Speed guidance and regional energy efficiency optimization method, device and equipment based on vehicle-road cloud cooperation and storage medium

Abstract

The application discloses a speed guidance and regional energy efficiency optimization method and device based on vehicle-road cloud cooperation, equipment and a storage medium, relates to the field of traffic control technology, and the speed guidance and regional energy efficiency optimization method based on vehicle-road cloud cooperation comprises the following steps: receiving vehicle running state data, signal lamp state data, lane-level traffic flow data and historical traffic data, and generating a single vehicle preliminary speed planning result; acquiring regional traffic state data, and generating a regional signal timing strategy according to the regional traffic state data; coordinating and optimizing the single vehicle preliminary speed planning result according to the regional traffic state data, and generating a regional cooperative speed guidance strategy; issuing the regional signal timing strategy to a signal machine, and issuing the regional cooperative speed guidance strategy to a vehicle terminal. The application can improve the traffic efficiency of vehicles passing through a signal lamp road section.

Description

Technical Field

[0001] This application relates to the field of traffic control technology, and in particular to a method, apparatus, equipment, and storage medium for speed guidance and regional energy efficiency optimization based on vehicle-road-cloud collaboration. Background Technology

[0002] Current urban traffic signal control systems mostly employ fixed timing or inductive control strategies. Vehicles struggle to anticipate traffic light phase changes during transit, leading to frequent deceleration and acceleration cycles. While vehicle-to-infrastructure (V2I) technology enables real-time broadcasting of traffic light status information, existing systems largely remain at the information prompt level, lacking predictive speed guidance mechanisms for individual vehicle passage and regional coordination. This makes it difficult to ensure vehicles can pass through multiple signalized intersections consecutively while maintaining traffic safety. Therefore, improving the efficiency of vehicle passage through signalized intersections remains a problem that needs to be addressed.

[0003] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0004] The main purpose of this application is to provide a method, device, equipment, and storage medium for speed guidance and regional energy efficiency optimization based on vehicle-road-cloud collaboration, aiming to solve the technical problem of how to improve the traffic efficiency of vehicles passing through traffic light sections.

[0005] To achieve the above objectives, this application proposes a speed guidance and regional energy efficiency optimization method based on vehicle-road-cloud cooperation, the method comprising:

[0006] It receives vehicle operation status data uploaded by the vehicle terminal, obtains traffic light status data and lane-level traffic flow data collected by the roadside unit, and obtains historical traffic data stored on the cloud platform. A preliminary speed planning result for a single vehicle is generated based on the vehicle operation status data, the traffic light status data, the lane-level traffic flow data, and the historical traffic data. The traffic light status data, lane-level traffic flow data, and vehicle operation status data from multiple intersections within the area are aggregated and processed to obtain regional traffic status data, and a regional signal timing strategy is generated based on the regional traffic status data. Based on the regional traffic status data, the preliminary speed planning results of the single vehicle are coordinated and optimized to generate a regional collaborative speed guidance strategy. The regional signal timing strategy is sent to the traffic signal controller, and the regional coordinated speed guidance strategy is sent to the vehicle terminal.

[0007] In one embodiment, the step of generating a preliminary speed planning result for a single vehicle based on the vehicle operating status data, the traffic light status data, the lane-level traffic flow data, and the historical traffic data includes: Based on the historical traffic data, the traffic light status data, and the lane-level traffic flow data, the traffic light status and queue dissipation time for at least one future signal cycle are predicted to obtain the predicted signal status and the predicted queue dissipation time. The vehicle's current position and speed are determined based on the vehicle's operating status data; A vehicle speed optimization model is constructed based on the vehicle's current position, vehicle speed, predicted signal state, and predicted queue dissipation time. The vehicle speed optimization model is then solved to obtain preliminary speed planning results for a single vehicle that satisfy vehicle dynamics constraints, safety constraints, and comfort constraints.

[0008] In one embodiment, the step of constructing a vehicle speed optimization model based on the vehicle's current position, the vehicle speed, the predicted signal state, and the predicted queue dissipation time includes: The effective passage time window is determined based on the vehicle's current location, the vehicle's speed, the predicted signal status, and the predicted queue dissipation time. Using the vehicle's acceleration at each discrete moment in the future as the decision variable to be solved, and the vehicle's position and speed as the state variables, an objective function is constructed that includes an acceleration penalty term, a speed deviation penalty term, and a time window matching penalty term. Based on the effective passage time window, set the constraint conditions for speed limit constraint, acceleration limit constraint, and stop line crossing time constraint; A vehicle speed optimization model is constructed based on the decision variables, the objective function, and the constraints.

[0009] In one embodiment, the step of generating a regional signal timing strategy based on the regional traffic state data includes: Based on the regional traffic state data, a regional signal timing optimization model is constructed using the duration, phase sequence, and cycle length of the signal phases at each intersection within the region as optimization variables. Under the conditions of satisfying the constraints of minimum and maximum green light duration of signal phase, phase conflict and safe passage constraints, and timing coordination constraints of adjacent intersections, the regional signal timing optimization model is solved to obtain the regional signal timing strategy.

[0010] In one embodiment, the step of coordinating and optimizing the preliminary speed planning results of the single vehicle based on the regional traffic state data to generate a regional coordinated speed guidance strategy includes: Based on the regional traffic status data, the preliminary speed planning results of multiple vehicles are analyzed collaboratively to determine the coordinated passage time window for each vehicle through the intersection. Based on the coordinated passage time window, the speed trajectory in the preliminary speed planning result of the single vehicle is adjusted to generate a regional coordinated speed guidance strategy.

[0011] In one embodiment, after the step of coordinating and optimizing the preliminary speed planning results of the single vehicle based on the regional traffic state data to generate a regional coordinated speed guidance strategy, the method further includes: The regional signal timing strategy and the regional coordinated speed guidance strategy are input into a preset traffic simulation model to simulate the operation effect and obtain regional traffic operation evaluation indicators. The regional signal timing strategy and the regional coordinated speed guidance strategy are iteratively optimized based on the regional traffic operation evaluation indicators until the preset optimization target is met, resulting in the optimized regional signal timing strategy and the optimized regional coordinated speed guidance strategy.

