups.

[0012] In some embodiments, based on the driving information of each vehicle in the multiple vehicle groups, a mathematical model for determining the passage order of the multiple vehicle groups and constraints for the mathematical model are constructed, including: based on the driving information of each vehicle in the multiple vehicle groups, determining the estimated time for the last vehicle of each vehicle group to arrive at the merging area; based on the estimated time for the last vehicle of each vehicle group to arrive at the merging area, constructing a mathematical model for minimizing vehicle driving time; based on the driving information of each vehicle in the multiple vehicle groups, constructing constraints for the mathematical model.

[0013] In some embodiments, the driving information of the vehicle numbered i includes the position and speed of the vehicle numbered i at the time of collection, the maximum speed and maximum acceleration of the vehicle numbered i; the constraint conditions for the mathematical model are constructed based on the driving information of each vehicle in the plurality of vehicle groups, including: determining a first constraint condition based on the position and speed of the vehicle numbered i at the time of collection, the preset length, and the time of collection; the first constraint condition represents the estimated time for the vehicle numbered i to arrive at the merging area; determining a second constraint condition based on the maximum speed and maximum acceleration of the vehicle numbered i, the position and speed of the vehicle numbered i at the time of collection, and the preset length; The second constraint condition represents the shortest time for the vehicle numbered i to arrive at the merging area; the third constraint condition is determined based on the estimated time for the vehicle numbered i to arrive at the merging area, the estimated time for the vehicle numbered i-1 to arrive at the merging area, and the time interval that adjacent vehicles in the same vehicle group need to maintain; the third constraint condition represents that adjacent vehicles in the same vehicle group must maintain a certain headway when passing; the fourth constraint condition is determined based on the estimated time for the head vehicle of the kth vehicle group to arrive at the merging area, the estimated time for the tail vehicle of the k-1th vehicle group to arrive at the merging area, and the time interval that adjacent vehicle groups need to maintain; the fourth constraint condition represents that adjacent vehicle groups must maintain a certain headway when passing.

[0014] In some embodiments, determining the second constraint condition based on the maximum speed and maximum acceleration of the vehicle numbered i, the position and speed of the vehicle numbered i at the collection time, and the preset length includes: determining a time required for the vehicle numbered i to accelerate to the maximum speed based on the maximum speed and maximum acceleration of the vehicle numbered i, and the speed of the vehicle numbered i at the collection time; determining a time required for the vehicle numbered i not to accelerate to the maximum speed based on the speed of the vehicle numbered i at the collection time, the maximum acceleration of the vehicle numbered i, the preset length, and the position of the vehicle numbered i at the collection time; determining an acceleration time of the vehicle numbered i based on the time required for the vehicle numbered i to accelerate to the maximum speed and the time required for the vehicle numbered i not to accelerate to the maximum speed; determining a cruising time of the vehicle numbered i based on the preset length, the position and speed of the vehicle numbered i at the collection time, the maximum acceleration and maximum speed of the vehicle numbered i; and determining the second constraint condition based on the collection time, the acceleration time of the vehicle numbered i, and the cruising time of the vehicle numbered i.

[0015] In some embodiments, controlling the travel of at least one vehicle based on the travel order of the multiple vehicle groups so as to merge the at least one vehicle into the merging area includes: adopting an equal headway control model to control the travel of an intelligent connected vehicle in the at least one vehicle based on the travel order of the multiple vehicle groups, and adopting a manual driving model to control the travel of a manually driven vehicle in the at least one vehicle based on the travel order of the multiple vehicle groups so as to merge the at least one vehicle into the merging area.

[0016] On the other hand, an embodiment of the present application provides a merging area traffic management device, which includes: a determination module, configured to determine the headway time between adjacent vehicles based on the driving information of at least one vehicle in a control area; the control area is an area within a preset length located before the merging area, and the driving information represents the current driving status of the corresponding vehicle; a grouping module, configured to group the at least one vehicle based on the headway time between the adjacent vehicles to obtain a plurality of vehicle groups; the determination module is also configured to simulate the time when each vehicle in the plurality of vehicle groups arrives at the merging area based on the driving information of each vehicle in the plurality of vehicle groups, and determine the passage order of the plurality of vehicle groups; a control module, configured to control the driving of the at least one vehicle based on the passage order of the plurality of vehicle groups, so as to merge the at least one vehicle into the merging area.

[0017] On the other hand, an embodiment of the present application provides a computer device, including a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the program, it implements some or all of the steps in the above method.

[0018] On the other hand, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, which implements some or all of the steps in the above method when executed by a processor.

[0019] On the other hand, an embodiment of the present application provides a computer program, including computer-readable code. When the computer-readable code is executed in a computer device, a processor in the computer device executes some or all of the steps for implementing the above method.

[0020] On the other hand, an embodiment of the present application provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and when the computer program is read and executed by a computer, implements some or all of the steps in the above method.

[0021] In an embodiment of the present application, at least one vehicle in the control area is divided into multiple vehicle groups based on the headway time between adjacent vehicles, and the intelligent networked vehicle is used as the leading vehicle of the vehicle group as much as possible, and the manually driven vehicles that do not follow the intelligent network are divided into a separate vehicle group. This can improve the controllability of manually driven vehicles in mixed traffic flows; by simulating the time it takes for each vehicle in the multiple vehicle groups to arrive at the merging area, the passage order of the multiple vehicle groups can be determined, and then the travel of the intelligent networked vehicles and manually driven vehicles can be controlled, so that the control and management of the vehicles on the entrance ramp can be achieved. The merging area traffic management method provided in the embodiment of the present application is not only applicable to pure networked traffic flows, but also to the merging of vehicles on the entrance ramp under mixed traffic flows, and has a wide range of applications.

[0022] It should be understood that the above general description and the following detailed description are merely exemplary and explanatory, and do not limit the technical solutions of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The drawings herein are incorporated into and constitute a part of the specification. These drawings illustrate embodiments consistent with the present application and, together with the specification, are used to illustrate the technical solutions of the present application.

[0024] Figure 1 A schematic diagram of the implementation process of a merging area traffic management method provided in an embodiment of the present application Figure 1 ;

[0025] Figure 2 A schematic diagram of mixed traffic flow in a merging area traffic management method provided in an embodiment of the present application;

[0026] Figure 3 A schematic diagram of the implementation process of a merging area traffic management method provided in an embodiment of the present application Figure 2 ;

[0027] Figure 4 A schematic diagram of the implementation process of double-layer control in a merging area traffic management method provided in an embodiment of the present application;

[0028] Figure 5 A schematic diagram of the implementation process of a merging area traffic management method provided in an embodiment of the present application Figure 3 ;

[0029] Figure 6 A schematic diagram of the structure of a merging area traffic management device provided in an embodiment of the present application;

[0030] Figure 7 A hardware entity diagram of a computer device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solutions and advantages of this application clearer, the technical solutions of this application are further elaborated in detail below with reference to the accompanying drawings and embodiments. The described embodiments should not be regarded as limiting this application. All other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0032] In the following description, reference is made to “some embodiments”, which describes a subset of all possible embodiments, but it will be understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.

[0033] The terms "first / second / third" involved are merely used to distinguish similar objects and do not represent a specific ordering of the objects. It is understandable that "first / second / third" can be interchanged with a specific order or sequence where permitted so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein.

[0034] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing this application only and are not intended to limit this application.

[0035] In order to better understand the merging area traffic management method provided in the embodiment of the present application, the solutions in the related technology are first described below.

[0036] With advancements in positioning, sensing, communication, and control technologies, intelligent connected vehicles (ICVs) are becoming an integral part of our lives, offering new solutions for ramp merging control. ICVs utilize advanced sensors, controllers, actuators, and other devices, combined with communication and network technologies, to enable environmental awareness, intelligent decision-making, and collaborative control. ICVs can exchange information with a central control unit (CCU) through IoV technology and execute control commands from the CCU. This allows vehicles to coordinate and cooperate, reducing collision risks and improving traffic efficiency in merging areas.

[0037] The technical solutions of the relevant technologies are mainly aimed at purely connected environments. Regarding the control of the entrance ramp merging area, it is divided into macro control and micro control from the overall framework. The idea of ​​macro control is as follows: by controlling the traffic lights of the entrance ramp, the traffic flow of the entrance ramp is adjusted, thereby improving the traffic capacity of the merging area and reducing safety risks. Micro control refers to using the collaborative control advantages of intelligent connected vehicles to control the behavior of each vehicle, thereby achieving efficient and safe merging of vehicles. In terms of control methods, micro control can be further divided into centralized control and distributed control. Centralized control refers to the use of a central control unit to globally coordinate vehicle movements to ensure optimal global benefits, but this control method is difficult to deal with emergencies. To this end, distributed control has been further derived, using vehicle-to-vehicle communication and vehicle-to-road communication. Each merging participant makes corresponding decisions based on its own surrounding environment.

[0038] The above solutions are all effective in purely connected environments, but are not suitable for mixed traffic flows where intelligent connected vehicles and human-driven vehicles coexist. Currently, approaches to on-ramp merging in mixed traffic flows primarily focus on macro-level traffic control systems, with limited attention paid to specific control methods for on-ramp merging areas.

