A signal light passing control method, system, device and medium for vehicle grouping

Abstract

This application provides a traffic light traffic control method, system, device, and medium for vehicle grouping, relating to the field of traffic control technology. The method includes: determining the maximum travel distance of each vehicle before the end of a target traffic cycle based on its motion parameters; grouping vehicles into clusters based on the maximum travel distance of vehicles in the same lane and their distance to the stop line ahead; constructing a speed guidance curve function based on the planned average speed of the lead vehicle in the cluster; optimizing the speed guidance curve function based on a cluster energy minimization optimization function to obtain a target speed guidance curve; controlling the lead vehicle to drive based on the target speed guidance curve; determining the following state of each vehicle in the cluster based on the lead vehicle's driving state and the dynamic models of various vehicles; and controlling the vehicles in the cluster to follow based on their own following states, so that all vehicles in the cluster pass through the intersection ahead before the end of the corresponding traffic cycle. The aim is to improve traffic efficiency.

Description

Technical Field

[0001] This application relates to the field of traffic control technology, specifically to a traffic light traffic control method, system, device, and medium for vehicle grouping. Background Technology

[0002] In the guidance area near the signal zone of traffic lights, traditional vehicles (Human-driven Vehicles, HVs) are strongly constrained by the periodic changes in traffic signals and affected by the ambiguity of drivers' judgment, often resulting in frequent starts and stops, acceleration and deceleration, and causing continuous traffic to exhibit periodic stop-and-go behavior, impacting the driving experience. With the development of intelligent connected and autonomous driving technologies, CAVs (Connected and Automatic Vehicles) can obtain information from multiple preceding vehicles and road equipment based on V2V (Vehicle to Vehicle) and V2I (Vehicle to Infrastructure) technologies, effectively adjusting their driving behavior to avoid unnecessary frequent stops and starts and reduce unnecessary energy waste caused by speed fluctuations.

[0003] However, before fully connected and autonomous driving becomes widespread, future transportation will be in a new hybrid mode where connected and autonomous vehicles coexist with traditional vehicles for a long time. This severely restricts the effectiveness of guiding vehicles through traffic lights at intersections. Summary of the Invention

[0004] In view of this, this application provides a traffic light control method, system, device, and medium for vehicle grouping. It aims to solve or partially solve the problems existing in the prior art.

[0005] The first aspect of this application provides a traffic light control method for vehicle grouping, the method comprising:

[0006] Based on the vehicle's motion parameters, determine the maximum travel distance of the vehicle before the end of the target traffic cycle, where the target traffic cycle is the closest current green light cycle.

[0007] Based on the maximum travel distance of each vehicle in the same lane and its distance to the stop line ahead, each vehicle is divided into clusters to obtain cluster division results. In the cluster division results, each cluster corresponds to a cluster of vehicles that passed the intersection ahead before the end of a travel cycle.

[0008] Based on the planned average speed of the lead vehicle in the cluster, a speed guidance curve function for the lead vehicle is constructed, wherein the lead vehicle is the first connected vehicle in the cluster.

[0009] Based on the pre-established cluster energy consumption minimization optimization function, the speed guidance curve function of the lead vehicle is optimized and solved to obtain the target speed guidance curve of the lead vehicle.

[0010] The navigator in the control cluster is guided to travel along a curve at its own target speed.

[0011] Based on the driving status of the lead vehicle in the cluster, the pre-established connected vehicle dynamics model and traditional vehicle dynamics model, the following status of each connected vehicle and each traditional vehicle in the cluster is determined.

[0012] The system controls each vehicle in the cluster to follow the others based on its own following status, so that all vehicles in the cluster can pass through the intersection ahead before the end of the corresponding traffic cycle.

[0013] A second aspect of this application provides a traffic light control system for vehicle grouping, the system comprising:

[0014] The passage distance determination module is used to determine the maximum passage distance of the vehicle before the end of the target passage cycle based on the vehicle's motion parameters, wherein the target passage cycle is the closest green light cycle at present;

[0015] The cluster division module is used to divide each vehicle into clusters based on the maximum travel distance of each vehicle in the same lane and its own distance to the stop line ahead, and obtain the cluster division result. In the cluster division result, one cluster corresponds to the cluster of vehicles that passed the intersection ahead before the end of a travel cycle.

[0016] The speed guidance curve construction module is used to construct the speed guidance curve function of the lead vehicle based on the planned average speed of the lead vehicle in the cluster. The lead vehicle is the first connected vehicle in the cluster.

[0017] The optimization and solution module is used to optimize and solve the speed guidance curve function of the navigator vehicle based on the pre-established cluster energy consumption minimization optimization function, so as to obtain the target speed guidance curve of the navigator vehicle.

[0018] The first control module is used to control the lead vehicle in the cluster to guide the car along the curve at its own target speed.

[0019] The following state determination module is used to determine the following state of each connected vehicle and each traditional vehicle in the cluster based on the driving state of the lead vehicle in the cluster, the pre-established connected vehicle power model and the traditional vehicle power model.

[0020] The second control module is used to control each vehicle in the cluster to follow the others based on their own following status, so that all vehicles in the cluster can pass through the intersection ahead before the end of the corresponding traffic cycle.

[0021] A third aspect of this application provides an electronic device, including: a processor, a memory, and a computer program stored in the memory and running on the processor, wherein the computer program, when executed by the processor, implements the steps of a traffic light control method for vehicle grouping as described in the first aspect of this application.

[0022] The fourth aspect of this application provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of a traffic light control method for vehicle grouping as described in the first aspect of this application.

[0023] The traffic light control method for vehicle grouping provided in this application has the following advantages:

[0024] This application provides a traffic light control method for vehicle grouping. Based on vehicle motion parameters, the method determines the maximum travel distance of each vehicle before the end of a target traffic cycle, where the target traffic cycle is the closest current green light cycle. Based on the maximum travel distance of each vehicle in the same lane and its distance to the stop line ahead, the method groups each vehicle into a cluster, obtaining cluster division results. In the cluster division results, each cluster corresponds to a group of vehicles passing through the intersection before the end of a traffic cycle. Based on the planned average speed of the lead vehicle in the cluster, a speed guidance curve function is constructed for the lead vehicle. The lead vehicle is the... The first connected vehicle in the cluster; based on a pre-established cluster energy minimization optimization function, the speed guidance curve function of the lead vehicle is optimized to obtain the target speed guidance curve of the lead vehicle; the lead vehicle in the cluster is controlled to drive according to its own target speed guidance curve; according to the driving state of the lead vehicle in the cluster, the pre-established connected vehicle power model and traditional vehicle power model, the following state of each connected vehicle and each traditional vehicle in the cluster is determined; each vehicle in the cluster is controlled to follow according to its own following state so that all vehicles in the cluster pass the intersection ahead before the end of the corresponding traffic cycle. This method aims to reduce the frequent start and stop of vehicles near the signal light zone and green wave traffic. In the guidance area near the signal light zone, the vehicle group is divided into clusters according to the signal cycle, and a controllable CAV vehicle is used as the lead vehicle. The fuel economy of the lead vehicle and other vehicles following it in the cluster is comprehensively considered. By optimizing the speed and trajectory of the lead vehicle and influencing and guiding the driving behavior of HV vehicles, the optimal passage of the entire vehicle group is achieved. This method can achieve efficient utilization of road network spatiotemporal resources, and can save energy, reduce emissions, and effectively improve traffic efficiency. Attached Figure Description

[0025] To more clearly illustrate the technical solutions of the embodiments of this application, the drawings used in the description of the embodiments of this application will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 A flowchart illustrating a traffic signal control method for vehicle grouping is shown in one embodiment of this application;

[0027] Figure 2 This is a schematic diagram illustrating the scene and cluster division in a traffic light control method for vehicle grouping according to an embodiment of this application;

[0028] Figure 3 This is a schematic diagram of a traffic flow-density basic graph in a traffic light control method for vehicle grouping according to an embodiment of this application;

[0029] Figure 4 This is a schematic diagram illustrating the formation and dissipation of queuing vehicles based on wave speed transmission, as shown in one embodiment of this application.

[0030] Figure 5 This is another flowchart illustrating a traffic light control method for vehicle grouping according to an embodiment of this application;

[0031] Figure 6 This is a schematic diagram illustrating a traffic light control system for vehicle grouping, as shown in one embodiment of this application. Detailed Implementation

[0032] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0033] refer to Figure 1 , Figure 1 This is a flowchart illustrating a traffic light control method for vehicle grouping according to one embodiment of this application, as shown below. Figure 1 As shown, the method includes:

[0034] Step S1: Based on the vehicle's motion parameters, determine the maximum travel distance of the vehicle before the end of the target traffic cycle, where the target traffic cycle is the closest current green light cycle.

[0035] In this embodiment, this application aims to propose a green wave traffic strategy for mixed traffic conditions (i.e., mixed traffic including both connected vehicles and conventional vehicles) near the signal light zone, from the perspective of vehicle clustering. It introduces the concept and methods of swarm intelligence into the near-signal zone to explore the evolutionary patterns of vehicle clusters. Controllable CAV vehicles within the mixed vehicle cluster are used as the main body for clustered and cooperative guidance and control, indirectly guiding and constraining conventional vehicles to achieve overall traffic optimization.