[0012] In one embodiment, the step of receiving vehicle operating status data uploaded by the vehicle-mounted terminal includes: Receive raw location information and vehicle driving status information uploaded by the vehicle terminal; The original positioning information is processed by a multi-source positioning information fusion algorithm to perform lane-level positioning enhancement processing, thereby obtaining lane-level vehicle position data. The lane-level vehicle position data and the vehicle driving status information are used together as vehicle operating status data.

[0013] Furthermore, to achieve the above objectives, this application also proposes a speed guidance and regional energy efficiency optimization device based on vehicle-road-cloud cooperation, the device comprising: The receiving module is used to receive vehicle operation status data uploaded by the vehicle terminal, obtain traffic light status data and lane-level traffic flow data collected by the roadside unit, and obtain historical traffic data stored on the cloud platform. The generation module is used to generate preliminary speed planning results for a single vehicle based on the vehicle operating status data, the traffic light status data, the lane-level traffic flow data, and the historical traffic data. The aggregation module is used to aggregate the traffic light status data, lane-level traffic flow data and vehicle operation status data of multiple intersections in the area to obtain regional traffic status data, and generate a regional signal timing strategy based on the regional traffic status data. The optimization module is used to coordinate and optimize the preliminary speed planning results of the single vehicle based on the regional traffic status data, and generate a regional coordinated speed guidance strategy. The distribution module is used to distribute the regional signal timing strategy to the traffic signal controller and the regional coordinated speed guidance strategy to the vehicle terminal.

[0014] Furthermore, to achieve the above objectives, this application also proposes a speed guidance and regional energy efficiency optimization device based on vehicle-road-cloud cooperation. The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. The computer program is configured to implement the steps of the speed guidance and regional energy efficiency optimization method based on vehicle-road-cloud cooperation as described above.

[0015] In addition, to achieve the above objectives, this application also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, it implements the steps of the speed guidance and regional energy efficiency optimization method based on vehicle-road-cloud cooperation as described above.

[0016] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the speed guidance and regional energy efficiency optimization method based on vehicle-road-cloud cooperation as described above.

[0017] This application provides a speed guidance and regional energy efficiency optimization method based on vehicle-road-cloud collaboration. The method receives vehicle operating status data uploaded by an onboard terminal, acquires traffic light status data and lane-level traffic flow data collected by a roadside unit, and obtains historical traffic data stored on a cloud platform. Based on the vehicle operating status data, traffic light status data, lane-level traffic flow data, and historical traffic data, a preliminary speed planning result for a single vehicle is generated. The traffic light status data, lane-level traffic flow data, and vehicle operating status data from multiple intersections within the region are aggregated to obtain regional traffic status data, and a regional signal timing strategy is generated based on this data. The preliminary speed planning result for a single vehicle is then coordinated and optimized based on the regional traffic status data to generate a regional coordinated speed guidance strategy. The regional signal timing strategy is then distributed to the traffic signal controller and the vehicle terminal. This application, through the coordinated control of vehicle speed planning and regional traffic lights, reduces vehicle waiting times at intersections and improves the traffic efficiency of vehicles passing through traffic light-controlled road sections. Attached Figure Description

[0018] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0019] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a flowchart illustrating an embodiment of the speed guidance and regional energy efficiency optimization method based on vehicle-road-cloud collaboration in this application. Figure 2 This is a flowchart illustrating Embodiment 2 of the method for speed guidance and regional energy efficiency optimization based on vehicle-road-cloud collaboration in this application. Figure 3 A simplified flowchart illustrating the speed guidance and regional energy efficiency optimization method based on vehicle-road-cloud collaboration provided in Embodiment 1 of this application; Figure 4 This is a schematic diagram of the module structure of the speed guidance and regional energy efficiency optimization device based on vehicle-road-cloud collaboration in an embodiment of this application; Figure 5 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the speed guidance and regional energy efficiency optimization method based on vehicle-road-cloud collaboration in the embodiments of this application.

[0021] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0022] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0023] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0024] This application receives vehicle operation status data uploaded by an on-board terminal, acquires traffic light status data and lane-level traffic flow data collected by a roadside unit, and obtains historical traffic data stored on a cloud platform; generates a preliminary speed planning result for a single vehicle based on the vehicle operation status data, the traffic light status data, the lane-level traffic flow data, and the historical traffic data; aggregates and processes the traffic light status data, the lane-level traffic flow data, and the vehicle operation status data from multiple intersections within the area to obtain regional traffic status data, and generates a regional signal timing strategy based on the regional traffic status data; coordinates and optimizes the preliminary speed planning result for a single vehicle based on the regional traffic status data to generate a regional coordinated speed guidance strategy; and distributes the regional signal timing strategy to the traffic signal controller and the regional coordinated speed guidance strategy to the vehicle terminal.

[0025] Current urban traffic signal control systems mostly employ fixed timing or inductive control strategies. Vehicles struggle to anticipate traffic light phase changes during transit, leading to frequent deceleration and acceleration cycles. While vehicle-to-infrastructure (V2I) technology enables real-time broadcasting of traffic light status information, existing systems largely remain at the information prompt level, lacking predictive speed guidance mechanisms for individual vehicle passage and regional coordination. This makes it difficult to ensure vehicles can pass through multiple signalized intersections consecutively while maintaining traffic safety. Therefore, improving the efficiency of vehicle passage through signalized intersections remains a problem that needs to be addressed.

[0026] This application reduces vehicle waiting time at intersections and improves traffic efficiency by coordinating vehicle speed planning with regional traffic light control.

[0027] Based on this, embodiments of this application provide a speed guidance and regional energy efficiency optimization method based on vehicle-road-cloud cooperation, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the speed guidance and regional energy efficiency optimization method based on vehicle-road-cloud collaboration in this application.