[0039] Existing control methods for on-ramp merging areas are primarily based on a purely connected environment, including determining vehicle traffic order and planning vehicle trajectories. However, these methods have limited adaptability and are not suitable for scenarios where intelligent connected vehicles and human-driven vehicles coexist. In the future, intelligent connected vehicles and human-driven vehicles will coexist on the road. Human-driven vehicles are subject to human subjectivity and many uncontrollable factors, which increases the complexity of the control system.

[0040] The embodiment of the present application provides a method for managing traffic in a merging area, which can be executed by a processor of a computer device. The computer device may refer to a server, a laptop, a tablet computer, a desktop computer, a smart TV, a set-top box, a mobile device (such as a mobile phone, a portable video player, a personal digital assistant, a dedicated messaging device, a portable gaming device), or other device with data processing capabilities. Figure 1 As shown, the method includes the following steps 101 to 104:

[0041] Step 101: Determine the headway time between adjacent vehicles based on driving information of at least one vehicle in a control area.

[0042] The control area is an area within a preset length before the merging area, and the driving information represents the current driving state of the corresponding vehicle.

[0043] The merging area traffic management method provided in the embodiments of the present application can be applied to vehicle merging scenarios in entrance ramp merging areas under mixed traffic flows, and can also be applied to vehicle merging scenarios in entrance ramp merging areas under purely connected traffic flows. The merging area traffic management method provided in the embodiments of the present application can be directly applied to a central control unit in a computer device, that is, the central control unit executes the steps of the merging area traffic management method provided in the embodiments of the present application.

[0044] like Figure 2 As shown, in the vehicle merging scenario of the entrance ramp merging area under mixed traffic flow, the vehicles in the main road and the vehicles in the ramp will merge in the merging area. The merging area refers to the area where the vehicles in the main road merge with the vehicles in the ramp. The control area refers to the area before the merging area that guides vehicles into the merging area. Furthermore, the control area is an area on the main road and the ramp that is a preset length away from the merging area; wherein the preset length can be pre-set, for example, the preset length can be set to 200-400 meters (m). As Figure 2 As shown, the length of the merging area can be expressed as S, and the length of the control area (preset length) can be expressed as L. Figure 2 As can be seen from the figure, the control area can include two areas: the control area on the main road and the control area on the ramp.

[0045] The vehicle merging scenario at the entrance ramp merging area under mixed traffic flow includes both intelligent connected vehicles (CAVs) and human-driven vehicles (HVs). CAVs and central control units can exchange information with each other through vehicle-to-everything (V2X) devices. Human-driven vehicles can adjust their driving status by observing nearby road signs. In an intelligent transportation system, the central control unit may refer to the computer system of the traffic management center, which is responsible for collecting real-time data from various traffic monitoring devices, analyzing traffic conditions, and adjusting traffic lights, issuing information, or implementing other traffic control measures accordingly.

[0046] like Figure 2 As shown in the figure, the main road includes one manually driven vehicle and one intelligent connected vehicle, while the ramp includes two intelligent connected vehicles and one manually driven vehicle. Both the intelligent connected vehicles on the main road and the intelligent connected vehicles on the ramp can communicate with the central control unit through the Internet of Vehicles equipment.

[0047] Headway time is a crucial concept in traffic scenarios. It refers to the time interval between the arrival of the rear end of the leading vehicle at a specific point and the arrival of the front end of the following vehicle at the same point. Headway time is a key indicator of traffic density and road efficiency, directly impacting vehicle safety and road capacity.

[0048] Vehicle driving information refers to various information generated by a vehicle during its driving process. This information can be used for vehicle management, maintenance, safety monitoring, and traffic analysis. In one feasible implementation, vehicle driving information may include, but is not limited to, vehicle location information, vehicle speed information, vehicle direction information, vehicle braking information, vehicle distance traveled, vehicle fuel consumption, vehicle battery status, vehicle performance information, and vehicle environment information.

[0049] For example, the vehicle's location information may include the vehicle's coordinates under the Global Positioning System (including longitude, latitude, altitude, etc.), the vehicle's position on the road, etc. The vehicle's speed information may include the vehicle's instantaneous speed, average speed, historical speed records, and acceleration (including lateral acceleration and longitudinal acceleration), etc. The vehicle's direction information may include the azimuth of the vehicle's current direction and whether it is about to change lanes. The vehicle's braking information may include braking force and frequency, etc. The vehicle's driving distance refers to the distance the vehicle has traveled since it was started or within a specific time period. The vehicle's fuel consumption may include the vehicle's fuel consumption, etc. The vehicle's battery status may include the battery's charge status and cruising range, etc. The vehicle's performance information may include engine speed, coolant temperature, oil pressure, air flow, etc. The vehicle's environmental information may include the ambient temperature, humidity, air pressure, etc. outside the vehicle, and the road conditions in front of the vehicle (such as wet, icy, or snowy, etc.).

[0050] In some embodiments, a specific implementation method for obtaining the driving information of at least one vehicle in the control area may be: collecting the driving information of at least one vehicle collected by the road detection device at preset time intervals.

[0051] Among them, the preset duration can be pre-set, and can be set specifically according to the computing power of the central control unit and the actual traffic scenario. For example, the preset duration can be set to 10 seconds (s). The road detection device is used to collect vehicle driving information. In a feasible implementation, the road detection device may include but is not limited to: road side unit (RSU), perception sensor, on-board diagnostic system, millimeter wave radar, lidar, high-definition camera, global positioning system, and edge server and other equipment.

[0052] Since there will be a steady stream of vehicles entering the control area in actual scenarios, collecting the driving information of at least one vehicle collected by the road detection device every preset time period can adapt to the ever-changing traffic flow and make real-time adjustments based on the changed traffic flow.

[0053] In a feasible implementation, driving information of at least one vehicle collected by devices such as RSU, vehicle-mounted sensors, vehicle-mounted diagnostic systems (such as OBD-II), vehicle-mounted data acquisition terminals (such as T-BOX), and environmental perception sensors can be collected every 10 seconds.

[0054] In some embodiments, determining the time headway between adjacent vehicles may be accomplished by determining the time headway between adjacent vehicles based on the headway distance between adjacent vehicles and the average vehicle speed in the driving information of at least one vehicle. Specifically, the headway distance between adjacent vehicles is divided by the average vehicle speed to obtain the time headway between adjacent vehicles.

[0055] In some embodiments, the specific implementation method for determining the headway time between adjacent vehicles can also be: micro traffic simulation software (such as VISSIM, Paramics) can be used to simulate the driving behavior of each vehicle based on the driving information of at least one vehicle to obtain the simulation results of the headway time between adjacent vehicles.

[0056] Step 102: Group the at least one vehicle based on the headway time between adjacent vehicles to obtain a plurality of vehicle groups.

[0057] Since intelligent connected vehicles are more controllable and manually driven vehicles are less controllable, vehicles in the control area can be grouped based on this characteristic to improve the controllability of manually driven vehicles and enhance the safety and driving efficiency of vehicles in the merging area.

[0058] In some embodiments, determining multiple vehicle groups may be accomplished by setting a vehicle grouping strategy that prioritizes an intelligent connected vehicle as the lead vehicle in a vehicle group and groups manually driven vehicles not following the intelligent connected vehicle into a separate group; and then grouping at least one vehicle into multiple vehicle groups based on the vehicle grouping strategy and the headway time between adjacent vehicles. The principle of the vehicle grouping strategy is to prioritize the intelligent connected vehicle as the lead vehicle (lead vehicle) in a vehicle group, and to group manually driven vehicles not following the intelligent connected vehicle into a separate vehicle group.

[0059] Step 103 : Simulate the time it takes for each vehicle in the multiple vehicle groups to arrive at the merging area based on the driving information of each vehicle in the multiple vehicle groups, and determine a passing order for the multiple vehicle groups.

[0060] In some embodiments, the specific implementation method of determining the passage order of multiple vehicle groups can be: through micro-traffic simulation software, based on the driving information of each vehicle in the multiple vehicle groups, simulate the time it takes for each vehicle in the multiple vehicle groups to arrive at the merging area, and determine the passage order of the multiple vehicle groups.

[0061] In some embodiments, the specific implementation method for determining the passage order of multiple vehicle groups can also be: constructing a mathematical model based on the driving information of each vehicle in the multiple vehicle groups, simulating the time when each vehicle in the multiple vehicle groups arrives at the merging area through the mathematical model, and determining the passage order of the multiple vehicle groups.

[0062] Step 104 : Control the travel of the at least one vehicle based on the passing order of the multiple vehicle groups to merge the at least one vehicle into the merging area.

[0063] In a feasible implementation, the intelligent connected vehicle in at least one vehicle group can be controlled to drive based on the passage order of multiple vehicle groups, and the manually driven vehicle in at least one vehicle group can be guided to drive through roadside signs to merge at least one vehicle into the merging area.

[0064] It should be noted that, in the vehicle merging scenario of the entrance ramp merging area under pure networked traffic flow, the merging area traffic management method provided in the embodiment of the present application can be adopted to group multiple vehicles under the pure networked traffic flow based on the headway time between adjacent vehicles to obtain multiple vehicle groups. The passage order of the multiple vehicle groups is determined by simulating the travel of the multiple vehicle groups, and then the vehicles are merged into the merging area based on the passage order of the multiple vehicle groups.