[0036] In this embodiment, the traffic signal control method for vehicle grouping provided in this application is applied to a single-lane mixed traffic scenario near the signal zone of a traffic signal. Specifically, this method is used to perform vehicle grouping and communication control on a single lane. For example, when a road includes three lanes, the method will perform vehicle grouping and communication control on each lane separately. Mixed traffic refers to a situation where a single lane includes both connected vehicles and conventional vehicles. The method provided in this application is also applicable when a single lane only includes connected vehicles.

[0037] In this embodiment, the connected vehicle is a CAV (Continuously Operated Vehicle) equipped with V2X communication equipment, while the traditional vehicle (i.e., a human-driven, HV) is a vehicle without V2X communication equipment and no communication capabilities. Within the connected vehicle's perception and communication range, the traffic light timing and current status at the intersection can be transmitted to the connected vehicle in real time through the vehicle-to-infrastructure communication system. Furthermore, the CAV can obtain real-time motion information from other vehicles and roadside facilities through real-time interconnection, allowing the autonomous driving system to precisely adjust the vehicle's physical motion state. Traditional vehicles, on the other hand, cannot obtain traffic light information before entering the traffic light's field of vision. These vehicles follow the vehicle in front or proceed at an ideal speed until they enter the traffic light's field of vision, at which point they adjust their vehicle state based on the current traffic light phase information.

[0038] In this embodiment, the motion parameters of vehicles within the same lane are obtained as follows: the connected roadside system monitors and determines the relevant motion parameters of the vehicle through its own sensing module; for motion parameters that the connected roadside system cannot sense, the connected roadside system selects matching motion parameter values ​​based on the vehicle's model. For example, the connected roadside system pre-stores reasonable theoretical values ​​for various motion parameters when the vehicle model is a sedan, and pre-stores reasonable theoretical values ​​for various motion parameters when the vehicle model is an SUV, etc., and selects matching theoretical values ​​for motion parameters based on the determined vehicle model; when the vehicle is a connected vehicle, the vehicle can also directly interact with the connected roadside system and feed back its relevant motion parameters to the connected roadside system.

[0039] In this embodiment, after receiving the relevant motion parameters of each vehicle, the connected roadside system calculates the relevant motion parameters of the same vehicle and determines the maximum travel distance that the vehicle can reach before the end of the target travel cycle based on the calculation results. The target travel cycle is the nearest green light cycle to the vehicle. For example, if the nearest traffic light ahead of the vehicle is currently red, the target travel cycle is the green light cycle following that red light; if the nearest traffic light ahead of the vehicle is currently green, the target travel cycle is the green light cycle following that green light. Using the same calculation method, a corresponding maximum travel distance is calculated for each vehicle. The motion parameters used to determine the maximum travel distance of a vehicle include: the time required for the vehicle to accelerate to the road speed limit (e.g., if the road speed limit is 60 km / h, this represents the time required for the vehicle to accelerate to 60 km / h), the road speed limit of the road the vehicle is on, the vehicle's target acceleration, the vehicle's speed at time t, the end time of the target travel cycle, and the time the vehicle enters the speed guidance area. The target acceleration of the vehicle refers to the upper limit of acceleration (e.g., 2 m / s²) pre-set for the vehicle in urban environments, prioritizing safety and comfort. 2 In subsequent implementations, the target deceleration of the vehicle refers to the upper limit of deceleration (e.g., -4 m / s²) pre-set for the vehicle in urban environments, prioritizing safety and comfort. 2 ).

[0040] The speed guidance zone is a range of preset distances from the stop line at the intersection ahead. Once a vehicle is detected entering this zone, the maximum travel distance that the vehicle can reach from its current position before the end of the target travel cycle is determined. At the same time, the time point for vehicle travel is also calculated and planned when the vehicle enters this zone.

[0041] Step S2: Based on the maximum travel distance of each vehicle in the same lane and its distance to the stop line ahead, each vehicle is divided into clusters to obtain cluster division results. In the cluster division results, each cluster corresponds to a group of vehicles that passed through the intersection ahead before the end of a travel cycle.

[0042] In this embodiment, after obtaining the maximum travel distance for each vehicle in the same lane through step S1, the maximum travel distance for each vehicle is compared with the distance from its current position to the stop line ahead. Vehicles whose maximum travel distance is greater than the distance from their current position to the stop line ahead are grouped into one cluster, while other vehicles are grouped into another cluster. Each cluster corresponds to the group of vehicles that pass through the intersection ahead before the end of a traffic cycle.

[0043] Step S3: Based on the planned average speed of the lead vehicle in the cluster, construct the speed guidance curve function of the lead vehicle, which is the first connected vehicle in the cluster.

[0044] In this embodiment, after obtaining the cluster partitioning result through step S2, the first connected vehicle in each cluster is determined as the lead vehicle for that cluster. Then, based on the planned average speed of the lead vehicle in the cluster, a speed guidance curve function for that lead vehicle is constructed.

[0045] Step S4: Based on the pre-established cluster energy consumption minimization optimization function, optimize and solve the speed guidance curve function of the lead vehicle to obtain the target speed guidance curve of the lead vehicle.

[0046] In this embodiment, a cluster energy consumption minimization optimization function is pre-established with the goal of minimizing cluster energy consumption. Based on this cluster energy consumption minimization optimization function, a heuristic algorithm is used to optimize and solve the speed guidance curve function of the lead vehicle, with the goal of minimizing cluster energy consumption, to obtain the target speed guidance curve of the lead vehicle. This target speed guidance curve is used to determine the speed of the lead vehicle during the guidance process.

[0047] Step S5: Control the lead vehicle in the cluster to guide the vehicle along the curve at its own target speed.

[0048] In this embodiment, based on the target speed guidance curve of the lead vehicle in the cluster, the speed of the lead vehicle at each moment during the guidance process is determined, and the lead vehicle is controlled to drive based on the determined speed at each moment.

[0049] Step S6: Based on the driving status of the lead vehicle in the cluster, the pre-established connected vehicle dynamics model and traditional vehicle dynamics model, determine the following status of each connected vehicle and each traditional vehicle in the cluster.

[0050] In this embodiment, this application pre-establishes a connected vehicle dynamics model for connected vehicles and a conventional vehicle dynamics model for conventional vehicles. The dynamics models constrain the following state of the corresponding vehicles when following the lead vehicle in the cluster. During the guided driving process of the lead vehicle in the cluster, the following state of each connected vehicle in the cluster is determined based on the lead vehicle's driving state and the connected vehicle dynamics model; similarly, the following state of each conventional vehicle in the cluster is determined based on the lead vehicle's driving state and the conventional vehicle dynamics model. The motion parameters involved in the following state include acceleration and the safe distance between vehicles.

[0051] Step S7: Control each vehicle in the cluster to follow based on its own following status, so that all vehicles in the cluster can pass the intersection ahead before the end of the corresponding traffic cycle.

[0052] In this embodiment, after obtaining the following status of each vehicle in the cluster through step S6, the system controls each vehicle in the cluster to follow based on its own following status, so that all vehicles in the cluster can pass through the intersection ahead before the end of the corresponding traffic cycle.

[0053] This application provides a traffic light control method for vehicle grouping. Based on vehicle motion parameters, the method determines the maximum travel distance of each vehicle before the end of a target traffic cycle, where the target traffic cycle is the closest current green light cycle. Based on the maximum travel distance of each vehicle in the same lane and its distance to the stop line ahead, the method groups each vehicle into a cluster, obtaining cluster division results. In the cluster division results, each cluster corresponds to a group of vehicles passing through the intersection before the end of a traffic cycle. Based on the planned average speed of the lead vehicle in the cluster, a speed guidance curve function is constructed for the lead vehicle. The lead vehicle is the... The first connected vehicle in the cluster; based on a pre-established cluster energy minimization optimization function, the speed guidance curve function of the lead vehicle is optimized to obtain the target speed guidance curve of the lead vehicle; the lead vehicle in the cluster is controlled to drive according to its own target speed guidance curve; according to the driving state of the lead vehicle in the cluster, the pre-established connected vehicle power model and traditional vehicle power model, the following state of each connected vehicle and each traditional vehicle in the cluster is determined; each vehicle in the cluster is controlled to follow according to its own following state so that all vehicles in the cluster pass the intersection ahead before the end of the corresponding traffic cycle. This method aims to reduce the frequent start and stop of vehicles near the signal light zone and green wave traffic. In the guidance area near the signal light zone, the vehicle group is divided into clusters according to the signal cycle, and a controllable CAV vehicle is used as the lead vehicle. The fuel economy of the lead vehicle and other vehicles following it in the cluster is comprehensively considered. By optimizing the speed and trajectory of the lead vehicle and influencing and guiding the driving behavior of HV vehicles, the optimal passage of the entire vehicle group is achieved. This method can achieve efficient utilization of road network spatiotemporal resources, and can save energy, reduce emissions, and effectively improve traffic efficiency.

[0054] In conjunction with the above embodiments, in one implementation, this application also provides a traffic light control method for vehicle grouping. In this traffic light control method for vehicle grouping, step S2 may include steps S21 to S24:

[0055] Step S21: All vehicles in the same lane whose maximum travel distance is greater than their own distance to the stop line ahead are divided into a first cluster. The first cluster is a cluster of vehicles that pass through the intersection ahead before the end of the target travel cycle.