[0028] In this embodiment, the speed guidance and regional energy efficiency optimization method based on vehicle-road-cloud cooperation includes steps S10 to S50: Step S10: Receive vehicle operation status data uploaded by the vehicle terminal, obtain traffic light status data and lane-level traffic flow data collected by the roadside unit, and obtain historical traffic data stored on the cloud platform; It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions, such as a speed guidance and regional energy efficiency optimization device based on vehicle-road-cloud collaboration. The following description uses a speed guidance and regional energy efficiency optimization device based on vehicle-road-cloud collaboration as an example to illustrate this embodiment and the subsequent embodiments.

[0029] It should be noted that before speed planning, vehicle-road information connectivity and basic data construction are required. This includes vehicle-side access and vehicle status collection: vehicles access the vehicle-road cooperative network through pre-installed vehicle terminals or aftermarket mobile terminals, periodically uploading vehicle operation status data, including vehicle location, speed, acceleration, and driving status information. Roadside unit deployment and signal data acquisition: Roadside units (RSUs) are deployed at target intersections or along roads. Each RSU includes a communication module, an edge computing module, and a multi-source sensing module. The sensing module includes at least one of radar or camera, used to acquire lane-level traffic flow information. The RSU connects to traffic signal controllers via wired or wireless means, acquiring real-time signal phase and timing information, and uniformly converting it into standardized signal status messages. Cloud platform construction: A traffic data processing platform is established in the cloud to complete data access, cleaning, storage, and service scheduling. The cloud platform adopts a microservice architecture, decoupling and deploying device management, real-time communication, historical query, and algorithm service modules.

[0030] In one feasible approach, the step of receiving vehicle operating status data uploaded by the vehicle terminal includes: receiving raw positioning information and vehicle driving status information uploaded by the vehicle terminal; performing lane-level positioning enhancement processing on the raw positioning information using a multi-source positioning information fusion algorithm to obtain lane-level vehicle location data; and using the lane-level vehicle location data and the vehicle driving status information together as vehicle operating status data.

[0031] It should be noted that after receiving the raw positioning information (such as latitude and longitude coordinates, positioning accuracy, number of satellites, etc.) and vehicle driving status information (such as heading angle and instantaneous speed obtained through the phone's gyroscope and accelerometer) uploaded by the vehicle terminal via the mobile network, a multi-source positioning information fusion algorithm is invoked to fuse the vehicle's raw positioning information with high-precision map data collected by roadside units, lane-level traffic flow data, and road geometry information stored in the cloud. For example, a map matching algorithm corrects drifting GPS points to the correct road, and combined with the vehicle's heading angle and historical trajectory, it determines which lane the vehicle is currently traveling in (such as the leftmost straight lane). The enhanced lane-level vehicle position data is then integrated with the raw received vehicle driving status information to form high-precision vehicle operating status data for use in subsequent steps.

[0032] Step S20: Generate preliminary speed planning results for a single vehicle based on the vehicle operating status data, the traffic light status data, the lane-level traffic flow data, and the historical traffic data; It should be noted that, based on the vehicle operation status data, the traffic light status data, the lane-level traffic flow data, and the historical traffic data, a short-term traffic flow prediction model can be constructed first to predict future queue dissipation time and traffic light status. Then, a vehicle speed optimization model can be constructed to generate preliminary speed planning results for a single vehicle.

[0033] Step S30: Aggregate the traffic light status data, lane-level traffic flow data, and vehicle operation status data of multiple intersections within the area to obtain regional traffic status data, and generate a regional signal timing strategy based on the regional traffic status data. It should be noted that after speed planning for a single vehicle, it is also necessary to obtain regional traffic status data for the entire area to optimize regional energy efficiency. Specifically, this can generate regional signal timing strategies and optimize the initial speed planning results for a single vehicle.

[0034] It should be noted that the aggregation processing can take multiple intersections and road segments within the region as objects. Based on real-time traffic flow data, queue length, average delay, saturation, vehicle arrival rate and other parameters, the regional traffic operation status is mathematically expressed, and the regional traffic status data is finally output, such as the saturation of each intersection, average queue length, flow ratio, green light utilization rate and other status data.

[0035] In one feasible approach, the step of generating a regional signal timing strategy based on the regional traffic state data includes: constructing a regional-level signal timing optimization model based on the regional traffic state data, using the duration, phase sequence, and cycle length of signal phases at each intersection within the region as optimization variables; and solving the regional-level signal timing optimization model to obtain the regional signal timing strategy under the conditions of satisfying constraints on minimum and maximum green light duration of signal phases, phase conflict and safe passage constraints, and timing coordination constraints of adjacent intersections.

[0036] It should be noted that the regional signal timing optimization model belongs to the control decision model. Its optimization variables include control parameters such as the signal phase duration, cycle length, phase sequence, and phase offset between adjacent intersections at each intersection. In the optimization process of the regional signal timing optimization model, the objective function and constraints are determined based on regional traffic state data, such as arrival rate, queue length, and saturation flow rate. For example, the optimization variables might be: green light time, cycle, and phase offset at each intersection; the objective function might be: minimizing delay time or number of stops; and the constraints might be: minimum green light duration, conflict phase constraints, and cycle consistency constraints. An optimization algorithm such as a genetic algorithm can be used to search for the optimal solution, ultimately obtaining the regional signal timing strategy.

[0037] Step S40: Based on the regional traffic status data, coordinate and optimize the preliminary speed planning results of the single vehicle to generate a regional coordinated speed guidance strategy; It should be noted that if all vehicles follow their own initial speed planning results, it may lead to new congestion (e.g., multiple vehicles simultaneously rushing towards downstream intersections). Therefore, coordinated optimization is still necessary. Fine-tuning of vehicle speed recommendations is required to achieve smoother and more balanced traffic flow.

[0038] In one feasible approach, the step of coordinating and optimizing the preliminary speed planning results of a single vehicle based on the regional traffic state data to generate a regional coordinated speed guidance strategy includes: performing collaborative analysis on the preliminary speed planning results of multiple vehicles based on the regional traffic state data to determine the coordinated passage time window for each vehicle through the intersection; adjusting the speed trajectory in the preliminary speed planning results of the single vehicle based on the coordinated passage time window to generate a regional coordinated speed guidance strategy.