[0065] In the vehicle merging scenario of the entrance ramp merging area under mixed traffic flow, the merging area traffic management method provided in the embodiment of the present application can still be adopted. Based on the headway time between adjacent vehicles, multiple vehicles in the mixed traffic flow are grouped to obtain multiple vehicle groups. The passage order of the multiple vehicle groups is determined by simulating the driving behavior of the multiple vehicle groups, and then the vehicles are merged into the merging area based on the passage order of the multiple vehicle groups.

[0066] It should be noted that the merging area traffic management method provided in the embodiment of the present application is not only applicable to the vehicle merging scenario of the entrance ramp merging area under pure networked traffic flow, but also to the vehicle merging scenario of the entrance ramp merging area under mixed traffic flow.

[0067] In an embodiment of the present application, at least one vehicle in the control area is divided into multiple vehicle groups based on the headway time between adjacent vehicles, and the intelligent networked vehicle is used as the leading vehicle of the vehicle group as much as possible, and the manually driven vehicles that do not follow the intelligent network are divided into a separate vehicle group. This can improve the controllability of manually driven vehicles in mixed traffic flows; by simulating the time it takes for each vehicle in the multiple vehicle groups to arrive at the merging area, the passage order of the multiple vehicle groups can be determined, and then the travel of the intelligent networked vehicles and manually driven vehicles can be controlled, so that the control and management of the vehicles on the entrance ramp can be achieved. The merging area traffic management method provided in the embodiment of the present application is not only applicable to pure networked traffic flows, but also to the merging of vehicles on the entrance ramp under mixed traffic flows, and has a wide range of applications.

[0068] The present application embodiment provides a method for managing traffic in a merging area, which can be executed by a processor of a computer device. Figure 3 As shown, the method includes the following steps 301 to 307:

[0069] Step 301: Determine the headway time between adjacent vehicles based on driving information of at least one vehicle in a control area.

[0070] The control area is an area within a preset length before the merging area, and the driving information represents the current driving state of the corresponding vehicle.

[0071] Here, step 301 corresponds to the aforementioned step 101, and the specific implementation of the aforementioned step 101 may be referred to during implementation.

[0072] Step 302: Set a vehicle grouping strategy that includes an intelligent connected vehicle as the first vehicle in a vehicle group and groups manually driven vehicles that do not follow the intelligent connected vehicle into a separate group.

[0073] In a feasible implementation method, a grouping logic can be set to use the intelligent connected vehicle as the first vehicle in the vehicle group and to divide the manually driven vehicles that do not follow the intelligent connected vehicle into a separate group. The grouping logic can be written into code and used as a vehicle grouping strategy.

[0074] Step 303: Identify the at least one vehicle and determine the type of each vehicle.

[0075] Vehicle types can include intelligent connected vehicles and manually driven vehicles.

[0076] In some implementations, step 303 may be specifically implemented by: determining a picture of each vehicle; and identifying the picture of each vehicle to determine the type of each vehicle.

[0077] In some embodiments, the specific implementation method of step 303 can also be: using V2X technology to send identification information to each vehicle, if feedback information for the identification information is received, the vehicle is determined to be an intelligent connected vehicle; if no feedback information for the identification information is received, the vehicle is determined to be a manually driven vehicle.

[0078] Step 304: Group the at least one vehicle based on the vehicle grouping strategy, the type of each vehicle, and the headway time between adjacent vehicles to obtain the multiple vehicle groups.

[0079] In some embodiments, the specific implementation method of step 304 can be: according to the vehicle grouping strategy and the type of each vehicle, the leading vehicle of each vehicle group can be made into an intelligent connected vehicle as much as possible, and the non-following vehicles are determined according to the headway time between adjacent vehicles, so as to divide the non-following vehicles into new vehicle groups, thereby obtaining multiple vehicle groups.

[0080] Step 305 : Based on the driving information of each vehicle in the plurality of vehicle groups, construct a mathematical model for determining a passing order of the plurality of vehicle groups and constraints for the mathematical model.

[0081] The mathematical model is used to determine the passage order of multiple vehicle groups, and the constraints are used to constrain the mathematical model to solve the optimal passage order at the moment.

[0082] In some embodiments, step 305 may be implemented by constructing a mathematical model for minimizing vehicle travel time based on the driving information of each vehicle in the plurality of vehicle groups; and constructing constraints for the mathematical model based on the driving information of each vehicle in the plurality of vehicle groups. Constructing the mathematical model for minimizing vehicle travel time means that the optimization objective of the mathematical model is to minimize vehicle travel time.

[0083] In a feasible implementation, since the optimization goal is to minimize vehicle travel time, the arrival time of the last vehicle in the vehicle group can be predicted, and minimizing the travel time of all vehicles is converted into minimizing the arrival time of the last vehicle in all vehicle groups. Therefore, the mathematical formula of the mathematical model can be expressed as: Where K is the total number of vehicle groups, represents the arrival time of the last vehicle in vehicle group numbered k; L represents the length of the control area (i.e. the preset length), The last vehicle in the vehicle group numbered k is The location at the moment, The last vehicle in the vehicle group numbered k is The speed of time.

[0084] In a feasible implementation method, a first constraint condition representing the estimated time for the vehicle numbered i to reach the merging area, a second constraint condition representing the shortest time for the vehicle numbered i to reach the merging area, a third constraint condition representing that adjacent vehicles in the same vehicle group must maintain a certain headway when passing, and a fourth constraint condition representing that adjacent vehicle groups must maintain a certain headway when passing can be constructed based on the driving information of each vehicle in multiple vehicle groups.

[0085] Step 306: Solve the mathematical model based on the constraints of the mathematical model to obtain a passing order of the multiple vehicle groups.

[0086] In a feasible implementation method, the first constraint condition representing the estimated time for the vehicle numbered i to arrive at the merging area, the second constraint condition representing the shortest time for the vehicle numbered i to arrive at the merging area, the third constraint condition representing that adjacent vehicles in the same vehicle group must maintain a certain headway when passing, and the fourth constraint condition representing that adjacent vehicle groups must maintain a certain headway when passing can be used as the solution space of the mathematical model. The optimal solution of the mathematical model is solved in the solution space, and the optimal solution is used as the passage order of multiple vehicle groups.

[0087] For example, the Python CasADi library and MATLAB's optimization toolbox can be used to solve the mathematical model based on its constraints and determine the traffic order for multiple vehicle groups. Python is a computer programming language that supports many open-source libraries, such as the CasADi library. The CasADi library is specifically designed for modeling and solving numerical optimization and optimal control problems. It provides an advanced symbolic computation framework that allows users to express complex optimization problems mathematically and solve them through algorithmic differentiation, numerical integration, and optimization algorithms.

[0088] Step 307: Control the travel of the at least one vehicle based on the passing order of the multiple vehicle groups to merge the at least one vehicle into the merging area.

[0089] In some embodiments, the specific implementation method of step 307 can be: using an equal headway control model to control the travel of the intelligent connected vehicle in the at least one vehicle based on the travel order of the multiple vehicle groups, and using a manual driving model to control the travel of the manually driven vehicle in the at least one vehicle based on the travel order of the multiple vehicle groups, so as to merge the at least one vehicle into the merging area.

[0090] The mathematical formula of the equal headway control model can be expressed as: Among them, △t represents the step size of control time, L i-1represents the length of the preceding vehicle (vehicle numbered i-1), gap represents the ideal headway, and p i (t) represents the position of vehicle i. The above formula describes the updated relationship for vehicle positions under the equal headway model. Due to the varying dynamic characteristics of different vehicles, the control variables directly derived from basic kinematic relationships are not appropriate. Instead, the vehicle can use its current and next position to derive and execute appropriate control variables based on its own dynamic characteristics. This can reduce the error between the vehicle's actual position and the desired position.

[0091] The manual driving model is primarily based on individual behavior and has a certain degree of randomness and heterogeneity. The manual driving model used in the embodiments of the present application may be an Intelligent Driver Model (IDM). For a mixed vehicle group containing both manual vehicles and intelligent connected vehicles, when a manual vehicle in the mixed vehicle group is far away from the vehicle ahead, a roadside sign can be used to remind the manual vehicle to speed up to reduce the distance to the vehicle ahead. For example, the message displayed on the sign may be "Vehicle with license plate number xx should accelerate to reduce the distance to the vehicle ahead."

[0092] In some embodiments, the above step 304 may be implemented by the following steps 3041 to 3044:

[0093] Step 3041: Number the at least one vehicle based on the time when each vehicle enters the control area and the position of each vehicle on the road to obtain a number for each vehicle.

[0094] In a feasible implementation, the vehicles in the control area on the ramp can be numbered in sequence based on the time each vehicle enters the control area and the position of each vehicle on the road, and then the vehicles in the control area on the main road can be numbered in sequence to obtain the number of each vehicle.

[0095] In some embodiments, the specific implementation method of step 3041 may be: based on the position of each vehicle on the road, determining the distance between each vehicle and the merging area; in order of distance from near to far and time from early to late, based on the time when each vehicle enters the control area and the distance between each vehicle and the merging area, first numbering the vehicles in the control area on the ramp in sequence, and then numbering the vehicles in the control area on the main road in sequence to obtain the number of each vehicle.

[0096] In a feasible implementation, when there are multiple vehicles at the same distance from the merging area, the numbering sequence of the multiple vehicles may be determined according to the time when the multiple vehicles enter the control area.