[0056] In this embodiment, this application only divides vehicles into two clusters: one cluster consists of vehicles that pass through the intersection before the end of the target traffic cycle, and the other cluster consists of vehicles that pass through the intersection before the end of the next traffic cycle of the target traffic cycle. Specifically, all vehicles in the same lane whose maximum travel distance is greater than their distance to the stop line ahead are grouped into the first cluster. This first cluster consists of vehicles that pass through the intersection before the end of the target traffic cycle. In other words, through traffic control, all vehicles in this first cluster will pass through the intersection in the nearest current green light cycle.

[0057] Step S22: All vehicles in the same lane whose maximum travel distance is less than or equal to their distance to the stop line ahead are divided into a second group. The second group is a group of vehicles that will pass through the intersection ahead before the end of the next travel cycle of the target travel cycle.

[0058] In this embodiment, all vehicles in the same lane whose maximum travel distance is less than or equal to their distance to the stop line ahead are divided into a second cluster. This second cluster is a cluster of vehicles that will pass through the intersection ahead before the end of the next traffic cycle of the target traffic cycle. In other words, through traffic control, all vehicles in this second cluster will not pass through the intersection ahead in the current closest green light cycle, but will all pass through the intersection ahead before the end of the next green light cycle of the current closest green light cycle.

[0059] Step S23: Based on the following status of vehicles in the cluster, determine the maximum number of vehicles that can pass through in a single passage cycle using a passage algorithm. The passage algorithm is as follows:

[0060]

[0061] in, This represents the expected speed of the lead vehicle in the cluster. Indicates rounding down. This represents the expected safe following distance among vehicles in the cluster when a steady state is reached. Indicates the stationary safe distance between the vehicle and the vehicle in front. Indicates the safe time interval between the vehicle and the vehicle in front. Indicates the maximum number of passes. Indicates the start time of the traffic cycle. This indicates the end time of the traffic cycle.

[0062] In this embodiment, to ensure more accurate vehicle cluster segmentation results, after obtaining the cluster segmentation results based on the maximum travel distance, this application further adjusts the obtained cluster segmentation results based on the maximum number of vehicles that can pass through in a single travel cycle. Specifically, according to the following state of the vehicles in the cluster, the following state parameters of the vehicles are substituted into the travel algorithm for calculation to obtain the maximum number of vehicles that can pass through in a single travel cycle.

[0063] Step S24: Based on the maximum number of passages, perform cluster optimization adjustment on the first cluster and the second cluster obtained by the division to obtain the adjusted first cluster and the second cluster.

[0064] In this embodiment, after calculating the maximum number of vehicles passing through a single traffic cycle in step S23, the previously divided first and second clusters are optimized and adjusted. Specifically, when the total number of all vehicles in the previously divided first cluster is greater than the maximum number of vehicles passing through, starting from the last vehicle in the first cluster, the last vehicles are excluded from the first cluster, so that the total number of vehicles in the first cluster is exactly the maximum number of vehicles passing through. At this time, the vehicles excluded from the first cluster are assigned to the second cluster. Then, the total number of all vehicles in the second cluster after receiving the vehicles excluded from the first cluster is determined. If the total number of all vehicles in the second cluster is greater than the maximum number of vehicles passing through, again starting from the last vehicle in the second cluster, the last vehicles are excluded from the second cluster, so that the total number of vehicles in the second cluster is exactly the maximum number of vehicles passing through. If the total number of all vehicles in the new second cluster is less than or equal to the maximum number of vehicles passing through, no adjustment is made to the second cluster. If the total number of vehicles in the first cluster obtained from the previous division is less than or equal to the maximum passage capacity, then no adjustment is made to the first cluster. Next, the total number of vehicles in the second cluster obtained from the previous division is determined. If this total number is also less than or equal to the maximum passage capacity, then no adjustment is made to the first cluster. However, if the total number is greater than the maximum passage capacity, starting with the last vehicle in the second cluster, vehicles further back are excluded from the second cluster, so that the total number of vehicles in the second cluster is exactly the maximum passage capacity. Subsequent traffic control is then performed using the adjusted first and second clusters.

[0065] In conjunction with the above embodiments, in one implementation, this application also provides a traffic light control method for vehicle grouping. In this traffic light control method for vehicle grouping, before step S1, it further includes: real-time monitoring of whether vehicles enter a speed guidance area near the signal zone of the current road, wherein the speed guidance area is a range of a preset distance from the stop line at the intersection ahead.

[0066] In this application, if the speed guidance zone for real-time monitoring of whether a vehicle enters the near-signal zone of the current road is included before step S1, step S1 may include: substituting the motion parameters of the vehicle entering the speed guidance zone into a pre-established travel distance determination algorithm to calculate the maximum travel distance of the vehicle before the end of the target travel cycle; the expression of the travel distance determination algorithm is:

[0067]

[0068] in, The time required for a vehicle to accelerate to the speed limit of the road it is on. The speed limit for the road where the vehicle is located. For the target acceleration of the vehicle, Let be the speed of the vehicle at time t. This represents the maximum travel distance that a vehicle can reach before the end of the target travel cycle. The end time of the target traffic cycle. The moment a vehicle enters the speed guidance zone.

[0069] In this embodiment, the application pre-defines a speed guidance zone near the traffic lights on the road. When each vehicle enters this speed guidance zone, the maximum travel distance it can reach before the end of the target traffic cycle is determined. For example... Figure 2 As shown, this application preferably sets the total length of a single-lane mixed traffic scenario near the traffic light signal zone to 500m. Simultaneously, the field of vision for traditional vehicles is set at 50m, and the communication range for connected vehicles to obtain traffic light information is set at 200 meters. This communication range corresponds to the length of the speed guidance zone. It should be understood that the aforementioned values ​​for the communication range for connected vehicles to obtain traffic light information, the total length of the single-lane mixed traffic scenario, and the field of vision for traditional vehicles are all preferred values, and they can be set to other values ​​according to specific planning and control scenarios. The reason for preferably setting the communication range for connected vehicles to obtain traffic light information to 200 meters is that this is the reliable range within which high-definition cameras can identify traffic lights and establish connections.

[0070] In this embodiment, after each vehicle enters the speed guidance range of the signal light near the signal zone, the connected roadside system, based on the obtained vehicle motion parameters, substitutes the vehicle motion parameters into a pre-established travel distance determination algorithm to calculate the maximum travel distance of the vehicle before the end of the target travel cycle.

[0071] In conjunction with the above embodiments, in one implementation, this application also provides a traffic light control method for vehicle grouping. In this traffic light control method for vehicle grouping, before step S3, the method further includes a step of determining the planned average speed of the lead vehicle in the group, which includes steps S3a to S3e:

[0072] Step S3a: Determine whether there are conventional vehicles ahead of the lead vehicle in the same cluster.

[0073] In this embodiment, when determining the planned average speed of the lead vehicle, the application determines the planned average speed based on the lead vehicle's specific location within the cluster. First, based on the fact that the aggregation and dissipation process of vehicle queues under signal light switching conditions near the signal zone satisfies the LWR (Light Hill, Whitham and Richards, LWR) model, a basic flow-density map of the current road segment is obtained. This map can be used to characterize the change in flow density as traffic transitions from free flow to congested flow, and can be obtained by fitting historical monitoring data for that road segment. For example... Figure 3 As shown in the diagram, point A represents the state of the vehicle before entering the approach signal zone (i.e., the speed guidance starting point), point B corresponds to the state when the queue of vehicles disperses, and point C corresponds to the state of the queue of vehicles. Correspondingly, Traffic flow on the road at the initial moment when the lead vehicle begins speed guidance. The road density at the initial moment when the lead vehicle begins speed guidance. This represents the saturation flow rate of free flow, indicating that after the traffic light turns green, the area near the signal light will release queued vehicles at the road's saturation flow rate, generating dissipation waves that propagate backward. The saturation density of the free flow. This refers to the maximum density of vehicles queuing at the stop line after the traffic light turns red. When the traffic density suddenly changes for some reason, a traffic wave is created and propagates backward. For example... Figure 4 As shown, a certain flow of conventional vehicles entering the signal zone will generate queued vehicles and form traffic waves, which can be used to estimate the queued vehicles (i.e., the queue length and queue dissipation time). Figure 4 Section A in Figure 3 Point A in the diagram corresponds to, Figure 4 Segment B in Figure 3 Point B in the diagram corresponds to... Figure 4 Section C in Figure 3 Point C in the diagram corresponds to this. Therefore, this application first determines whether there is a conventional vehicle belonging to the same cluster as the lead vehicle in front of it within the same cluster.

[0074] Step S3b: When there are no conventional vehicles in front of the lead vehicle, determine the planned average speed of the lead vehicle based on the motion parameters of the lead vehicle.

[0075] In this embodiment, if there are no traditional vehicles belonging to the same cluster in front of the lead vehicle, there is no need to consider the queuing of traditional vehicles. In this case, the planned average speed can be determined directly based on the motion parameters of the lead vehicle.

[0076] Step S3c: When there are conventional vehicles in front of the lead vehicle, the wave velocity of the shock wave is determined by the first wave velocity algorithm, and the wave velocity of the dissipation wave is determined by the second wave velocity algorithm.