[0039] It's important to note that preliminary speed planning results for individual vehicles can be collaboratively optimized based on regional traffic condition data. For example, if it's predicted that traffic flow at a downstream intersection will approach saturation at some point in the future, and the platform aggregates the preliminary speed planning results of multiple vehicles approaching the intersection, collaborative analysis can reveal that if all vehicles travel at their initially planned speeds, they will arrive at the downstream intersection almost simultaneously, inevitably leading to queuing and stopping. Therefore, based on regional traffic condition data, the coordinated passage time windows for these vehicles can be reallocated or fine-tuned. For instance, it might be suggested that vehicle 1 slightly increase its speed to pass within the current cycle's green light window; vehicle 2 maintain its original speed; and vehicle 3 slightly decrease its speed to pass within the next cycle's green light window. Based on the newly allocated coordinated passage time windows for each vehicle, the speed trajectories in the preliminary speed planning results are smoothly adjusted, thereby generating a regional collaborative speed guidance strategy.

[0040] In one feasible approach, the regional signal timing strategy and the regional coordinated speed guidance strategy are input into a preset traffic simulation model to simulate the operational effects and obtain regional traffic operation evaluation indicators. Based on the regional traffic operation evaluation indicators, the regional signal timing strategy and the regional coordinated speed guidance strategy are iteratively optimized until a preset optimization target is met, resulting in optimized regional signal timing strategy and optimized regional coordinated speed guidance strategy.

[0041] It should be noted that after obtaining the regional signal timing strategy and regional coordinated speed guidance strategy, a traffic simulation model can be used for simulation. The traffic simulation model is a microscopic or macroscopic-microscopic combined traffic simulation model. Its core function is to simulate the traffic evolution process, including vehicle arrival, queue formation, signal release, vehicle following, and lane changing behavior, and to output regional operation evaluation indicators, such as average delay time, queue length, number of stops, and regional energy consumption. Model construction can be achieved by independently establishing a discrete-time update model based on microscopic traffic flow theory, and then calibrating and further developing parameters based on the existing traffic simulation framework. Finally, the model parameters are calibrated using measured traffic data. Model parameters include maximum vehicle acceleration, expected travel time, reaction time, and saturation release rate, which can be calibrated using historical data regression or least squares methods to minimize the error between the simulation output and measured traffic indicators. For example, at the entrance lane of the i-th intersection in the region, let the vehicle arrival rate be λ. i (t), release rate is μ i (t), then the queue length Q i The evolution of (t) can be expressed in continuous-time form:

[0042] Among them, g i (t)∈{0,1} is the signal phase indication function (1 for green light, 0 for red light).

[0043] It should be noted that after constructing the traffic simulation model, the regional signal timing strategy and regional coordinated speed guidance strategy generated in the current period are used as input parameters and loaded into the traffic simulation model. The model simulates the operation of all vehicles in the region over a future period (e.g., the next 15 minutes) in a virtual environment and outputs a series of quantitative evaluation indicators, such as average delay time, average queue length, total number of stops, and total regional energy consumption, i.e., regional traffic operation evaluation indicators. After the simulation is completed, it is determined whether the output evaluation indicators meet the preset optimization objectives (e.g., the average regional delay is reduced by more than 5% compared to the previous period). If not, the optimization algorithm (e.g., Bayesian optimization) will automatically correct the regional signal timing strategy (e.g., fine-tuning the green light ratio at a certain intersection) and the regional coordinated speed guidance strategy (e.g., adjusting the weight of traffic flow allocation) based on the simulation results, and then input the corrected new strategies back into the traffic simulation model for simulation evaluation. This process continues until the preset optimization objectives are met or the maximum number of iterations is reached, finally outputting the optimized regional signal timing strategy and the optimized regional coordinated speed guidance strategy.

[0044] Step S50: Send the regional signal timing strategy to the traffic signal controller and send the regional coordinated speed guidance strategy to the vehicle terminal.

[0045] It should be noted that the generated regional signal timing strategy is finally distributed to the traffic signal controllers at each intersection via a wired network, and the signal controllers execute the new timing scheme. At the same time, the generated regional coordinated speed guidance strategy is distributed to the terminal devices of the corresponding vehicles via a wireless network, prompting drivers to drive at the recommended speed in a visual or voice manner.

[0046] This embodiment receives vehicle operation status data uploaded by the vehicle-mounted terminal, acquires traffic light status data and lane-level traffic flow data collected by the roadside unit, and obtains historical traffic data stored on the cloud platform. Based on the vehicle operation status data, traffic light status data, lane-level traffic flow data, and historical traffic data, it generates a preliminary speed planning result for a single vehicle. It then aggregates the traffic light status data, lane-level traffic flow data, and vehicle operation status data from multiple intersections within the area to obtain regional traffic status data, and generates a regional signal timing strategy based on this data. The preliminary speed planning result for a single vehicle is then coordinated and optimized based on the regional traffic status data to generate a regional coordinated speed guidance strategy. Finally, the regional signal timing strategy is sent to the traffic signal controller, and the regional coordinated speed guidance strategy is sent to the vehicle terminal. This embodiment, through the coordinated control of vehicle speed planning and regional traffic lights, reduces vehicle waiting times at intersections and improves the traffic efficiency of vehicles passing through traffic light-controlled road sections.

[0047] Based on the first embodiment of this application, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description, and will not be repeated hereafter. Based on this, please refer to... Figure 2 Step S20 also includes steps S201 to S203: Step S201: Based on the historical traffic data, the traffic light status data, and the lane-level traffic flow data, predict the traffic light status and queue dissipation time for at least one future signal cycle to obtain the predicted signal status and the predicted queue dissipation time. It should be noted that a short-term traffic flow prediction model can be constructed based on historical traffic data, traffic light status data, and lane-level traffic flow data. This model can be used to predict the traffic light status and queue dissipation time for at least one future signal cycle. The prediction model can be implemented using a recurrent neural network or a self-attention model. The queue dissipation time refers to the time required for vehicles currently queuing at the intersection to fully move and pass the stop line after the green light turns on. For example, if the prediction model outputs that the next green light will start in 15 seconds, but since there are currently 10 vehicles in the queue, the predicted queue dissipation time is 8 seconds. In this case, the actual passable time window will open 8 seconds after the green light begins.