[0097] During implementation, the central control unit can assign a unique number to each vehicle based on the time it enters the control area. Vehicle numbering begins with the ramp vehicle, with the vehicle closest to the merging area numbered i = 1. Subsequent ramp vehicles are numbered 2, 3, 4, and so on. Once all vehicles on the ramp have been numbered, the main lane vehicles are numbered similarly, with lower numbers assigned to vehicles closer to the merging area.

[0098] Step 3042: When the type of vehicle numbered i is an intelligent connected vehicle and the headway time between the vehicle numbered i and the vehicle numbered i-1 is less than the grouping threshold, or when the type of vehicle numbered i is a manually driven vehicle and the type of vehicle numbered i-1 is an intelligent connected vehicle, and the headway time between the vehicle numbered i and the vehicle numbered i-1 is less than the grouping threshold, the vehicle numbered i is classified into the vehicle group to which the vehicle numbered i-1 belongs; i is a positive integer greater than or equal to 2.

[0099] In which, the grouping threshold includes a first grouping threshold and a second grouping threshold, the first grouping threshold is the grouping threshold when the types of adjacent vehicles are all intelligent connected vehicles, and the second grouping threshold is the grouping threshold when the types of adjacent vehicles are different; when the type of the vehicle numbered i and the type of the vehicle numbered i-1 are both intelligent connected vehicles, the grouping threshold is the first grouping threshold; when the type of the vehicle numbered i is an intelligent connected vehicle and the type of the vehicle numbered i-1 is a manually driven vehicle, or when the type of the vehicle numbered i is a manually driven vehicle and the type of the vehicle numbered i-1 is an intelligent connected vehicle, the grouping threshold is the second grouping threshold.

[0100] The headway between vehicle i and vehicle i-1 is less than the grouping threshold, indicating that vehicle i and vehicle i-1 are in a following relationship and can be grouped into the same vehicle group.

[0101] In one feasible implementation, when both vehicle i and vehicle i-1 are intelligent connected vehicles, a first grouping threshold may be used to determine whether the two vehicles are in a following relationship. If the two vehicles i and i-1 are different types, a second grouping threshold may be used to determine whether the two vehicles are in a following relationship.

[0102] Specifically, when the types of vehicle numbered i and vehicle numbered i-1 are both intelligent connected vehicles, and the headway time between vehicle numbered i and vehicle numbered i-1 is less than the first grouping threshold, it is determined that vehicle numbered i and vehicle numbered i-1 are in a following relationship, and at this time, vehicle numbered i is classified into the vehicle group where vehicle numbered i-1 is located.

[0103] When the type of vehicle numbered i is an intelligent connected vehicle, the type of vehicle numbered i-1 is a manually driven vehicle, and the headway time between the vehicle numbered i and the vehicle numbered i-1 is less than the second grouping threshold, it is determined that the vehicle numbered i and the vehicle numbered i-1 are in a following relationship, and at this time, the vehicle numbered i is classified into the vehicle group where the vehicle numbered i-1 is located.

[0104] When the type of vehicle numbered i is a manually driven vehicle and the type of vehicle numbered i-1 is an intelligent connected vehicle, and the headway time between the vehicle numbered i and the vehicle numbered i-1 is less than the second grouping threshold, it is determined that the vehicle numbered i and the vehicle numbered i-1 are in a following relationship, and at this time, the vehicle numbered i is classified into the vehicle group where the vehicle numbered i-1 is located.

[0105] Step 3043: When the type of the vehicle numbered i is an intelligent connected vehicle and the headway time between the vehicle numbered i and the vehicle numbered i-1 is greater than or equal to the grouping threshold, or when the type of the vehicle numbered i and the vehicle numbered i-1 are both manually driven vehicles, or when the vehicle numbered i is a manually driven vehicle and the type of the vehicle numbered i-1 is an intelligent connected vehicle, and the headway time between the vehicle numbered i and the vehicle numbered i-1 is greater than or equal to the grouping threshold, the vehicle numbered i is divided into a new vehicle group.

[0106] If the headway between vehicle i and vehicle i-1 is greater than or equal to the grouping threshold, it means that vehicle i and vehicle i-1 are not in a following relationship. In this case, vehicle i needs to be assigned to a new vehicle group.

[0107] Specifically, when the types of vehicle numbered i and vehicle numbered i-1 are both intelligent connected vehicles, and the headway time between vehicle numbered i and vehicle numbered i-1 is greater than or equal to the first grouping threshold, it is determined that vehicle numbered i and vehicle numbered i-1 are not in a following relationship, and vehicle numbered i is then divided into a new vehicle group.

[0108] When the type of vehicle numbered i is an intelligent connected vehicle, the type of vehicle numbered i-1 is a manually driven vehicle, and the headway time between the vehicle numbered i and the vehicle numbered i-1 is greater than or equal to the second grouping threshold, it is determined that the vehicle numbered i and the vehicle numbered i-1 are not in a following relationship, and the vehicle numbered i is then divided into a new vehicle group.

[0109] When the type of the vehicle numbered i and the vehicle numbered i-1 are both manually driven vehicles, the vehicle numbered i is directly classified into a new vehicle group.

[0110] When the vehicle numbered i is a manually driven vehicle, the type of vehicle numbered i-1 is an intelligent connected vehicle, and the headway time between the vehicle numbered i and the vehicle numbered i-1 is greater than or equal to the second grouping threshold, it is determined that the vehicle numbered i and the vehicle numbered i-1 are not in a following relationship, and the vehicle numbered i is then divided into a new vehicle group.

[0111] Step 3044: When i is greater than the total number of vehicles in the control area, obtain the multiple vehicle groups.

[0112] If i is greater than the total number of vehicles in the control area, it means that all vehicles in the control area have been determined. At this time, the grouping is completed and multiple vehicle groups are obtained.

[0113] In some embodiments, after obtaining multiple vehicle groups, the vehicle groups can be numbered according to the vehicle numbering method, starting from the ramp vehicle group. The closer the group is to the merging area, the smaller the number. After the ramp vehicle group is numbered, the main road vehicle group is numbered. Similarly, the closer the group is to the merging area, the smaller the number.

[0114] In some embodiments, the above step 305 can be implemented by the following steps 3051 to 3053:

[0115] Step 3051: Based on the driving information of each vehicle in the plurality of vehicle groups, determine an estimated time for the last vehicle of each vehicle group to arrive at the merging area.

[0116] In one feasible implementation, microscopic traffic simulation software can be used to simulate the time it takes for each vehicle in multiple vehicle groups to arrive at the merging area based on the driving information of each vehicle in the multiple vehicle groups, thereby determining the estimated time for the last vehicle in each vehicle group to arrive at the merging area.

[0117] Step 3052: Based on the estimated time for the last vehicle of each vehicle group to arrive at the merging area, a mathematical model for minimizing vehicle travel time is constructed.

[0118] Step 3053: Construct constraint conditions for the mathematical model based on the driving information of each vehicle in the multiple vehicle groups.

[0119] Here, the above steps 3052 and 3053 correspond to the above step 305. When implementing, you can refer to the description in the above step 305, and the embodiment of the present application will not go into details about this.

[0120] In some embodiments, the driving information of the vehicle numbered i includes the position and speed of the vehicle numbered i at the time of collection, and the maximum speed and maximum acceleration of the vehicle numbered i. The above step 3053 can be implemented by the following steps 3053a to 3053d:

[0121] Step 3053a: Determine a first constraint based on the position and speed of the vehicle numbered i at the time of collection, the preset length, and the time of collection; the first constraint represents the estimated time for the vehicle numbered i to arrive at the merging area.

[0122] The collection time refers to the time point when the vehicle's driving information is collected.

[0123] In some embodiments, the time required for vehicle i to reach the merging area can be determined based on a preset length and the position and speed of vehicle i at the time of collection; and the estimated time for vehicle i to reach the merging area can be determined based on the time of collection and the time required for vehicle i to reach the merging area.

[0124] In a feasible implementation, the specific implementation method for determining the time required for vehicle numbered i to reach the merging area can be: based on a preset length and the position of vehicle numbered i at the time of collection, determining the distance that vehicle numbered i still needs to travel; based on the distance that vehicle numbered i still needs to travel and the speed of vehicle numbered i at the time of collection, determining the time required for vehicle numbered i to reach the merging area.

[0125] In one feasible implementation, the specific implementation method for determining the estimated time for vehicle number i to arrive at the merging area can be: adding the collection time and the time required for vehicle number i to arrive at the merging area to obtain the estimated time for vehicle number i to arrive at the merging area.

[0126] For example, the mathematical formula for the estimated time for vehicle number i to arrive at the merging area can be expressed as: Wherein, L represents the length of the control area (i.e., the preset length), Indicates that the vehicle numbered i is The location at the moment, Indicates that the vehicle numbered i is The speed of time, It represents the time required for vehicle number i to reach the merging area.

[0127] Step 3053b: Determine a second constraint based on the maximum speed and maximum acceleration of the vehicle numbered i, the position and speed of the vehicle numbered i at the time of collection, and the preset length; the second constraint represents the shortest time for the vehicle numbered i to reach the merging area.

[0128] In some embodiments, the specific implementation method of step 3053b can be: determine the acceleration time of vehicle numbered i based on the maximum speed and maximum acceleration of vehicle numbered i, the speed and position of vehicle numbered i at the collection time, and the preset length; determine the cruising time of vehicle numbered i based on the preset length, the speed and position of vehicle numbered i at the collection time, and the maximum speed and maximum acceleration of vehicle numbered i; determine the second constraint condition based on the collection time, the acceleration time of vehicle numbered i, and the cruising time of vehicle numbered i.