[0077] The expression for the first wave speed algorithm is: ,in, The wave speed of the shock wave (the wave speed of the shock wave is as follows) Figure 3 (slope of the line connecting points AC) This is the maximum density of traditional vehicles stopping and queuing before the stop line after the traffic light turns red. Traffic flow on the road at the initial moment when the lead vehicle begins speed guidance. The road density at the initial moment when the lead vehicle begins speed guidance;

[0078] The expression for the second wave speed algorithm is: ,in, The wave speed of the dissipating wave (the wave speed of the dissipating wave is as follows) Figure 3 (slope of the line connecting points B and C) The saturation density of the free flow. The saturation flow rate of free flow indicates that after the traffic light turns green, the queuing vehicles in the near-signal zone will be released at the road's saturation flow rate, generating a dissipation wave that propagates backward.

[0079] In this embodiment, if there are conventional vehicles belonging to the same cluster in front of the lead vehicle, the queuing of conventional vehicles needs to be considered. The specific process is as follows: first, the wave velocity of the shock wave is determined by the first wave velocity algorithm, and then the wave velocity of the dissipation wave is determined by the second wave velocity algorithm.

[0080] Step S3d: Based on the wave velocity of the shock wave and the wave velocity of the dissipation wave, determine the queue length and queue dissipation time of the conventional vehicles in front of the lead vehicle using the queue length algorithm and the dissipation time algorithm, respectively.

[0081] The queue length algorithm is expressed as:

[0082]

[0083] The dissipation time algorithm is expressed as:

[0084]

[0085] in, This indicates the starting moment when the lead vehicle begins speed guidance. express Average vehicle speed at any given time This indicates the distance from the current position of the lead vehicle to the stop line ahead. This indicates the length of the queue of vehicles ahead. Indicates the current time The time difference between the light turning on and the next green light signal Indicates the queue dissipation time.

[0086] In this embodiment, based on the wave velocity of the shock wave and the wave velocity of the dissipation wave obtained through step S3c, they are substituted into the queue length algorithm and the dissipation time algorithm respectively to estimate the queue length and queue dissipation time of the conventional vehicles in front of the lead vehicle that belong to the same cluster.

[0087] Step S3e: Determine the planned average speed of the lead vehicle based on the queue length, the queue dissipation time, and the motion parameters of the lead vehicle.

[0088] In this embodiment, the planned average speed of the lead vehicle is determined based on the queue length and queue dissipation time of the traditional vehicles belonging to the same cluster in front of the lead vehicle as determined in step S3d, as well as the motion parameters of the lead vehicle.

[0089] In conjunction with the above embodiments, in one implementation, this application also provides a traffic light control method for vehicle grouping. In this traffic light control method for vehicle grouping, step S3b may include steps S3b_1 to S3b_3:

[0090] Step S3b_1: Substitute the motion parameters of the first lead vehicle into the first algorithm for calculation to determine the range of the planned average speed of the first lead vehicle. The first lead vehicle is the lead vehicle in the first cluster.

[0091] The first algorithm is:

[0092] in, This indicates the planned average speed of the lead vehicle. This indicates the distance from the current position of the lead vehicle to the stop line ahead. This marks the starting moment when the lead vehicle begins speed guidance. Indicates the start time of the target passage cycle. Indicates the end time of the target passage cycle. This indicates the speed limit for the road where the corresponding vehicle is located;

[0093] In this embodiment, when there are no traditional vehicles belonging to the same cluster in front of the first lead vehicle, the planned average speed range of the first lead vehicle can be obtained by directly substituting the relevant motion parameters of the first lead vehicle into the first algorithm for calculation.

[0094] Step S3b_2: Substitute the motion parameters of the second lead vehicle into the second algorithm for calculation to determine the range of the planned average speed of the second lead vehicle. The second lead vehicle is the lead vehicle in the second cluster.

[0095] The second algorithm is:

[0096]

[0097] in, This indicates the planned average speed of the second lead vehicle. This indicates the distance from the current position of the second lead vehicle to the stop line ahead. This marks the starting moment when the second lead vehicle begins speed guidance. This indicates the start time of the next traffic cycle following the target traffic cycle. This indicates the end time of the next traffic cycle following the target traffic cycle. The duration of the traffic light cycle. This indicates the speed limit for the road where the corresponding vehicle is located.

[0098] In this embodiment, when there are no traditional vehicles belonging to the same cluster in front of the second lead vehicle, the planned average speed range of the second lead vehicle can be obtained by directly substituting the relevant motion parameters of the second lead vehicle into the second algorithm for calculation.

[0099] Step S3b_3: Determine the maximum value in the range of the planned average speed of the navigator as the planned average speed of the navigator.

[0100] In this embodiment, to ensure traffic efficiency, the maximum value of the planned average speed is taken as the planned average speed of the corresponding lead vehicle. After obtaining the range of planned average speed values ​​for the lead vehicles in the corresponding cluster through the above steps, the maximum value within this range is determined as the planned average speed of the lead vehicle.

[0101] In conjunction with the above embodiments, in one implementation, this application also provides a traffic light control method for vehicle grouping. In this traffic light control method for vehicle grouping, step S3e may include steps S3e_1 to S3e_3:

[0102] Step S3e_1: Substitute the determined queue length, queue dissipation time, and motion parameters of the first lead vehicle into the third algorithm for calculation to determine the range of planned average speed values ​​for the first lead vehicle.

[0103] The third algorithm is as follows:

[0104]

[0105] in, This indicates the planned average speed of the lead vehicle. and These represent the queue length and queue dissipation time of the conventional vehicles in front of the first lead vehicle in the first cluster.

[0106] In this embodiment, when there are traditional vehicles belonging to the same cluster in front of the first lead vehicle, the planned average speed range of the first lead vehicle can be obtained by substituting the relevant motion parameters of the first lead vehicle, as well as the calculated queue length and queue dissipation time corresponding to the cluster to which the first lead vehicle belongs, into the third algorithm.

[0107] Step S3e_2: Substitute the determined queue length, queue dissipation time, and motion parameters of the second lead vehicle into the fourth algorithm for calculation to determine the range of planned average speed values ​​for the second lead vehicle.

[0108] The fourth algorithm is as follows:

[0109]

[0110] in, This indicates the planned average speed of the second lead vehicle. and These represent the queue length and queue dissipation time of the conventional vehicles in front of the second lead vehicle in the second cluster.

[0111] In this embodiment, when there are traditional vehicles belonging to the same cluster in front of the second lead vehicle, the planned average speed range of the second lead vehicle can be obtained by substituting the relevant motion parameters of the second lead vehicle, as well as the calculated queue length and queue dissipation time corresponding to the cluster to which the second lead vehicle belongs, into the fourth algorithm.

[0112] Step S3e_3: Determine the maximum value in the range of the planned average speed of the navigator as the planned average speed of the navigator.

[0113] In this embodiment, to ensure traffic efficiency, the maximum value of the planned average speed is taken as the planned average speed of the corresponding lead vehicle. After obtaining the range of planned average speed values ​​for the lead vehicles in the corresponding cluster through the above steps, the maximum value within this range is determined as the planned average speed of the lead vehicle.

[0114] In conjunction with the above embodiments, in one implementation, this application also provides a traffic light control method for vehicle grouping. In this traffic light control method for vehicle grouping, the speed guidance curve function of the lead vehicle is constructed as follows: ,in, Let t represent the guiding speed of the lead vehicle at time point t, where t indicates the corresponding time point in the speed guidance process. and For the unknown parameters of the solution to the requirement, The planned average speed of the lead vehicle within the speed guidance zone. The current speed of the lead car With the planned average speed deviation, that is .

[0115] In conjunction with the above embodiments, in one implementation, this application also provides a traffic light control method for vehicle grouping. In this traffic light control method for vehicle grouping, the pre-established cluster energy consumption minimization optimization function is:

[0116]

[0117] Constraints:

[0118] in, The starting moment for the lead vehicle to begin speed guidance (that is, the moment it enters the speed guidance area). The total number of vehicles in the cluster to which the lead vehicle belongs; A model for evaluating the instantaneous fuel consumption of the i-th vehicle in the cluster; The length of the queue of traditional vehicles in front of the lead vehicle in the cluster; The speed of the lead vehicle in the cluster at time t; Indicates the current time The time difference between the light turning on and the next green light signal; This indicates the distance from the current position of the lead vehicle to the stop line ahead; Let represent the speed of the nth vehicle at time t; , , , , , , These are constant coefficients; and These represent the position difference between the nth car and the (n-1th car) in the cluster; Indicates the safe time interval between the vehicle and the vehicle in front; and These are the maximum and minimum speeds of the nth vehicle in the cluster, respectively. Let be the acceleration of the nth vehicle in the cluster at time t; and Let x and y be the target acceleration and target deceleration of the nth vehicle in the cluster, respectively.