[0048] Step S202: Determine the vehicle's current position and speed based on the vehicle's operating status data; It should be noted that the vehicle's current precise location (i.e., the vehicle's current position) and current speed (i.e., the vehicle's speed) can be determined from the vehicle's uploaded vehicle operation status data.

[0049] Step S203: Construct a vehicle speed optimization model based on the vehicle's current position, vehicle speed, predicted signal state, and predicted queue dissipation time, and solve the vehicle speed optimization model to obtain preliminary speed planning results for a single vehicle that satisfy vehicle dynamics constraints, safety constraints, and comfort constraints.

[0050] It should be noted that, based on the vehicle's current position and speed, the traffic light status and queue dissipation time are predicted to construct a vehicle speed optimization model. The optimization objective of this model is to pass through the intersection within the effective green light time window. The optimization model includes state variables: vehicle position and time; a cost function: including stopping penalties, speed deviation penalties, and sudden acceleration / deceleration penalties; and constraints: vehicle dynamics constraints, safe distance constraints, and comfort constraints. The optimal speed trajectory can be solved using dynamic programming or equivalent optimization methods.

[0051] In one feasible approach, an effective passage time window is determined based on the vehicle's current position, vehicle speed, predicted signal state, and predicted queue dissipation time. The vehicle's acceleration at each discrete future moment is used as the decision variable to be solved, and the vehicle's position and speed are used as state variables. An objective function is constructed, including an acceleration penalty term, a speed deviation penalty term, and a time window matching penalty term. Constraints, including speed limit constraints, acceleration limit constraints, and stop line crossing time constraints, are set based on the effective passage time window. A vehicle speed optimization model is constructed based on the decision variables, the objective function, and the constraints.

[0052] It should be noted that, firstly, an effective passage time window is constructed: the signal phase and timing prediction results for at least one future signal cycle are obtained from the predicted signal state, and the predicted queue dissipation time is received. Based on the predicted green light start time t... g，s The green light ends at time t g，e Including queue dissipation time, an effective passage time window for vehicles to cross the stop line is constructed. For example, let the distance from the target vehicle to the stop line be d0, the current time be t0, and the future signal phase / timing be S. PaT The queue dissipation time is T. dis For the j-th candidate green light window, let the predicted start and end times of the green light be denoted as . The valid time window for passage is defined as follows:

[0053] in, It can be estimated from queue length and release capacity. When multiple feasible green light cycles are predicted, multiple candidate time window sets can be generated. Through the above time window construction process, the traffic light status and queue dissipation time output by the preceding prediction module are transformed into time boundary conditions in the subsequent speed trajectory optimization model, forming a parameter mapping from the prediction results to the optimization model.

[0054] Then, vehicle motion state modeling is performed. After the effective passage time window is determined, a discrete-time longitudinal motion model of the vehicle is established with the target vehicle as the optimization object. The state variables include the vehicle's current position s. (k) Speed ​​v (k) and time variable t (k) The control variable is the vehicle's longitudinal acceleration a. (k) Modeling is done with a discrete time step Δt, and the decision time domain length is N. The state and control can be defined as follows:

[0055] Within the discrete period, using a constant acceleration discrete dynamic state model, it can be expressed as:

[0056] The position of the vehicle when it reaches the stop line is recorded as Then the arrival time t arr This can be defined by the "first step out of bounds":

[0057] If more continuous arrival times are needed to represent the first boundary crossing step, interpolation can be used:

[0058] This allows us to predict the time it takes for a vehicle to reach the stop line under different control sequences, and use this as the basis for subsequent time window matching judgments, thus making the optimization calculation process physically feasible.

[0059] Next is the step of constructing the objective function. Based on the vehicle motion model, a comprehensive performance optimization objective function is further constructed, expressed as follows:

[0060] in, The overall optimization objective value; These are the weighting coefficients; This is the square term of acceleration, used to suppress rapid acceleration and deceleration; The desired cruising speed; This indicates a speed deviation penalty. The time window matching penalty function is defined as follows:

[0061] in, and The penalty intensity coefficient, and This represents the boundary of the effective passage time window. A time deviation penalty term encourages vehicles to pass through the middle of the time window to improve robustness; speed and acceleration penalty terms suppress abrupt acceleration and deceleration. If the target vehicle is an electric vehicle, an energy consumption penalty term based on a power model can also be introduced. No penalty is imposed when the vehicle's arrival time falls within the effective green light window; if it arrives early or late, a secondary penalty is applied, thus explicitly reflecting the impact of traffic lights and queue dissipation time on the objective function.

[0062] Next is the constraint setting step. After the objective function is determined, multiple types of constraints are set to ensure the feasibility and safety of the solution. These constraints include: vehicle dynamics constraints, speed upper and lower limit constraints, acceleration and comfort constraints, and stop line crossing time constraints. The speed constraint can be expressed as...

[0063] in, This indicates the maximum speed limit for the road.

[0064] Acceleration constraints are expressed as follows:

[0065] in, and These are the maximum and minimum accelerations, respectively; the time constraint for crossing the stop line is... This constraint is the core constraint, used to force vehicles to pass the stop line within the predicted effective time window. When no solution satisfies the constraint, the system automatically selects the time window of the next signal cycle for optimization calculation. Furthermore, in the presence of a vehicle ahead, a safe distance constraint must be added to ensure a safe following distance. The safe distance constraint is expressed as follows:

[0066] in, Indicates the position of the vehicle in front; Minimum static safety distance; For safety time intervals, the above constraints transform the queuing dissipation time and signal phase prediction results into hard constraints for an optimization problem.

[0067] Finally, the optimal control sequence can be solved based on Model Predictive Control (MPC). After completing the state modeling, objective function construction, and constraint setting, dynamic programming and model predictive control methods are used to solve the control variable sequence. The optimal control sequence for the target vehicle is obtained by solving the above finite-time-domain constrained optimization problem.