[0129] In some embodiments, a specific implementation method for determining the acceleration time of the vehicle numbered i may be: based on the maximum speed and maximum acceleration of the vehicle numbered i and the speed of the vehicle numbered i at the time of collection, determining the time required for the vehicle numbered i not to accelerate to the maximum speed based on the speed of the vehicle numbered i at the time of collection, the maximum acceleration of the vehicle numbered i, the preset length and the position of the vehicle numbered i at the time of collection; based on the time required for the vehicle numbered i to accelerate to the maximum speed and the time required for the vehicle numbered i not to accelerate to the maximum speed, determining the acceleration time of the vehicle numbered i.

[0130] In a feasible implementation, the specific implementation method for determining the time required for vehicle numbered i to accelerate to the maximum speed can be: based on the maximum speed of vehicle numbered i and the speed of vehicle numbered i at the time of collection, determine the speed that needs to be increased; based on the speed that needs to be increased and the maximum acceleration of vehicle numbered i, determine the time required for vehicle numbered i to accelerate to the maximum speed.

[0131] Specifically, the maximum speed of vehicle numbered i is subtracted from the speed of vehicle numbered i at the time of collection to obtain the speed that needs to be increased; the speed that needs to be increased is divided by the maximum acceleration of vehicle numbered i to obtain the time required for vehicle numbered i to accelerate to the maximum speed.

[0132] For example, the mathematical formula for the time required for vehicle number i to accelerate to the maximum speed can be expressed as: Among them, v max represents the maximum speed of vehicle number i, Indicates that the vehicle numbered i is The speed of the moment, a max Indicates the maximum acceleration of vehicle number i.

[0133] In a feasible implementation, the specific implementation method for determining the time required for vehicle numbered i to travel at a uniform speed at maximum speed to the merging area can be: based on the speed of vehicle numbered i at the time of collection, the maximum acceleration of vehicle numbered i, the preset length and the position of vehicle numbered i at the time of collection, determine the speed required to travel to the merging area; based on the speed required to travel to the merging area and the maximum acceleration of vehicle numbered i, determine the time required for vehicle numbered i not to accelerate to the maximum speed.

[0134] Specifically, the mathematical formula for the speed required to reach the merging area can be expressed as: The mathematical formula for the time required for vehicle number i to not accelerate to its maximum speed can be expressed as: in, Indicates that the vehicle numbered i is The speed of the moment, a max represents the maximum acceleration of vehicle number i, L represents the length of the control area, Indicates the position of vehicle number i at the collection time i, a max Indicates the maximum acceleration of vehicle number i.

[0135] In one feasible implementation, the specific implementation method for determining the acceleration time of vehicle number i may be: the minimum value between the time required for vehicle number i to accelerate to the maximum speed and the time required for vehicle number i not to accelerate to the maximum speed is used as the acceleration time of vehicle number i. For example, the mathematical formula for the acceleration time of vehicle number i may be expressed as: in, represents the acceleration time of vehicle number i, v max represents the maximum speed of vehicle number i, Indicates that the vehicle numbered i is The speed of the moment, a max represents the maximum acceleration of vehicle number i, L represents the length of the control area, Indicates the position of vehicle numbered i at the collection time i.

[0136] In some embodiments, the specific implementation method of determining the cruising time of the vehicle numbered i can be: determining the cruising time of the vehicle numbered i based on the preset length, the position and speed of the vehicle numbered i at the time of collection, and the maximum acceleration and maximum speed of the vehicle numbered i.

[0137] In a feasible implementation, based on a preset length, the position and speed of vehicle number i at the time of collection, and the maximum acceleration and maximum speed of vehicle number i, the time required for vehicle number i to travel at a constant speed at the maximum speed to reach the merging area is determined; and the cruising time of vehicle number i is determined based on the time required for vehicle number i to travel at a constant speed at the maximum speed to reach the merging area.

[0138] Specifically, the mathematical formula for the time required for vehicle number i to travel at a constant maximum speed to the merging area can be expressed as: Where L represents the length of the control area, represents the position of vehicle number i at the collection time i, v max represents the maximum speed of vehicle number i, Indicates that the vehicle numbered i is The speed of the moment, a max Indicates the maximum acceleration of vehicle number i.

[0139] When vehicle i fails to accelerate to its maximum speed, its cruising time is 0. Therefore, the maximum of the time required for vehicle i to reach the merging area at a constant maximum speed and the cruising time of 0 can be used as the cruising time of vehicle i. In this case, the mathematical formula for the cruising time of vehicle i can be expressed as: in, represents the cruising time of vehicle number i, L represents the length of the control area, represents the position of vehicle number i at the collection time i, v max represents the maximum speed of vehicle number i, Indicates that the vehicle numbered i is The speed of the moment, a max Indicates the maximum acceleration of vehicle number i.

[0140] Step 3053c: Determine a third constraint based on the estimated time for the vehicle numbered i to arrive at the merging area, the estimated time for the vehicle numbered i-1 to arrive at the merging area, and the time interval that needs to be maintained between adjacent vehicles in the same vehicle group; the third constraint indicates that adjacent vehicles in the same vehicle group must maintain a certain headway when passing.

[0141] In a feasible implementation, the mathematical formula of the third constraint condition can be expressed as: in, is the estimated time for vehicle number i to arrive at the merging area, is the estimated time for vehicle i-1 to arrive at the merging area, and t3 is the time interval between adjacent vehicles in the same vehicle group. t3 can be determined based on operational experience and multiple experiments.

[0142] Step 3053d: Determine a fourth constraint based on the estimated time for the head vehicle of the k-th vehicle group to arrive at the merging area, the estimated time for the tail vehicle of the k-1-th vehicle group to arrive at the merging area, and the time interval to be maintained by adjacent vehicle groups; the fourth constraint indicates that adjacent vehicle groups must maintain a certain headway when passing.

[0143] In a feasible implementation, the mathematical formula of the fourth constraint condition can be expressed as: in, represents the estimated time for the first vehicle of the k-th vehicle group to arrive at the merging area, represents the estimated time for the last vehicle of the k-1th (k-1=j) vehicle group to arrive at the merging area, and t4 represents the time interval to be maintained between adjacent vehicle groups. t4 can be obtained based on business experience and multiple experiments.

[0144] In an embodiment of the present application, at least one vehicle in the control area is divided into multiple vehicle groups based on the headway time between adjacent vehicles, and the intelligent networked vehicle is used as the leading vehicle of the vehicle group as much as possible, and the manually driven vehicles that do not follow the intelligent network are divided into a separate vehicle group. This can improve the controllability of manually driven vehicles in mixed traffic flows; by simulating the time it takes for each vehicle in the multiple vehicle groups to arrive at the merging area, the passage order of the multiple vehicle groups can be determined, and then the travel of the intelligent networked vehicles and manually driven vehicles can be controlled, so that the control and management of the vehicles on the entrance ramp can be achieved. The merging area traffic management method provided in the embodiment of the present application is not only applicable to pure networked traffic flows, but also to the merging of vehicles on the entrance ramp under mixed traffic flows, and has a wide range of applications.

[0145] The following describes the application of the merging area traffic management method provided in the embodiment of the present application in actual scenarios.

[0146] In order to improve the safety and driving efficiency of the entrance ramp merging area under mixed traffic, this application establishes a two-layer optimization control method, such as Figure 4As shown, it is divided into upper-level control 41 and lower-level control 42. The upper-level control 41 may specifically include: step 401, collecting vehicle information (i.e., vehicle driving information); step 402, grouping vehicles; step 403, determining the passing order. The lower-level control 42 may specifically include: step 404, controlling the movement of CAVs; step 405, guiding the movement of HVs; and step 406, completing vehicle merging.

[0147] The embodiment of the present application may include the following steps 1 to 4:

[0148] Step 1: The roadside unit identifies the vehicle's license plate information, records the time the vehicle enters the control area, collects the vehicle's driving information such as position, speed and acceleration, and transmits this information to the central control unit.

[0149] Step 2: The central control unit assigns a unique number to each vehicle based on the time the vehicle enters the control area.

[0150] Vehicle numbering starts with the ramp vehicles. The vehicle closest to the merging area is numbered i=1, and subsequent ramp vehicles are numbered 2, 3, 4, ...; after all ramp vehicles are numbered, the main road vehicles are numbered in a similar manner to the ramp vehicles. The closer the main road vehicle is to the merging area, the lower the number.

[0151] The vehicles in the control area are divided into different vehicle groups based on the headway between adjacent vehicles. The specific vehicle grouping process is as follows:

[0152] (1) The central control unit collects vehicle information fed back by the roadside units at a specific frequency and determines the headway time between vehicles.

[0153] (2) The central control unit uses V2X technology to send identification information to each vehicle. If feedback information is received, the vehicle is determined to be a CAV; otherwise, it is determined to be an HV.

[0154] (3) The principle of vehicle grouping is to use CAV as the leading vehicle in the vehicle group as much as possible. Since the behavior of HVs is somewhat random, HVs that do not follow CAVs are divided into a separate vehicle group. The process of the central control unit executing vehicle grouping is as follows: Figure 5 , specifically including the following steps 501 to step:

[0155] Step 501: Initialize a first grouping threshold t1 and a second grouping threshold t2.