[0119] In conjunction with the above embodiments, in one implementation, this application also provides a traffic light control method for vehicle grouping. In this traffic light control method for vehicle grouping, the pre-established connected vehicle dynamics model is as follows:

[0120]

[0121] in, Let be the acceleration of the nth vehicle in the cluster at time t. This represents the number of vehicles in the cluster that are ahead of the nth vehicle. This indicates the sensitivity of the nth vehicle to the speed difference. This represents the sensitivity of the nth vehicle to the distance difference. and Let be the speeds of the nth and ith vehicles in the cluster at time t, respectively. and These are the position information of the nth and ith vehicles in the cluster at time t, respectively. This represents the expected distance between the nth car and the ith car, where i is a value less than n. Indicates the stationary safe distance between the vehicle and the vehicle in front. Indicates the safe time interval between the vehicle and the vehicle in front;

[0122] The pre-established traditional vehicle dynamics model is as follows:

[0123]

[0124] in, This represents the position difference between the nth car in the cluster and the (n-1)th car ahead. This represents the speed difference between the nth car in the cluster and the (n-1)th car ahead at time t; This represents the target acceleration of the nth vehicle in the cluster. This represents the target deceleration of the nth vehicle in the cluster. Indicates the vehicle's desired speed. The acceleration index, which represents the desired speed of a vehicle. Indicates the stationary safe distance between the vehicle and the vehicle in front. Indicates the safe time interval between the vehicle and the vehicle in front. This represents the expected distance between the nth vehicle and the vehicle in front of it.

[0125] In conjunction with the above embodiments, in one implementation, this application also provides a traffic light control method for vehicle grouping. In this traffic light control method for vehicle grouping, step S4 may include steps S41 to S45:

[0126] Step S41: Based on the constraints, a preset number of particles are randomly generated for the unknown parameters in the velocity guidance curve function, and each particle is assigned a random position and velocity, wherein the position of the particle is a potential solution of the unknown parameters.

[0127] In this embodiment, one optional implementation method for solving the unknown parameters in the speed guidance curve function of the navigator is to use the particle swarm optimization algorithm, a heuristic algorithm, for solving the problem. Specifically, a preset number of particles X1 to Xn are first generated for the unknown parameters in the speed guidance curve function of the navigator. Based on the constraints in the swarm energy minimization optimization function, each particle is assigned a random position and velocity. The particle position represents a potential solution for the unknown parameters, and the particle velocity represents the direction and step size of the particle's movement in the solution space from its current position. For example, if the unknown parameters are s and a, and a particle X1 is randomly assigned the position X1(s1, a1), then s1 is a potential solution for the unknown parameter s, and a1 is a potential solution for the unknown parameter. The preset number can be set according to the actual scenario and is not specifically limited here.

[0128] Step S42: Determine the fitness of each particle position using the cluster energy consumption minimization optimization function.

[0129] In this embodiment, the particle position of each particle is calculated using a pre-established cluster energy minimization optimization function to obtain the fitness corresponding to each particle position. The fitness of a particle position refers to the energy consumption of the corresponding cluster under the potential solution at that particle position. An energy consumption scenario for the corresponding cluster is calculated for each particle position.

[0130] Step S43: Determine the global optimal position and the individual optimal position of each particle based on the fitness of each particle's position. The global optimal position is the optimal position determined by the particle cluster, and the individual optimal position is the optimal position determined by the corresponding particle itself.

[0131] In this embodiment, after obtaining the fitness corresponding to the particle position of each particle, the particle position corresponding to the fitness with the smallest value (i.e., the current minimum energy consumption) is selected from all fitness values ​​as the current global optimal position. Since each particle has only taken a particle position once, the current particle position of each particle is its own individual optimal position. For example, if particle X1 has only taken the particle position X1(s1,a1) once, then particle X has only one corresponding fitness value, and the individual optimal position of particle X1 is particle position X1(s1,a1).

[0132] Step S44: Update the position and velocity of the particle based on its current position and velocity, its own optimal position and global optimal position to obtain the new position and velocity of the particle.

[0133] In this embodiment, the method for updating the position and velocity of each particle is the same, and a single particle is used as an example for explanation. Based on the particle's current position and velocity, as well as its individual optimal position and the current global optimal position, a new particle position and velocity are determined for the particle.

[0134] Using the same implementation method, each particle can redetermine a new particle position and velocity. Each time a particle redetermines its new particle position and velocity, it is counted as one iteration. After obtaining the new particle position and velocity, based on the obtained new particle position and velocity, the fitness corresponding to the new particle position can be calculated using the same implementation method as in step S42. Using the same implementation method, a corresponding fitness will be calculated based on the new particle position of each particle. At this time, based on the fitness of each particle position obtained in the second round of iteration and the previously determined global optimal position (which is actually also a fitness), a new global optimal position (that is, the particle position with the smallest fitness) is determined. At this time, after the second round of iteration, each particle has two particle positions, and for each particle, the particle position with the smallest corresponding fitness is selected from its two particle positions as its individual optimal position.

[0135] Step S45: After performing a preset number of iterations, the global optimal position is determined as the optimal solution of the unknown parameters in the speed guidance curve function, so as to obtain the target speed guidance curve of the navigator vehicle.

[0136] In this embodiment, after a preset number of iterations, the final globally optimal position (that is, the final position of the particle with the smallest fitness) is determined as the optimal solution of the unknown parameters in the speed guidance curve function of the navigator. Then, the optimal solution is substituted into the speed guidance curve function of the navigator to obtain the target speed guidance curve of the navigator.

[0137] In this embodiment, the traffic signal control method for vehicle grouping provided in this application is as follows: Figure 5 As shown, the system first determines whether each vehicle has entered the speed guidance zone through the connected roadside system. For vehicles entering the speed guidance zone, it acquires vehicle motion parameters and traffic light information, then determines the maximum travel distance a vehicle can reach before the end of the target travel cycle. Based on this maximum travel distance, it determines whether the vehicle can pass the next intersection before the end of the target travel cycle. If it cannot pass the next intersection before the end of the target travel cycle, it is assigned to the next travel cycle. Vehicles that can pass the next intersection before the end of the target travel cycle are assigned to the first cluster. Then, it determines the maximum number of vehicles that can pass in a single travel cycle. If the number of vehicles in the first cluster is less than or equal to the maximum number of vehicles, the current first cluster is directly determined as the final first cluster. If the number of vehicles in the first cluster exceeds the maximum number of vehicles, the vehicles exceeding the maximum number of vehicles in the first cluster are assigned to the next travel cycle cluster, resulting in the final first cluster. The number of vehicles in the second cluster cannot exceed the maximum number of vehicles. After the cluster is divided, it is determined whether a vehicle is a lead vehicle. If not, it follows the lead vehicle based on the corresponding power model. If it is a lead vehicle, it is further determined whether there are conventional vehicles belonging to the same cluster ahead of it. Based on the presence of conventional vehicles ahead and its own cluster, the appropriate algorithm is selected to calculate the range of planned average speeds, and the largest planned average speed is selected as the lead vehicle's final planned average speed. Then, based on the established cluster energy minimization optimization function, the speed guidance curve function established based on the lead vehicle's final planned average speed is solved using the particle swarm optimization algorithm to obtain the lead vehicle's target speed guidance curve. After obtaining the target speed guidance curve of the lead vehicle, the speed control commands at each moment are determined based on the target speed guidance curve and sent to the lead vehicle by the connected roadside system for speed control. When other vehicles in the cluster follow, for connected vehicles, the connected roadside system controls them based on the corresponding connected vehicle power model, while for conventional vehicles, the corresponding conventional vehicle power model is configured in the vehicle controller, and the vehicle controller follows based on its own conventional vehicle power model.

[0138] Based on the same inventive concept, one embodiment of this application provides a traffic light control system for vehicle grouping, such as... Figure 6 As shown, the traffic light control system 600 for vehicle grouping includes:

[0139] The passage distance determination module 601 is used to determine the maximum passage distance of the vehicle before the end of the target passage cycle based on the vehicle's motion parameters, wherein the target passage cycle is the closest green light cycle at present;

[0140] The cluster division module 602 is used to divide each vehicle into clusters based on the maximum travel distance of each vehicle in the same lane and its own distance to the stop line ahead, and to obtain the cluster division result. In the cluster division result, one cluster corresponds to the cluster of vehicles that passed the intersection ahead before the end of a travel cycle.

[0141] The speed guidance curve construction module 603 is used to construct the speed guidance curve function of the lead vehicle based on the planned average speed of the lead vehicle in the cluster. The lead vehicle is the first connected vehicle in the cluster.

[0142] The optimization and solution module 604 is used to optimize and solve the speed guidance curve function of the navigator vehicle based on the pre-established cluster energy consumption minimization optimization function, so as to obtain the target speed guidance curve of the navigator vehicle.

[0143] The first control module 605 is used to control the lead vehicle in the cluster to travel along a curve at its own target speed.

[0144] The following state determination module 606 is used to determine the following state of each connected vehicle and each traditional vehicle in the cluster based on the driving state of the lead vehicle in the cluster, the pre-established connected vehicle power model and the traditional vehicle power model.

[0145] The second control module 607 is used to control each vehicle in the cluster to follow the traffic based on its own following status, so that all vehicles in the cluster can pass through the intersection ahead before the end of the corresponding traffic cycle.

[0146] Optionally, the cluster partitioning module 602 includes:

[0147] The first cluster division module is used to divide all vehicles in the same lane whose maximum travel distance is greater than their own distance to the stop line ahead into the first cluster. The first cluster is the cluster of vehicles that pass through the intersection ahead before the end of the target travel cycle.