[0068] In this case, model predictive control only executes the first control variable: Then, the state is updated and the solution is recalculated at the next sampling time, thus forming a rolling optimization mechanism of "prediction-optimization-execution-re-prediction". In the above MPC solution process, the predicted traffic light state and queue dissipation time are explicitly transformed into time window constraints. It participates in the optimization solution to achieve mathematical closed-loop coupling control between signal prediction results and vehicle speed trajectory optimization.

[0069] This embodiment predicts the traffic light status and queue dissipation time for at least one future signal cycle based on historical traffic data, traffic light status data, and lane-level traffic flow data, obtaining predicted signal status and predicted queue dissipation time. It then determines the vehicle's current position and speed based on vehicle operating status data. A vehicle speed optimization model is constructed based on the vehicle's current position, speed, predicted signal status, and predicted queue dissipation time. Solving this model yields preliminary speed planning results for a single vehicle that satisfy vehicle dynamics, safety, and comfort constraints. This embodiment introduces the prediction of future traffic light status and queue dissipation time into the single-vehicle speed planning stage and combines this with the vehicle's real-time position and speed to construct an optimization model. This allows the vehicle to anticipate the dynamic passage window at the upcoming intersection, thereby generating a smooth speed trajectory while meeting dynamics, safety, and comfort constraints.

[0070] For example, to help understand the implementation process of the speed guidance and regional energy efficiency optimization method based on vehicle-road-cloud cooperation obtained by combining this embodiment with the above embodiment one, please refer to... Figure 3 , Figure 3 A simplified flowchart of a speed guidance and regional energy efficiency optimization method based on vehicle-road-cloud cooperation is provided. Specifically, the method involves: first, vehicle-road-cloud information collection and data construction; second, traffic light status and traffic flow prediction modeling; third, single-vehicle predictive speed trajectory optimization calculation; fourth, generation of regional collaborative optimization strategy; and finally, simulation effect evaluation and iterative correction, and execution of signal timing strategy.

[0071] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the speed guidance and regional energy efficiency optimization method based on vehicle-road-cloud cooperation in this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0072] This application also provides a speed guidance and regional energy efficiency optimization device based on vehicle-road-cloud cooperation. Please refer to [link / reference]. Figure 4 The speed guidance and regional energy efficiency optimization device based on vehicle-road-cloud collaboration includes: The receiving module 10 is used to receive vehicle operation status data uploaded by the vehicle terminal, obtain traffic light status data and lane-level traffic flow data collected by the roadside unit, and obtain historical traffic data stored on the cloud platform. The generation module 20 is used to generate a preliminary speed planning result for a single vehicle based on the vehicle operating status data, the traffic light status data, the lane-level traffic flow data, and the historical traffic data. The aggregation module 30 is used to aggregate the traffic light status data, lane-level traffic flow data and vehicle operation status data of multiple intersections in the area to obtain regional traffic status data, and generate a regional signal timing strategy based on the regional traffic status data. Optimization module 40 is used to coordinate and optimize the preliminary speed planning results of the single vehicle based on the regional traffic status data, and generate a regional coordinated speed guidance strategy. The distribution module 50 is used to distribute the regional signal timing strategy to the traffic signal controller and the regional coordinated speed guidance strategy to the vehicle terminal.

[0073] This application receives vehicle operation status data uploaded by an onboard terminal, acquires traffic light status data and lane-level traffic flow data collected by a roadside unit, and obtains historical traffic data stored on a cloud platform. Based on the vehicle operation status data, traffic light status data, lane-level traffic flow data, and historical traffic data, it generates a preliminary speed planning result for a single vehicle. It then aggregates the traffic light status data, lane-level traffic flow data, and vehicle operation status data from multiple intersections within the area to obtain regional traffic status data, and generates a regional signal timing strategy based on this data. Finally, it coordinates and optimizes the preliminary speed planning result based on the regional traffic status data to generate a regional coordinated speed guidance strategy. The regional signal timing strategy is then distributed to the traffic signal controller, and the regional coordinated speed guidance strategy is distributed to the vehicle terminal. This application, through the coordinated control of vehicle speed planning and regional traffic lights, reduces vehicle waiting times at intersections and improves the traffic efficiency of vehicles passing through traffic light-controlled road sections.

[0074] In one embodiment, the generation module 20 is further configured to predict the traffic light status and queue dissipation time for at least one future signal cycle based on the historical traffic data, the traffic light status data, and the lane-level traffic flow data, to obtain the predicted signal status and the predicted queue dissipation time; determine the current position and speed of the vehicle based on the vehicle operating status data; construct a vehicle speed optimization model based on the current position, the vehicle speed, the predicted signal status, and the predicted queue dissipation time, and solve the vehicle speed optimization model to obtain a preliminary speed planning result for a single vehicle that satisfies vehicle dynamics constraints, safety constraints, and comfort constraints.

[0075] In one embodiment, the generation module 20 is further configured to determine an effective passage time window based on the vehicle's current position, the vehicle's speed, the predicted signal state, and the predicted queue dissipation time; construct an objective function containing an acceleration penalty term, a speed deviation penalty term, and a time window matching penalty term, using the vehicle's acceleration at each discrete future moment as the decision variable to be solved, and the vehicle's position and speed as state variables; set speed limit constraints, acceleration limit constraints, and stop line crossing time constraints based on the effective passage time window; and construct a vehicle speed optimization model based on the decision variables, the objective function, and the constraints.

[0076] In one embodiment, the aggregation module 30 is further configured to construct a regional signal timing optimization model based on the regional traffic state data, using the duration, phase sequence, and cycle length of the signal phases at each intersection within the region as optimization variables; and to solve the regional signal timing optimization model to obtain a regional signal timing strategy under the conditions of satisfying the constraints of minimum and maximum green light duration of signal phases, phase conflict and safe passage constraints, and timing coordination constraints of adjacent intersections.

[0077] In one embodiment, the optimization module 40 is further configured to perform collaborative analysis on the preliminary speed planning results of multiple vehicles based on the regional traffic state data, determine the coordinated passage time window for each vehicle through the intersection, and adjust the speed trajectory in the preliminary speed planning results of the vehicles based on the coordinated passage time window to generate a regional coordinated speed guidance strategy.