[0156] t1 represents the grouping threshold between CAV and CAV, and t2 represents the grouping threshold between CAV and HV. The specific values ​​of t1 and t2 can be selected based on experience or experimental tests.

[0157] Step 502: Initialize the number of vehicle groups G in the control area = 0, and the number of the vehicle to be determined i = 1.

[0158] Step 503: Determine whether i is 1.

[0159] If i is 1, proceed to step 504 ; if i is not 1, proceed to step 507 .

[0160] Step 504: Determine whether vehicle i is a CAV.

[0161] If vehicle i is a CAV, step 505 and subsequent steps are executed; if vehicle i is not a CAV, step 506 is executed.

[0162] Step 505, G=G+1.

[0163] Step 506, i=i+1.

[0164] Step 507: Determine whether i is greater than the total number of vehicles in the control area.

[0165] If i is greater than the total number of vehicles in the control area, the process ends. If i is less than or equal to the total number of vehicles in the control area, the process proceeds to step 508 .

[0166] Step 508: Determine whether vehicle i is a CAV.

[0167] If vehicle i is a CAV, execute step 509 ; if the preceding vehicle is not a CAV, execute step 512 .

[0168] Step 509: Determine whether the preceding vehicle is a CAV.

[0169] If the preceding vehicle is a CAV, step 510 is executed; if the preceding vehicle is not a CAV, step 511 is executed.

[0170] Step 510: Determine whether the headway between vehicle i and the preceding vehicle is less than t1.

[0171] If the headway between vehicle i and the preceding vehicle is less than t1, step 506 is executed; if the headway between vehicle i and the preceding vehicle is greater than or equal to t1, step 505 and subsequent steps are executed.

[0172] Step 511: Determine whether the headway between vehicle i and the preceding vehicle is less than t2.

[0173] If the headway between vehicle i and the preceding vehicle is less than t2, step 506 is executed; if the headway between vehicle i and the preceding vehicle is greater than or equal to t2, step 505 and subsequent steps are executed.

[0174] Step 512: Determine whether the preceding vehicle is a HV.

[0175] If the preceding vehicle is a HV, step 505 and subsequent steps are executed; if the preceding vehicle is not a HV, step 513 is executed.

[0176] Step 513: Determine whether the headway between vehicle i and the preceding vehicle is less than t2.

[0177] If the headway between vehicle i and the preceding vehicle is less than t2, step 506 is executed; if the headway between vehicle i and the preceding vehicle is greater than or equal to t2, step 505 and subsequent steps are executed.

[0178] (4) The vehicle grouping method is similar to the vehicle numbering method. Starting with the ramp vehicle grouping, the closer to the merging area, the smaller the group number. After the ramp vehicle grouping is completed, the main road vehicle grouping is numbered. Similarly, the closer to the merging area, the smaller the group number. To ensure that the control system can adapt to the random and uncertain behavior of HVs, the central control unit determines the vehicle grouping and vehicle sequence at a certain frequency.

[0179] Step 3: The central control unit determines the passage order of the vehicle groups in the control area with the goal of minimizing the vehicle travel time. The specific process is as follows:

[0180] (1) Estimate the time it takes for an HV to reach the merging area based on the vehicle's state. The estimated time it takes for a manually driven vehicle to reach the merging area is as follows:

[0181]

[0182] Where L represents the length of the control area (i.e. the starting point of the control area is the origin, and the coordinates of the starting point of the merging area are L). and Represents the central control unit in The position and speed of vehicle numbered i are collected at all times.

[0183] (2) To avoid allocating unreasonable vehicle arrival times, it is necessary to estimate the shortest arrival time of CAVs, as follows:

[0184]

[0185]

[0186]

[0187] Among them, v max represents the maximum speed of the vehicle, a max represents the maximum acceleration of the vehicle, Indicates that the central control unit is The position of vehicle i is collected at all times, Indicates that the central control unit is The speed of vehicle i is collected at all times, represents the acceleration time of vehicle i, represents the cruising time of vehicle i.

[0188] Formula (3) determines the time required for the vehicle to accelerate to the maximum speed based on the vehicle speed and the road speed limit. Indicates that it can accelerate to the maximum speed. Indicates that the maximum speed is insufficient to travel from the current position to the merge point.

[0189] Formula (4) represents the time required for the vehicle to travel at a constant speed at maximum speed to the merging point. Indicates the time the vehicle can reach its maximum speed and cruise to the merging point. 0 means the vehicle fails to accelerate to its maximum speed, so the cruising time is 0.

[0190] (3) For driving safety, adjacent vehicles in the same vehicle group need to maintain a certain distance when passing through the merging area. That is, the estimated time to reach the merging area needs to maintain a time interval t3, as follows:

[0191]

[0192] To ensure safe merging, a certain headway must be maintained between vehicle groups. That is, the estimated arrival time of the leading vehicle in the vehicle group and the last vehicle in the preceding vehicle group must be kept at a certain time interval t4, as shown in the following formula (6):

[0193]

[0194] in, represents the time when the first vehicle of group k arrives at the merging area, represents the time when the last vehicle in group j arrives at the merging area. k and j are two adjacent vehicle groups, with group k passing after group j. t3 and t4 can be obtained based on experience or experimental testing.

[0195] (4) Establish the objective function of minimizing vehicle travel time and solve the merge sequence. Based on the above relationship and constraints, the arrival time of the last vehicle in the vehicle group can be estimated, and the problem of minimizing the travel time of all vehicles can be transformed into the problem of minimizing the arrival time of the last vehicle in all vehicle groups. Therefore, the objective function is as follows:

[0196]

[0197] Among them, K represents the total number of vehicle groups, represents the arrival time of the last vehicle in vehicle group k.

[0198] By solving formula (7), the passage order of all vehicle groups can be obtained, and then the passage order of all vehicles can be known and transmitted to step 4.

[0199] Step 4: Complete the merge by controlling the CAV and guiding the HV. The specific process is as follows:

[0200] (1) CAV adopts the equal headway control model as follows:

[0201]

[0202] Among them, △t represents the step size of control time, L i-1 Indicates the length of the preceding vehicle, gap represents the ideal headway, and p i (t) represents the position of vehicle i. Equation (8) describes the updated relationship for vehicle position based on the equal headway model. Due to the different dynamic characteristics of different vehicles, the control variables directly derived from the basic kinematic relationship are not appropriate. The vehicle can use its current position and the next position to obtain and execute the corresponding control variables based on its own dynamic characteristics. This can reduce the error between the actual position of the vehicle and the desired position.

[0203] (1) The HV uses its own motion model to make corresponding decisions based on the status of surrounding vehicles.

[0204] Guiding HVs: If an HV in a mixed CAV-HV group is too far away from the vehicle ahead, the central control unit uses roadside signage to remind the HV, "License plate number xx, you should accelerate and reduce the distance to the vehicle ahead." The license plate number can be identified by the roadside unit.

[0205] It should be noted that the embodiments of the present application include at least the following innovations:

[0206] (1) A group-based optimization control method for the mixed traffic entrance ramp merging area. This method divides the vehicle groups according to the vehicle status and determines the passage order through the central control unit, and completes the vehicle merging by controlling the CAV movement and guiding the HV movement.

[0207] (2) Specific steps for vehicle grouping. A vehicle grouping strategy is proposed based on the vehicle sequence and the headway between CAVs and between CAVs and HVs. The vehicle grouping strategy is to group the vehicles into a group with the CAV as the leading vehicle and the HVs that do not follow the CAV into a separate group.

[0208] (3) Specific steps for determining the vehicle traffic order. Using the time it takes for CAVs and HVs to arrive at the merging area and the distance between different vehicles as constraints, an objective function is constructed with the goal of minimizing vehicle travel time to determine the traffic order of vehicle groups in the control area.

[0209] It should be noted that the embodiments of the present application include at least the following technical effects:

[0210] (1) This application proposes a dual-layer optimization control method and system for mixed traffic flow on-ramp merging. This method is applicable not only to mixed traffic flow situations but also to pure connected environments. This application enhances the scope of application of the method by controlling the movement of intelligent connected vehicles and guiding the movement of manually driven vehicles.

[0211] (2) This application can adapt to the impact of random and uncertain behaviors of manually driven vehicles on the control system. The central control unit in this application updates the vehicle grouping and traffic order at a certain frequency to adapt to the disturbance of the system caused by manually driven vehicles.

[0212] Based on the foregoing embodiments, an embodiment of the present application provides a merging area traffic management device, which includes the various units included and the various modules included in each unit, and can be implemented by a processor in a computer device; of course, it can also be implemented by a specific logic circuit; in the implementation process, the processor can be a central processing unit (CPU), a microprocessor (MPU), a digital signal processor (DSP) or a field programmable gate array (FPGA), etc.