[0148] The second cluster division module is used to divide all vehicles in the same lane whose maximum travel distance is less than or equal to their own distance to the stop line ahead into a second cluster. The second cluster is the cluster of vehicles that will pass through the intersection ahead before the end of the next travel cycle of the target travel cycle.

[0149] The maximum number of vehicles passing through module is used to determine the maximum number of vehicles passing through in a single passage cycle based on the following status of vehicles in the cluster and through a passage algorithm.

[0150] The access algorithm is as follows:

[0151]

[0152] in, This represents the expected speed of the lead vehicle in the cluster. Indicates rounding down. This represents the expected safe following distance among vehicles in the cluster when a steady state is reached. Indicates the stationary safe distance between the vehicle and the vehicle in front. Indicates the safe time interval between the vehicle and the vehicle in front. Indicates the maximum number of passes. Indicates the start time of the traffic cycle. Indicates the end time of the passage cycle;

[0153] The cluster optimization and adjustment module is used to optimize and adjust the first cluster and the second cluster obtained by division according to the maximum number of passages, so as to obtain the adjusted first cluster and the second cluster.

[0154] Optionally, the traffic light control system 600 for vehicle grouping also includes:

[0155] The real-time detection module is used to monitor in real time whether a vehicle enters the speed guidance area near the signal zone of the current road. The speed guidance area is a range of preset distances from the stop line at the intersection ahead.

[0156] The travel distance determination module 601 is specifically used to substitute the motion parameters of the vehicle entering the speed guidance area into a pre-established travel distance determination algorithm for calculation, so as to obtain the maximum travel distance of the vehicle before the end of the target travel cycle.

[0157] The expression for the algorithm for determining the travel distance is:

[0158]

[0159] in, The time required for a vehicle to accelerate to the speed limit of the road it is on. The speed limit for the road where the vehicle is located. For the target acceleration of the vehicle, Let be the speed of the vehicle at time t. This represents the maximum travel distance that a vehicle can reach before the end of the target travel cycle. The end time of the target traffic cycle. The moment a vehicle enters the speed guidance zone.

[0160] Optionally, the traffic signal control system 600 for vehicle grouping further includes: a planned average speed determination module, used to determine the planned average speed of the lead vehicle in the group; the planned average speed determination module includes:

[0161] The first determination module is used to determine whether there are conventional vehicles in front of the lead vehicle in the same cluster;

[0162] The second determining module is used to determine the planned average speed of the lead vehicle based on the motion parameters of the lead vehicle when there are no conventional vehicles in front of the lead vehicle.

[0163] The wave velocity determination module is used to determine the wave velocity of the shock wave by means of a first wave velocity algorithm and to determine the wave velocity of the dissipation wave by means of a second wave velocity algorithm when there is a conventional vehicle in front of the navigating vehicle.

[0164] The expression for the first wave speed algorithm is: ,in, The wave speed of the shock wave. This is the maximum density of traditional vehicles stopping and queuing before the stop line after the traffic light turns red. Traffic flow on the road at the initial moment when the lead vehicle begins speed guidance. The road density at the initial moment when the lead vehicle begins speed guidance;

[0165] The expression for the second wave speed algorithm is: ,in, To dissipate the wave velocity, The saturation density of the free flow. The saturation flow rate of free flow indicates that after the traffic light turns green, the queuing vehicles in the near-signal zone will be released at the road's saturation flow rate, generating a dissipation wave that propagates backward.

[0166] The queue determination module is used to determine the queue length and queue dissipation time of conventional vehicles in front of the lead vehicle based on the wave speed of the shock wave and the wave speed of the dissipation wave, respectively, using a queue length algorithm and a dissipation time algorithm.

[0167] The queue length algorithm is expressed as:

[0168]

[0169] The dissipation time algorithm is expressed as:

[0170]

[0171] in, This indicates the starting moment when the lead vehicle begins speed guidance. express Average vehicle speed at any given time This indicates the distance from the current position of the lead vehicle to the stop line ahead. This indicates the length of the queue of vehicles ahead. Indicates the current time The time difference between the light turning on and the next green light signal Indicates the queue dissipation time;

[0172] The third determining module is used to determine the planned average speed of the lead vehicle based on the queue length, the queue dissipation time, and the motion parameters of the lead vehicle.

[0173] Optionally, the second determining module includes:

[0174] The fourth determining module is used to input the motion parameters of the first lead vehicle into the first algorithm for calculation to determine the range of the planned average speed of the first lead vehicle, wherein the first lead vehicle is the lead vehicle in the first cluster.

[0175] The first algorithm is:

[0176] in, This indicates the planned average speed of the lead vehicle. This indicates the distance from the current position of the lead vehicle to the stop line ahead. This marks the starting moment when the lead vehicle begins speed guidance. Indicates the start time of the target passage cycle. Indicates the end time of the target passage cycle. This indicates the speed limit for the road where the corresponding vehicle is located;

[0177] The fifth determining module is used to input the motion parameters of the second lead vehicle into the second algorithm for calculation, and to determine the range of the planned average speed of the second lead vehicle, wherein the second lead vehicle is the lead vehicle in the second cluster;

[0178] The second algorithm is:

[0179]

[0180] in, This indicates the planned average speed of the second lead vehicle. This indicates the distance from the current position of the second lead vehicle to the stop line ahead. This marks the starting moment when the second lead vehicle begins speed guidance. This indicates the start time of the next traffic cycle following the target traffic cycle. This indicates the end time of the next traffic cycle following the target traffic cycle. The duration of the traffic light cycle. This indicates the speed limit for the road where the corresponding vehicle is located;

[0181] The sixth determining module is used to determine the maximum value in the range of the planned average speed of the navigator as the planned average speed of the navigator.

[0182] Optional, the third determining module includes:

[0183] The seventh determination module is used to input the determined queue length, queue dissipation time, and motion parameters of the first lead vehicle into the third algorithm for calculation to determine the range of planned average speed values ​​for the first lead vehicle.

[0184] The third algorithm is as follows:

[0185]

[0186] in, This indicates the planned average speed of the lead vehicle. and These represent the queue length and queue dissipation time of conventional vehicles in front of the first lead vehicle in the first cluster, respectively.

[0187] The eighth determination module is used to input the determined queue length, queue dissipation time, and motion parameters of the second lead vehicle into the fourth algorithm for calculation to determine the range of planned average speed values ​​for the second lead vehicle.

[0188] The fourth algorithm is as follows:

[0189]

[0190] in, This indicates the planned average speed of the second lead vehicle. and These represent the queue length and queue dissipation time of the conventional vehicles in front of the second lead vehicle in the second cluster, respectively.

[0191] The ninth determining module is used to determine the maximum value in the range of the planned average speed of the navigator as the planned average speed of the navigator.

[0192] Optionally, the speed guidance curve function of the pilot vehicle constructed in the speed guidance curve construction module 603 is:

[0193]

[0194] in, Let t represent the guiding speed of the lead vehicle at time point t, where t indicates the corresponding time point in the speed guidance process. and For the unknown parameters of the solution to the requirement, The planned average speed of the lead vehicle within the speed guidance zone. The current speed of the lead car With the planned average speed deviation, that is .

[0195] Optionally, the pre-established cluster energy consumption minimization optimization function in the optimization solution module 604 is:

[0196]

[0197] Constraints:

[0198] in, The starting moment for the lead vehicle to begin speed guidance (that is, the moment it enters the speed guidance area). The total number of vehicles in the cluster to which the lead vehicle belongs; A model for evaluating the instantaneous fuel consumption of the i-th vehicle in the cluster; The length of the queue of traditional vehicles in front of the lead vehicle in the cluster; The speed of the lead vehicle in the cluster at time t; Indicates the current time The time difference between the light turning on and the next green light signal; This indicates the distance from the current position of the lead vehicle to the stop line ahead; Let represent the speed of the nth vehicle at time t; , , , , , , These are constant coefficients; and These represent the position difference between the nth car and the (n-1th car) in the cluster; Indicates the safe time interval between the vehicle and the vehicle in front; and These are the maximum and minimum speeds of the nth vehicle in the cluster, respectively. Let be the acceleration of the nth vehicle in the cluster at time t; and Let x and y be the target acceleration and target deceleration of the nth vehicle in the cluster, respectively.

[0199] Optionally, the connected vehicle dynamics model pre-established in the following state determination module 606 is:

[0200]

[0201] in, Let be the acceleration of the nth vehicle in the cluster at time t. This represents the number of vehicles in the cluster that are ahead of the nth vehicle. This indicates the sensitivity of the nth vehicle to the speed difference. This represents the sensitivity of the nth vehicle to the distance difference. and Let be the speeds of the nth and ith vehicles in the cluster at time t, respectively. and These are the position information of the nth and ith vehicles in the cluster at time t, respectively. This represents the expected distance between the nth car and the ith car, where i is a value less than n. Indicates the stationary safe distance between the vehicle and the vehicle in front. Indicates the safe time interval between the vehicle and the vehicle in front;

[0202] The pre-established traditional vehicle dynamics model is as follows:

[0203]

[0204] in, This represents the position difference between the nth car in the cluster and the (n-1)th car ahead. This represents the speed difference between the nth car in the cluster and the (n-1)th car ahead at time t; This represents the target acceleration of the nth vehicle in the cluster. This represents the target deceleration of the nth vehicle in the cluster. Indicates the vehicle's desired speed. The acceleration index, which represents the desired speed of a vehicle. Indicates the stationary safe distance between the vehicle and the vehicle in front. Indicates the safe time interval between the vehicle and the vehicle in front. This represents the expected distance between the nth vehicle and the vehicle in front of it.