[0078] In one embodiment, the optimization module 40 is further configured to input the regional signal timing strategy and the regional coordinated speed guidance strategy into a preset traffic simulation model to simulate the operation effect and obtain regional traffic operation evaluation indicators; and to iteratively optimize the regional signal timing strategy and the regional coordinated speed guidance strategy according to the regional traffic operation evaluation indicators until the preset optimization target is met, thereby obtaining the optimized regional signal timing strategy and the optimized regional coordinated speed guidance strategy.

[0079] In one embodiment, the receiving module 10 is further configured to receive the original positioning information and vehicle driving status information uploaded by the vehicle terminal; perform lane-level positioning enhancement processing on the original positioning information using a multi-source positioning information fusion algorithm to obtain lane-level vehicle position data, and use the lane-level vehicle position data and the vehicle driving status information together as vehicle operating status data.

[0080] The speed guidance and regional energy efficiency optimization device based on vehicle-road-cloud cooperation provided in this application, employing the speed guidance and regional energy efficiency optimization method based on vehicle-road-cloud cooperation in the above embodiments, can solve the technical problem of how to improve the traffic efficiency of vehicles passing through traffic light sections. Compared with the prior art, the beneficial effects of the speed guidance and regional energy efficiency optimization device based on vehicle-road-cloud cooperation provided in this application are the same as the beneficial effects of the speed guidance and regional energy efficiency optimization method based on vehicle-road-cloud cooperation provided in the above embodiments, and other technical features in the speed guidance and regional energy efficiency optimization device based on vehicle-road-cloud cooperation are the same as the features disclosed in the methods of the above embodiments, and will not be repeated here.

[0081] This application provides a speed guidance and regional energy efficiency optimization device based on vehicle-road-cloud cooperation. The speed guidance and regional energy efficiency optimization device based on vehicle-road-cloud cooperation includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the speed guidance and regional energy efficiency optimization method based on vehicle-road-cloud cooperation in the above embodiment 1.

[0082] The following is for reference. Figure 5 This document illustrates a structural schematic diagram of a speed guidance and regional energy efficiency optimization device based on vehicle-road-cloud cooperation suitable for implementing embodiments of this application. The speed guidance and regional energy efficiency optimization device based on vehicle-road-cloud cooperation in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 5 The speed guidance and regional energy efficiency optimization device based on vehicle-road-cloud collaboration shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0083] like Figure 5As shown, the vehicle-road-cloud cooperative speed guidance and regional energy efficiency optimization device may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to the program stored in ROM (Read Only Memory) 1002 or the program loaded from storage device 1003 into RAM (Random Access Memory) 1004. RAM 1004 also stores various programs and data required for the operation of the vehicle-road-cloud cooperative speed guidance and regional energy efficiency optimization device. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via bus 1005. Input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the vehicle-road-cloud cooperative speed guidance and regional energy efficiency optimization equipment to exchange data with other devices wirelessly or via wired communication. Although the figure shows a vehicle-road-cloud cooperative speed guidance and regional energy efficiency optimization equipment with various systems, it should be understood that it is not required to implement or possess all the systems shown. More or fewer systems can be implemented alternatively.

[0084] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0085] The speed guidance and regional energy efficiency optimization device based on vehicle-road-cloud cooperation provided in this application, employing the speed guidance and regional energy efficiency optimization method based on vehicle-road-cloud cooperation in the above embodiments, can solve the technical problem of how to improve the traffic efficiency of vehicles passing through traffic light sections. Compared with the prior art, the beneficial effects of the speed guidance and regional energy efficiency optimization device based on vehicle-road-cloud cooperation provided in this application are the same as the beneficial effects of the speed guidance and regional energy efficiency optimization method based on vehicle-road-cloud cooperation provided in the above embodiments, and other technical features in this speed guidance and regional energy efficiency optimization device are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0086] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0087] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0088] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the speed guidance and regional energy efficiency optimization method based on vehicle-road-cloud cooperation in the above embodiments.

[0089] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0090] The aforementioned computer-readable storage medium may be included in the vehicle-road-cloud cooperative speed guidance and regional energy efficiency optimization equipment; or it may exist independently and not be assembled into the vehicle-road-cloud cooperative speed guidance and regional energy efficiency optimization equipment.

[0091] The aforementioned computer-readable storage medium carries one or more programs. When these programs are executed by the vehicle-road-cloud cooperative speed guidance and regional energy efficiency optimization device, the device performs the following actions: receives vehicle operating status data uploaded by the vehicle terminal; acquires traffic light status data and lane-level traffic flow data collected by the roadside unit; and acquires historical traffic data stored on the cloud platform. Based on the vehicle operating status data, traffic light status data, lane-level traffic flow data, and historical traffic data, it generates a preliminary speed planning result for a single vehicle. It then aggregates and processes the traffic light status data, lane-level traffic flow data, and vehicle operating status data from multiple intersections within the region to obtain regional traffic status data, and generates a regional signal timing strategy based on the regional traffic status data. Based on the regional traffic status data, it coordinates and optimizes the preliminary speed planning result for a single vehicle to generate a regional cooperative speed guidance strategy. Finally, it distributes the regional signal timing strategy to the traffic signal controller and the regional cooperative speed guidance strategy to the vehicle terminal.

[0092] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0093] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0094] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0095] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the aforementioned speed guidance and regional energy efficiency optimization method based on vehicle-road-cloud cooperation, thereby solving the technical problem of how to improve the traffic efficiency of vehicles passing through traffic light sections. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the speed guidance and regional energy efficiency optimization method based on vehicle-road-cloud cooperation provided in the above embodiments, and will not be repeated here.

[0096] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the speed guidance and regional energy efficiency optimization method based on vehicle-road-cloud collaboration as described above.

[0097] The computer program product provided in this application can solve the technical problem of how to improve the traffic efficiency of vehicles passing through traffic light sections. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the speed guidance and regional energy efficiency optimization method based on vehicle-road-cloud cooperation provided in the above embodiments, and will not be repeated here.