[0213] Figure 6 A schematic diagram of the structure of a merging area traffic management device provided in an embodiment of the present application is shown in FIG. Figure 6 As shown, the merging area traffic management device 600 includes: a determination module 610, a grouping module 620 and a control module 630, wherein:

[0214] Determining module 610 is configured to determine a headway time between adjacent vehicles based on driving information of at least one vehicle within a control area, wherein the control area is an area within a preset length before the merging area, and the driving information represents a current driving state of the corresponding vehicle;

[0215] a grouping module 620 configured to group the at least one vehicle based on the headway time between adjacent vehicles to obtain a plurality of vehicle groups;

[0216] The determination module 610 is further configured to simulate the time when each vehicle in the plurality of vehicle groups arrives at the merging area based on the driving information of each vehicle in the plurality of vehicle groups, and determine the passing order of the plurality of vehicle groups;

[0217] The control module 630 is configured to control the at least one vehicle to travel based on the passing order of the plurality of vehicle groups, so as to merge the at least one vehicle into the merging area.

[0218] In some embodiments, the grouping module 620 is specifically configured to: set a vehicle grouping strategy that represents a vehicle group with an intelligent connected vehicle as the first vehicle, and divide manually driven vehicles that do not follow the intelligent connected vehicle into a separate group; identify the at least one vehicle and determine the type of each vehicle; group the at least one vehicle based on the vehicle grouping strategy, the type of each vehicle and the headway time between the adjacent vehicles to obtain the multiple vehicle groups.

[0219] In some embodiments, the grouping module 620 is specifically configured to: number the at least one vehicle based on the time when each vehicle enters the control area and the position of each vehicle on the road to obtain the number of each vehicle; when the type of the vehicle numbered i is an intelligent connected vehicle and the headway between the vehicle numbered i and the vehicle numbered i-1 is less than the grouping threshold, or when the type of the vehicle numbered i is a manually driven vehicle and the type of the vehicle numbered i-1 is an intelligent connected vehicle, and the headway between the vehicle numbered i and the vehicle numbered i-1 is less than the grouping threshold, the vehicle numbered i is divided into the group where the vehicle numbered i-1 is located. vehicle group; i is a positive integer greater than or equal to 2; when the type of the vehicle numbered i is an intelligent connected vehicle and the headway time between the vehicle numbered i and the vehicle numbered i-1 is greater than or equal to the grouping threshold, or when the type of the vehicle numbered i and the vehicle numbered i-1 are both manually driven vehicles, or when the vehicle numbered i is a manually driven vehicle and the type of the vehicle numbered i-1 is an intelligent connected vehicle, and the headway time between the vehicle numbered i and the vehicle numbered i-1 is greater than or equal to the grouping threshold, the vehicle numbered i is divided into a new vehicle group; when i is greater than the total number of vehicles in the control area, the multiple vehicle groups are obtained.

[0220] In some embodiments, the grouping module 620 is specifically configured to: determine the distance between each vehicle and the merging area based on the position of each vehicle on the road; and number the vehicles in the control area on the ramp in order, and then number the vehicles in the control area on the main road in order, in order of distance from near to far and time from early to late, based on the time when each vehicle enters the control area and the distance between each vehicle and the merging area, to obtain the number of each vehicle.

[0221] In some embodiments, the grouping threshold includes a first grouping threshold and a second grouping threshold, the first grouping threshold is the grouping threshold when the types of adjacent vehicles are all intelligent connected vehicles, and the second grouping threshold is the grouping threshold when the types of adjacent vehicles are different; when the type of the vehicle numbered i and the type of the vehicle numbered i-1 are both intelligent connected vehicles, the grouping threshold is the first grouping threshold; when the type of the vehicle numbered i is an intelligent connected vehicle and the type of the vehicle numbered i-1 is a manually driven vehicle, or when the type of the vehicle numbered i is a manually driven vehicle and the type of the vehicle numbered i-1 is an intelligent connected vehicle, the grouping threshold is the second grouping threshold.

[0222] In some embodiments, the determination module 610 is specifically configured to: construct a mathematical model for determining the passage order of the multiple vehicle groups and constraints on the mathematical model based on the driving information of each vehicle in the multiple vehicle groups; solve the mathematical model based on the constraints of the mathematical model to obtain the passage order of the multiple vehicle groups.

[0223] In some embodiments, the determination module 610 is specifically configured to: determine the estimated time for the last vehicle of each vehicle group to arrive at the merging area based on the driving information of each vehicle in the multiple vehicle groups; construct a mathematical model that minimizes the vehicle driving time based on the estimated time for the last vehicle of each vehicle group to arrive at the merging area; and construct constraints for the mathematical model based on the driving information of each vehicle in the multiple vehicle groups.

[0224] In some embodiments, the determination module 610 is specifically configured to: determine a first constraint based on the position and speed of the vehicle numbered i at the time of collection, the preset length, and the time of collection; the first constraint represents the estimated time for the vehicle numbered i to arrive at the merging area; determine a second constraint based on the maximum speed and maximum acceleration of the vehicle numbered i, the position and speed of the vehicle numbered i at the time of collection, and the preset length; the second constraint represents the shortest time for the vehicle numbered i to arrive at the merging area; based on the maximum speed and maximum acceleration of the vehicle numbered i, the position and speed of the vehicle numbered i at the time of collection, and the preset length; The third constraint condition is determined based on the estimated time of the merging area, the estimated time of the vehicle numbered i-1 arriving at the merging area, and the time interval that adjacent vehicles in the same vehicle group need to maintain; the third constraint condition indicates that adjacent vehicles in the same vehicle group must maintain a certain headway when passing; the fourth constraint condition is determined based on the estimated time of the head vehicle of the k-th vehicle group arriving at the merging area, the estimated time of the tail vehicle of the k-1-th vehicle group arriving at the merging area, and the time interval that adjacent vehicle groups need to maintain; the fourth constraint condition indicates that adjacent vehicle groups must maintain a certain headway when passing.

[0225] In some embodiments, the determination module 610 is specifically configured to: determine the time required for the vehicle numbered i to accelerate to the maximum speed based on the maximum speed and maximum acceleration of the vehicle numbered i and the speed of the vehicle numbered i at the collection time; determine the time required for the vehicle numbered i not to accelerate to the maximum speed based on the speed of the vehicle numbered i at the collection time, the maximum acceleration of the vehicle numbered i, the preset length, and the position of the vehicle numbered i at the collection time; determine the acceleration time of the vehicle numbered i based on the time required for the vehicle numbered i to accelerate to the maximum speed and the time required for the vehicle numbered i not to accelerate to the maximum speed; determine the cruising time of the vehicle numbered i based on the preset length, the position and speed of the vehicle numbered i at the collection time, the maximum acceleration and maximum speed of the vehicle numbered i; and determine the second constraint condition based on the collection time, the acceleration time of the vehicle numbered i, and the cruising time of the vehicle numbered i.

[0226] In some embodiments, the control module 630 is specifically configured to: adopt an equal headway control model to control the travel of the intelligent connected vehicle in the at least one vehicle based on the passage order of the multiple vehicle groups, and adopt a manual driving model to control the travel of the manually driven vehicle in the at least one vehicle based on the passage order of the multiple vehicle groups, so as to merge the at least one vehicle into the merging area.

[0227] The description of the above device embodiment is similar to the description of the above method embodiment and has similar beneficial effects as the method embodiment. In some embodiments, the functions or modules included in the device provided in the embodiments of the present application can be used to perform the methods described in the above method embodiments. For technical details not disclosed in the device embodiments of the present application, please refer to the description of the method embodiments of the present application for understanding.

[0228] It should be noted that, in the embodiment of the present application, if the above-mentioned merging area traffic management method is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiment of the present application, or the part that contributes to the relevant technology, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the methods described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a U disk, a mobile hard disk, a read-only memory (ROM), a magnetic disk or an optical disk. In this way, the embodiment of the present application is not limited to any specific hardware, software or firmware, or any combination of hardware, software and firmware.

[0229] An embodiment of the present application provides a computer device, including a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the program, some or all of the steps in the above method are implemented.

[0230] The present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements some or all of the steps in the above method. The computer-readable storage medium may be transient or non-transient.

[0231] An embodiment of the present application provides a computer program, including computer-readable code. When the computer-readable code is run in a computer device, a processor in the computer device executes some or all of the steps for implementing the above method.

[0232] An embodiment of the present application provides a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and when the computer program is read and executed by a computer, implements some or all of the steps in the above method. The computer program product can be implemented specifically by hardware, software, or a combination thereof. In some embodiments, the computer program product is embodied as a computer storage medium. In other embodiments, the computer program product is embodied as a software product, such as a software development kit (SDK), etc.

[0233] It should be noted that the descriptions of the various embodiments above tend to emphasize the differences between the various embodiments, and their similarities or similarities can be referenced to each other. The descriptions of the above device, storage medium, computer program, and computer program product embodiments are similar to the descriptions of the above method embodiments and have similar beneficial effects as the method embodiments. For technical details not disclosed in the embodiments of the device, storage medium, computer program, and computer program product of this application, please refer to the description of the method embodiments of this application for understanding.

[0234] It should be noted that Figure 7 A schematic diagram of a hardware entity of a computer device in an embodiment of the present application is shown in FIG. Figure 7 As shown, the hardware entity of the computer device 700 includes: a processor 701, a communication interface 702 and a memory 703, wherein:

[0235] Processor 701 generally controls the overall operation of computer device 700 .

[0236] The communication interface 702 enables the computer device to communicate with other terminals or servers through a network.

[0237] The memory 703 is configured to store instructions and applications executable by the processor 701. It can also cache data to be processed or processed by the processor 701 and various modules in the computer device 700 (for example, image data, audio data, voice communication data, and video communication data). This can be implemented using flash memory (FLASH) or random access memory (RAM). Data can be transmitted between the processor 701, the communication interface 702, and the memory 703 via a bus 704.