[0205] Optionally, the optimization solution module 604 includes:

[0206] The particle generation module is used to randomly generate a preset number of particles based on the constraints and unknown parameters in the velocity guidance curve function, and assign each particle a random position and velocity, wherein the position of the particle is a potential solution of the unknown parameters.

[0207] A fitness determination module is used to determine the fitness of each particle's position through the cluster energy consumption minimization optimization function;

[0208] The optimal position determination module is used to determine the global optimal position and the individual optimal position of each particle based on the fitness of each particle's position. The global optimal position is the optimal position determined by the particle cluster, and the individual optimal position is the optimal position determined by the corresponding particle itself.

[0209] The particle position and velocity update module is used to update the particle's position and velocity based on the particle's current position and velocity, its own individual optimal position, and the global optimal position to obtain the particle's new position and velocity;

[0210] The optimization solution submodule is used to determine the global optimal position as the optimal solution of the unknown parameters in the speed guidance curve function after performing a preset number of iterations, so as to obtain the target speed guidance curve of the navigator vehicle.

[0211] Based on the same inventive concept, one embodiment of this application provides an electronic device, including: a processor, a memory, and a computer program stored in the memory and running on the processor. When the computer program is executed by the processor, it implements the steps of the traffic light control method for vehicle grouping as described in the first aspect of this application.

[0212] Based on the same inventive concept, one embodiment of this application provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the steps of a traffic light control method for vehicle grouping as described in the first aspect of this application.

[0213] As the system implementation is basically similar to the method implementation, it is described in a relatively simple way. For relevant details, please refer to the description of the method implementation.

[0214] It should be noted that, for the sake of simplicity, the method embodiments are all described as a series of actions. However, those skilled in the art should understand that the embodiments of this application are not limited to the described order of actions, because according to the embodiments of this application, some steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also understand that the embodiments described in the specification are all preferred embodiments, and the actions involved are not necessarily necessary for the embodiments of this application.

[0215] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other.

[0216] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, embodiments of this application can take the form of entirely hardware embodiments, entirely software embodiments, or embodiments combining software and hardware aspects. Furthermore, embodiments of this application can take the form of computer program products implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0217] This application describes embodiments with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0218] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0219] These computer program instructions can also be loaded onto a computer or other programmable data processing terminal equipment, causing a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0220] Although preferred embodiments of the present application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the present application.

[0221] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0222] The above provides a detailed description of the traffic signal control method, system, device, and medium for vehicle grouping provided in this application. Specific examples have been used to illustrate the principles and implementation methods of this application. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A traffic light control method for vehicle grouping, characterized in that, The method includes: Based on the vehicle's motion parameters, determine the maximum travel distance of the vehicle before the end of the target traffic cycle, where the target traffic cycle is the closest current green light cycle. Based on the maximum travel distance of each vehicle in the same lane and its distance to the stop line ahead, each vehicle is divided into clusters to obtain cluster division results. In the cluster division results, each cluster corresponds to a cluster of vehicles that passed the intersection ahead before the end of a travel cycle. Based on the planned average speed of the lead vehicle in the cluster, a speed guidance curve function for the lead vehicle is constructed, wherein the lead vehicle is the first connected vehicle in the cluster. Based on the pre-established cluster energy consumption minimization optimization function, the speed guidance curve function of the lead vehicle is optimized and solved to obtain the target speed guidance curve of the lead vehicle. The navigator in the control cluster is guided to travel along a curve at its own target speed. Based on the driving status of the lead vehicle in the cluster, the pre-established connected vehicle dynamics model and traditional vehicle dynamics model, the following status of each connected vehicle and each traditional vehicle in the cluster is determined. The system controls each vehicle in the cluster to follow the traffic based on its own following status, so that all vehicles in the cluster can pass the intersection ahead before the end of the corresponding traffic cycle. Determining the planned average speed of the lead vehicle in the cluster includes: Determine whether there are conventional vehicles ahead of the lead vehicle in the same cluster; When there are no conventional vehicles in front of the lead vehicle, the planned average speed of the lead vehicle is determined based on the motion parameters of the lead vehicle. When there are conventional vehicles in front of the lead vehicle, the wave velocity of the shock wave is determined by the first wave velocity algorithm, and the wave velocity of the dissipation wave is determined by the second wave velocity algorithm. The expression for the first wave speed algorithm is: ,in, The wave speed of the shock wave. This is the maximum density of traditional vehicles stopping and queuing before the stop line after the traffic light turns red. Traffic flow on the road at the initial moment when the lead vehicle begins speed guidance. The road density at the initial moment when the lead vehicle begins speed guidance; The expression for the second wave speed algorithm is: ,in, To dissipate the wave velocity, The saturation density of the free flow. The saturation flow rate of free flow indicates that after the traffic light turns green, the queuing vehicles in the near-signal zone will be released at the road's saturation flow rate, generating dissipation waves that propagate backward. Based on the wave velocity of the shock wave and the wave velocity of the dissipation wave, the queue length and queue dissipation time of the conventional vehicles in front of the lead vehicle are determined by the queue length algorithm and the queue dissipation time algorithm, respectively. The queue length algorithm is expressed as: The dissipation time algorithm is expressed as: in, This indicates the starting moment when the lead vehicle begins speed guidance. express Average vehicle speed at any given time This indicates the distance from the current position of the lead vehicle to the stop line ahead. This indicates the length of the queue of vehicles ahead. Indicates the current time The time difference between the light turning on and the next green light signal Indicates the queue dissipation time; The determined queue length, queue dissipation time, and motion parameters of the first lead vehicle are substituted into the third algorithm for calculation to determine the range of planned average speed values ​​for the first lead vehicle. The third algorithm is as follows: in, This indicates the planned average speed of the lead vehicle. and These represent the queue length and queue dissipation time of conventional vehicles in front of the first lead vehicle in the first cluster, respectively. The determined queue length, queue dissipation time, and motion parameters of the second lead vehicle are substituted into the fourth algorithm for calculation to determine the range of planned average speed values ​​for the second lead vehicle. The fourth algorithm is as follows: in, This indicates the planned average speed of the second lead vehicle. and These represent the queue length and queue dissipation time of the conventional vehicles in front of the second lead vehicle in the second cluster, respectively. The maximum value within the range of the planned average speed of the navigator is determined as the planned average speed of the navigator.

2. The traffic signal control method for vehicle grouping according to claim 1, characterized in that, Based on the maximum travel distance of each vehicle in the same lane and its distance to the stop line ahead, each vehicle is grouped into clusters to obtain clustering results, including: All vehicles in the same lane whose maximum travel distance is greater than their own distance to the stop line ahead are divided into the first cluster. The first cluster is the cluster of vehicles that pass through the intersection ahead before the end of the target travel cycle. All vehicles in the same lane whose maximum travel distance is less than or equal to their distance to the stop line ahead are divided into a second group. The second group is the group of vehicles that will pass through the intersection ahead before the end of the next travel cycle of the target travel cycle. Based on the following status of vehicles in the cluster, the maximum number of vehicles that can pass through in a single passage cycle is determined by the passage algorithm. The access algorithm is as follows: in, This represents the expected speed of the lead vehicle in the cluster. This indicates rounding down. This represents the expected safe following distance among vehicles in the cluster when a steady state is reached. Indicates the stationary safe distance between the vehicle and the vehicle in front. Indicates the safe time interval between the vehicle and the vehicle in front. Indicates the maximum number of passes. Indicates the start time of the traffic cycle. Indicates the end time of the passage cycle; Based on the maximum number of passages, the first cluster and the second cluster obtained by the division are optimized and adjusted to obtain the adjusted first cluster and the second cluster.

3. The traffic signal control method for vehicle grouping according to claim 2, characterized in that, The method further includes: Real-time monitoring of whether vehicles enter the speed guidance zone of the current road's near-signal zone, wherein the speed guidance zone is a range of preset distances from the stop line at the intersection ahead; Determining the maximum travel distance of the vehicle before the end of the target travel cycle based on the vehicle's motion parameters includes: The motion parameters of the vehicle entering the speed guidance area are substituted into a pre-established travel distance determination algorithm to calculate the maximum travel distance of the vehicle before the end of the target travel cycle. The expression for the algorithm for determining the travel distance is: in, The time required for a vehicle to accelerate to the speed limit of the road it is on. The speed limit for the road where the vehicle is located. For the target acceleration of the vehicle, Let be the speed of the vehicle at time t. This represents the maximum travel distance that a vehicle can reach before the end of the target travel cycle. The end time of the target traffic cycle. The moment when a vehicle enters the speed guidance zone.