[0098] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A method for speed guidance and regional energy efficiency optimization based on vehicle-road-cloud cooperation, characterized in that, The method includes: It receives vehicle operation status data uploaded by the vehicle terminal, obtains traffic light status data and lane-level traffic flow data collected by the roadside unit, and obtains historical traffic data stored on the cloud platform. A preliminary speed planning result for a single vehicle is generated based on the vehicle operation status data, the traffic light status data, the lane-level traffic flow data, and the historical traffic data. The traffic light status data, lane-level traffic flow data, and vehicle operation status data from multiple intersections within the area are aggregated and processed to obtain regional traffic status data, and a regional signal timing strategy is generated based on the regional traffic status data. Based on the regional traffic status data, the preliminary speed planning results of the single vehicle are coordinated and optimized to generate a regional collaborative speed guidance strategy. The regional signal timing strategy is sent to the traffic signal controller, and the regional coordinated speed guidance strategy is sent to the vehicle terminal.

2. The method as described in claim 1, characterized in that, The step of generating a preliminary speed planning result for a single vehicle based on the vehicle operating status data, the traffic light status data, the lane-level traffic flow data, and the historical traffic data includes: Based on the historical traffic data, the traffic light status data, and the lane-level traffic flow data, the traffic light status and queue dissipation time for at least one future signal cycle are predicted to obtain the predicted signal status and the predicted queue dissipation time. The vehicle's current position and speed are determined based on the vehicle's operating status data; A vehicle speed optimization model is constructed based on the vehicle's current position, vehicle speed, predicted signal state, and predicted queue dissipation time. The vehicle speed optimization model is then solved to obtain preliminary speed planning results for a single vehicle that satisfy vehicle dynamics constraints, safety constraints, and comfort constraints.

3. The method as described in claim 2, characterized in that, The step of constructing a vehicle speed optimization model based on the vehicle's current position, vehicle speed, predicted signal state, and predicted queue dissipation time includes: The effective passage time window is determined based on the vehicle's current location, the vehicle's speed, the predicted signal status, and the predicted queue dissipation time. Using the vehicle's acceleration at each discrete moment in the future as the decision variable to be solved, and the vehicle's position and speed as the state variables, an objective function is constructed that includes an acceleration penalty term, a speed deviation penalty term, and a time window matching penalty term. Based on the effective passage time window, set the constraint conditions for speed limit constraint, acceleration limit constraint, and stop line crossing time constraint; A vehicle speed optimization model is constructed based on the decision variables, the objective function, and the constraints.

4. The method as described in claim 1, characterized in that, The step of generating a regional signal timing strategy based on the regional traffic state data includes: Based on the regional traffic state data, a regional signal timing optimization model is constructed using the duration, phase sequence, and cycle length of the signal phases at each intersection within the region as optimization variables. Under the conditions of satisfying the constraints of minimum and maximum green light duration of signal phase, phase conflict and safe passage constraints, and timing coordination constraints of adjacent intersections, the regional signal timing optimization model is solved to obtain the regional signal timing strategy.

5. The method as described in claim 1, characterized in that, The step of coordinating and optimizing the preliminary speed planning results of a single vehicle based on the regional traffic state data to generate a regional coordinated speed guidance strategy includes: Based on the regional traffic status data, the preliminary speed planning results of multiple vehicles are collaboratively analyzed to determine the coordinated passage time window for each vehicle through the intersection. Based on the coordinated passage time window, the speed trajectory in the preliminary speed planning result of the single vehicle is adjusted to generate a regional coordinated speed guidance strategy.

6. The method as described in claim 1, characterized in that, After the step of coordinating and optimizing the preliminary speed planning results of the single vehicle based on the regional traffic state data to generate a regional coordinated speed guidance strategy, the method further includes: The regional signal timing strategy and the regional coordinated speed guidance strategy are input into a preset traffic simulation model to simulate the operation effect and obtain regional traffic operation evaluation indicators. The regional signal timing strategy and the regional coordinated speed guidance strategy are iteratively optimized based on the regional traffic operation evaluation indicators until the preset optimization target is met, resulting in the optimized regional signal timing strategy and the optimized regional coordinated speed guidance strategy.

7. The method as described in claim 1, characterized in that, The steps for receiving vehicle operating status data uploaded by the vehicle-mounted terminal include: Receive raw location information and vehicle driving status information uploaded by the vehicle terminal; The original positioning information is processed by a multi-source positioning information fusion algorithm to perform lane-level positioning enhancement processing, thereby obtaining lane-level vehicle position data. The lane-level vehicle position data and the vehicle driving status information are used together as vehicle operating status data.

8. A speed guidance and regional energy efficiency optimization device based on vehicle-road-cloud cooperation, characterized in that, The device includes: The receiving module is used to receive vehicle operation status data uploaded by the vehicle terminal, obtain traffic light status data and lane-level traffic flow data collected by the roadside unit, and obtain historical traffic data stored on the cloud platform. The generation module is used to generate preliminary speed planning results for a single vehicle based on the vehicle operating status data, the traffic light status data, the lane-level traffic flow data, and the historical traffic data. The aggregation module is used to aggregate the traffic light status data, lane-level traffic flow data and vehicle operation status data of multiple intersections in the area to obtain regional traffic status data, and generate a regional signal timing strategy based on the regional traffic status data. The optimization module is used to coordinate and optimize the preliminary speed planning results of the single vehicle based on the regional traffic status data, and generate a regional coordinated speed guidance strategy. The distribution module is used to distribute the regional signal timing strategy to the traffic signal controller and the regional coordinated speed guidance strategy to the vehicle terminal.

9. A speed guidance and regional energy efficiency optimization device based on vehicle-road-cloud collaboration, characterized in that, The device includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the speed guidance and regional energy efficiency optimization method based on vehicle-road-cloud collaboration as described in any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium. When the computer program is executed by a processor, it implements the speed guidance and regional energy efficiency optimization method based on vehicle-road-cloud cooperation as described in any one of claims 1 to 7.

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