[0238] It should be understood that "one embodiment" or "an embodiment" mentioned throughout the specification means that the specific features, structures or characteristics related to the embodiment are included in at least one embodiment of the present application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. In addition, these specific features, structures or characteristics can be combined in one or more embodiments in any suitable manner. It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned steps / processes does not mean the order of execution, and the execution order of each step / process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application. The above-mentioned serial numbers of the embodiments of the present application are for description only and do not represent the advantages and disadvantages of the embodiments.

[0239] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0240] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0241] The units described above as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units; they may be located in one place or distributed across multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, the functional units in the various embodiments of the present application may all be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0242] The above is only an implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. A method for managing traffic in a merging area, characterized in that: The merging area traffic management method includes: Determining the headway time between adjacent vehicles based on driving information of at least one vehicle within a control area, wherein the control area is an area within a preset length before the merging area, and the driving information represents the current driving state of the corresponding vehicle; Grouping the at least one vehicle based on the headway time between adjacent vehicles to obtain a plurality of vehicle groups; simulating the time for each vehicle in the plurality of vehicle groups to arrive at the merging area based on the driving information of each vehicle in the plurality of vehicle groups, and determining a passing order of the plurality of vehicle groups; The at least one vehicle is controlled to travel based on a passing order of the plurality of vehicle groups so as to merge the at least one vehicle into the merging area.

2. The merging area traffic management method according to claim 1, characterized in that: The grouping of the at least one vehicle based on the headway time between adjacent vehicles to obtain a plurality of vehicle groups includes: A vehicle grouping strategy is set up in which the intelligent connected vehicle is the first vehicle in the vehicle group and the manually driven vehicles that do not follow the intelligent connected vehicle are grouped separately. identifying the at least one vehicle and determining the type of each vehicle; The at least one vehicle is grouped based on the vehicle grouping strategy, the type of each vehicle, and the headway time between adjacent vehicles to obtain the multiple vehicle groups.

3. The merging area traffic management method according to claim 2, characterized in that: The grouping of the at least one vehicle based on the vehicle grouping strategy, the type of each vehicle, and the headway time between adjacent vehicles to obtain the plurality of vehicle groups includes: Numbering the at least one vehicle based on the time when each vehicle enters the control area and the position of each vehicle on the road to obtain a number for each vehicle; When the type of vehicle numbered i is an intelligent connected vehicle and the headway time between the vehicle numbered i and the vehicle numbered i-1 is less than the grouping threshold, or when the type of vehicle numbered i is a manually driven vehicle and the type of vehicle numbered i-1 is an intelligent connected vehicle and the headway time between the vehicle numbered i and the vehicle numbered i-1 is less than the grouping threshold, the vehicle numbered i is classified into the vehicle group to which the vehicle numbered i-1 belongs; i is a positive integer greater than or equal to 2; When the type of the vehicle numbered i is an intelligent connected vehicle and the headway time between the vehicle numbered i and the vehicle numbered i-1 is greater than or equal to the grouping threshold, or when the type of the vehicle numbered i and the vehicle numbered i-1 are both manually driven vehicles, or when the vehicle numbered i is a manually driven vehicle and the type of the vehicle numbered i-1 is an intelligent connected vehicle, and the headway time between the vehicle numbered i and the vehicle numbered i-1 is greater than or equal to the grouping threshold, the vehicle numbered i is divided into a new vehicle group; When i is greater than the total number of vehicles in the control area, the multiple vehicle groups are obtained.

4. The merging area traffic management method according to claim 3, characterized in that: The step of numbering the at least one vehicle based on the time when each vehicle enters the control area and the position of each vehicle on the road to obtain the number of each vehicle includes: determining a distance between each vehicle and the merging area based on a position of each vehicle on the road; In order of distance from near to far and time from early to late, based on the time when each vehicle enters the control area and the distance between each vehicle and the merging area, the vehicles in the control area on the ramp are numbered in sequence, and then the vehicles in the control area on the main road are numbered in sequence to obtain the number of each vehicle.

5. The merging area traffic management method according to claim 3, characterized in that: The grouping threshold includes a first grouping threshold and a second grouping threshold, the first grouping threshold is the grouping threshold when the types of adjacent vehicles are all intelligent connected vehicles, and the second grouping threshold is the grouping threshold when the types of adjacent vehicles are different; When the type of the vehicle numbered i and the type of the vehicle numbered i-1 are both intelligent connected vehicles, the grouping threshold is the first grouping threshold; When the type of the vehicle numbered i is an intelligent connected vehicle and the type of the vehicle numbered i-1 is a manually driven vehicle, or when the type of the vehicle numbered i is a manually driven vehicle and the type of the vehicle numbered i-1 is an intelligent connected vehicle, the grouping threshold is the second grouping threshold.

6. The merging area traffic management method according to any one of claims 1 to 5, characterized in that: The simulating the time for each vehicle in the plurality of vehicle groups to arrive at the merging area based on the driving information of each vehicle in the plurality of vehicle groups to determine the passing order of the plurality of vehicle groups includes: constructing a mathematical model for determining a passing order of the plurality of vehicle groups and constraints for the mathematical model based on the driving information of each vehicle in the plurality of vehicle groups; The mathematical model is solved based on the constraints of the mathematical model to obtain a passing order of the multiple vehicle groups.

7. The merging area traffic management method according to claim 6, characterized in that: The constructing, based on the driving information of each vehicle in the plurality of vehicle groups, a mathematical model for determining the passage order of the plurality of vehicle groups and constraints for the mathematical model includes: determining, based on the driving information of each vehicle in the plurality of vehicle groups, an estimated time for the last vehicle of each vehicle group to arrive at the merging area; constructing a mathematical model for minimizing vehicle travel time based on an estimated time for the last vehicle of each vehicle group to arrive at the merging area; Based on the driving information of each vehicle in the plurality of vehicle groups, a constraint condition for the mathematical model is constructed.

8. The merging area traffic management method according to claim 7, characterized in that: The driving information of the vehicle numbered i includes the position and speed of the vehicle numbered i at the time of collection, the maximum speed and maximum acceleration of the vehicle numbered i; The constructing of constraint conditions for the mathematical model based on the driving information of each vehicle in the plurality of vehicle groups includes: Determining a first constraint condition based on the position and speed of the vehicle numbered i at the time of collection, the preset length, and the time of collection; the first constraint condition represents an estimated time for the vehicle numbered i to arrive at the merging area; Determining a second constraint condition based on the maximum speed and maximum acceleration of the vehicle numbered i, the position and speed of the vehicle numbered i at the time of collection, and the preset length; the second constraint condition represents the shortest time for the vehicle numbered i to reach the merging area; Determining a third constraint based on the estimated time for the vehicle numbered i to arrive at the merging area, the estimated time for the vehicle numbered i-1 to arrive at the merging area, and the time interval that must be maintained between adjacent vehicles in the same vehicle group; the third constraint indicates that adjacent vehicles in the same vehicle group must maintain a certain headway when passing; A fourth constraint is determined based on the estimated time for the head vehicle of the k-th vehicle group to arrive at the merging area, the estimated time for the tail vehicle of the k-1-th vehicle group to arrive at the merging area, and the time interval to be maintained by adjacent vehicle groups; the fourth constraint indicates that adjacent vehicle groups must maintain a certain headway when passing.

9. The merging area traffic management method according to claim 8, characterized in that: The determining of the second constraint condition based on the maximum speed and maximum acceleration of the vehicle numbered i, the position and speed of the vehicle numbered i at the time of collection, and the preset length includes: Determine the time required for the vehicle numbered i to accelerate to the maximum speed based on the maximum speed and maximum acceleration of the vehicle numbered i and the speed of the vehicle numbered i at the time of collection; Determining a time required for the vehicle numbered i not to accelerate to the maximum speed based on the speed of the vehicle numbered i at the time of collection, the maximum acceleration of the vehicle numbered i, the preset length, and the position of the vehicle numbered i at the time of collection; determining an acceleration time of the vehicle numbered i based on a time required for the vehicle numbered i to accelerate to a maximum speed and a time required for the vehicle numbered i not to accelerate to the maximum speed; Determine the cruising time of the vehicle numbered i based on the preset length, the position and speed of the vehicle numbered i at the time of collection, and the maximum acceleration and maximum speed of the vehicle numbered i; The second constraint condition is determined based on the collection time, the acceleration time of the vehicle numbered i, and the cruising time of the vehicle numbered i.

10. The merging area traffic management method according to any one of claims 1 to 5, characterized in that: The controlling the travel of the at least one vehicle based on the passing order of the plurality of vehicle groups so as to merge the at least one vehicle into the merging area includes: An equal headway control model is adopted to control the driving of the intelligent connected vehicle in the at least one vehicle based on the passage order of the multiple vehicle groups, and a manual driving model is adopted to control the driving of the manually driven vehicle in the at least one vehicle based on the passage order of the multiple vehicle groups, so as to merge the at least one vehicle into the merging area.

11. A computer device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor executes the program, the steps of the method according to any one of claims 1 to 10 are implemented.

12. A computer program product comprising a computer program or instructions, characterized in that When the computer program or instructions are executed by a processor, the steps of the method according to any one of claims 1 to 10 are performed.

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