4. The traffic signal control method for vehicle grouping according to claim 1, characterized in that, When there are no conventional vehicles in front of the lead vehicle, the planned average speed of the lead vehicle is determined based on its motion parameters, including: The motion parameters of the first lead vehicle are substituted into the first algorithm for calculation to determine the range of the planned average speed of the first lead vehicle. The first lead vehicle is the lead vehicle in the first cluster. The first algorithm is: in, This indicates the planned average speed of the lead vehicle. This indicates the distance from the current position of the lead vehicle to the stop line ahead. This marks the starting moment when the lead vehicle begins speed guidance. Indicates the start time of the target passage cycle. Indicates the end time of the target passage cycle. Indicates the speed limit for the road where the corresponding vehicle is located; The motion parameters of the second lead vehicle are substituted into the second algorithm for calculation to determine the range of the planned average speed of the second lead vehicle. The second lead vehicle is the lead vehicle in the second cluster. The second algorithm is: in, This indicates the planned average speed of the second lead vehicle. This indicates the distance from the current position of the second lead vehicle to the stop line ahead. This marks the starting moment when the second lead vehicle begins speed guidance. This indicates the start time of the next traffic cycle following the target traffic cycle. This indicates the end time of the next traffic cycle following the target traffic cycle. The duration of the traffic light cycle. Indicates the speed limit for the road where the corresponding vehicle is located; The maximum value within the range of the planned average speed of the navigator is determined as the planned average speed of the navigator.

5. The traffic signal control method for vehicle grouping according to claim 1, characterized in that, The constructed speed guidance curve function of the pilot vehicle is as follows: in, Let t represent the guiding speed of the lead vehicle at time point t, where t indicates the corresponding time point in the speed guidance process. and For the unknown parameters of the solution to the requirement, The planned average speed of the lead vehicle within the speed guidance zone. The current speed of the lead car With the planned average speed deviation, that is .

6. The traffic signal control method for vehicle grouping according to claim 1, characterized in that, The pre-established cluster energy consumption minimization optimization function is: Constraints: in, The starting moment for the lead vehicle to begin speed guidance; The total number of vehicles in the cluster to which the lead vehicle belongs; A model for evaluating the instantaneous fuel consumption of the i-th vehicle in the cluster; The length of the queue of traditional vehicles in front of the lead vehicle in the cluster; The speed of the lead vehicle in the cluster at time t; Indicates the current time The time difference between the light turning on and the next green light signal; This indicates the distance from the current position of the lead vehicle to the stop line ahead; Let represent the speed of the nth vehicle at time t; , , , , , , These are constant coefficients; and These represent the position difference between the nth car and the (n-1th car) in the cluster; Indicates the safe time interval between the vehicle and the vehicle in front; and These are the maximum and minimum speeds of the nth vehicle in the cluster, respectively. Let be the acceleration of the nth vehicle in the cluster at time t; and Let x and y be the target acceleration and target deceleration of the nth vehicle in the cluster, respectively.

7. The traffic signal control method for vehicle grouping according to claim 1, characterized in that, The pre-established connected vehicle dynamics model is as follows: in, Let be the acceleration of the nth vehicle in the cluster at time t. This represents the number of vehicles in the cluster that are ahead of the nth vehicle. This indicates the sensitivity of the nth vehicle to the speed difference. This represents the sensitivity of the nth vehicle to the distance difference. and Let be the speeds of the nth and ith vehicles in the cluster at time t, respectively. and These are the position information of the nth and ith vehicles in the cluster at time t, respectively. This represents the expected distance between the nth car and the ith car, where i is a value less than n. Indicates the stationary safe distance between the vehicle and the vehicle in front. Indicates the safe time interval between the vehicle and the vehicle in front; The pre-established traditional vehicle dynamics model is as follows: in, This represents the position difference between the nth car in the cluster and the (n-1)th car ahead of it. This represents the speed difference between the nth car in the cluster and the (n-1)th car ahead at time t; This represents the target acceleration of the nth vehicle in the cluster. This represents the target deceleration of the nth vehicle in the cluster. Indicates the vehicle's desired speed. The acceleration index represents the desired speed of a vehicle. Indicates the stationary safe distance between the vehicle and the vehicle in front. Indicates the safe time interval between the vehicle and the vehicle in front. This represents the expected distance between the nth vehicle and the vehicle in front of it.

8. A traffic light control method for vehicle grouping according to claim 6, characterized in that, Based on a pre-established cluster energy consumption minimization optimization function, the speed guidance curve function of the lead vehicle is optimized and solved to obtain the target speed guidance curve of the lead vehicle, including: Based on the constraints, a preset number of particles are randomly generated for the unknown parameters in the velocity guidance curve function, and each particle is assigned a random position and velocity, wherein the position of the particle is a potential solution of the unknown parameters. The fitness of each particle's position is determined by the cluster energy consumption minimization optimization function; Based on the fitness of each particle's position, the global optimal position and the individual optimal position of each particle are determined. The global optimal position is the optimal position determined by the particle cluster, and the individual optimal position is the optimal position determined by the corresponding particle itself. Based on the particle's current position and velocity, its own optimal position, and its global optimal position, the particle's position and velocity are updated to obtain the particle's new position and velocity; After a preset number of iterations, the global optimal position is determined as the optimal solution of the unknown parameters in the speed guidance curve function, so as to obtain the target speed guidance curve of the navigator vehicle.

9. A traffic light control system for vehicle grouping, characterized in that, The system includes: The passage distance determination module is used to determine the maximum passage distance of the vehicle before the end of the target passage cycle based on the vehicle's motion parameters, wherein the target passage cycle is the closest green light cycle at present; The cluster division module is used to divide each vehicle into clusters based on the maximum travel distance of each vehicle in the same lane and its distance to the stop line ahead, and obtain the cluster division result. In the cluster division result, one cluster corresponds to the cluster of vehicles that passed the intersection ahead before the end of a travel cycle. The speed guidance curve construction module is used to construct the speed guidance curve function of the lead vehicle based on the planned average speed of the lead vehicle in the cluster. The lead vehicle is the first connected vehicle in the cluster. The optimization and solution module is used to optimize and solve the speed guidance curve function of the navigator vehicle based on the pre-established cluster energy consumption minimization optimization function, so as to obtain the target speed guidance curve of the navigator vehicle. The first control module is used to control the lead vehicle in the cluster to guide the car along the curve at its own target speed. The following state determination module is used to determine the following state of each connected vehicle and each traditional vehicle in the cluster based on the driving state of the lead vehicle in the cluster, the pre-established connected vehicle power model and the traditional vehicle power model. The second control module is used to control each vehicle in the cluster to follow the traffic based on its own following status, so that all vehicles in the cluster can pass the intersection ahead before the end of the corresponding traffic cycle. The module for determining the average speed includes: The first determination module is used to determine whether there are conventional vehicles in front of the lead vehicle in the same cluster; The second determining module is used to determine the planned average speed of the lead vehicle based on the motion parameters of the lead vehicle when there are no conventional vehicles in front of the lead vehicle. The wave velocity determination module is used to determine the wave velocity of the shock wave by means of a first wave velocity algorithm and to determine the wave velocity of the dissipation wave by means of a second wave velocity algorithm when there is a conventional vehicle in front of the navigating vehicle. The expression for the first wave speed algorithm is: ,in, The wave speed of the shock wave. This is to determine the maximum density of traditional vehicles stopping and queuing before the stop line after the traffic light turns red. Traffic flow on the road at the initial moment when the lead vehicle begins speed guidance. The road density at the initial moment when the lead vehicle begins speed guidance; The expression for the second wave speed algorithm is: ,in, To dissipate the wave velocity, The saturation density of the free flow. The saturation flow rate of free flow indicates that after the traffic light turns green, the queuing vehicles in the near-signal zone will be released at the road's saturation flow rate, generating dissipation waves that propagate backward. The queue determination module is used to determine the queue length and queue dissipation time of conventional vehicles in front of the lead vehicle based on the wave speed of the shock wave and the wave speed of the dissipation wave, respectively, using a queue length algorithm and a dissipation time algorithm. The queue length algorithm is expressed as: The dissipation time algorithm is expressed as: in, This indicates the starting moment when the lead vehicle begins speed guidance. express Average vehicle speed at any given time This indicates the distance from the current position of the lead vehicle to the stop line ahead. This indicates the length of the queue of vehicles ahead. Indicates the current time The time difference between the light turning on and the next green light signal Indicates the queue dissipation time; The seventh determination module is used to input the determined queue length, queue dissipation time, and motion parameters of the first lead vehicle into the third algorithm for calculation to determine the range of planned average speed values ​​for the first lead vehicle. The third algorithm is as follows: in, This indicates the planned average speed of the lead vehicle. and These represent the queue length and queue dissipation time of conventional vehicles in front of the first lead vehicle in the first cluster, respectively. The eighth determination module is used to input the determined queue length, queue dissipation time, and motion parameters of the second lead vehicle into the fourth algorithm for calculation to determine the range of planned average speed values ​​for the second lead vehicle. The fourth algorithm is as follows: in, This indicates the planned average speed of the second lead vehicle. and These represent the queue length and queue dissipation time of the conventional vehicles in front of the second lead vehicle in the second cluster, respectively. The ninth determining module is used to determine the maximum value in the range of the planned average speed of the navigator as the planned average speed of the navigator.

10. An electronic device, characterized in that, include: A processor, a memory, and a computer program stored in the memory and running on the processor, wherein the computer program, when executed by the processor, implements the steps of a traffic light control method for vehicle grouping as described in any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the steps of a traffic light control method for vehicle grouping as described in any one of claims 1 to 8.

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