1929results about "Controlling traffic signals" patented technology

An intelligent traffic management device for optimizing traffic flow at one or more intersections using reinforcement learning, comprising: a central control unit housed in a weatherproof enclosure and mounted on a structural support near an intersection; a sensor interface operatively connected to a variety of multimodal sensors, including at least radar-based vehicle detectors, inductive loop sensors embedded in the road surface, infrared pedestrian detection detectors, and high-resolution optical imaging modules; a powerful embedded computing unit consisting of a multi-core processor and a GPU configured for on-device inference; a reinforcement learning engine embedded in the computing unit, comprising a deep reinforcement learning model trained to map environmental states defined by traffic-related sensor inputs to optimized action policies for traffic light control; a signal actuation module electrically connected to traffic signal controllers, with action policies outputting signal phase durations and transitions in real time; a communications module supporting wireless connectivity and configured for device-to-device communication and cloud synchronization; a policy synchronization module configured to perform federated learning-based updates of model parameters by exchanging gradient weights or distilled traffic state vectors with neighboring intelligent traffic management devices deployed in a traffic network; where the reinforcement learning engine continuously observes traffic state vectors consisting of vehicle counts, average speeds, queue lengths, and pedestrian activity, and dynamically updates the policy to maximize cumulative traffic throughput and minimize average delay.

The invention relates to the technical field of signal devices, and discloses a traffic signal control method and system based on vehicle-road cloud multi-modal data fusion, and the method comprises the steps: collecting multi-modal traffic data synchronously in real time through a vehicle-end sensor, road-side sensing equipment and a cloud Internet platform; fusing the heterogeneous data by adopting a space-time alignment algorithm, and constructing a standardized space-time feature matrix; traffic flow prediction is carried out based on a multi-layer space-time diagram neural network trained by a federated learning mechanism, and a signal control instruction is generated through reinforcement learning and a multi-objective optimization model; and issuing the green wave parameter, the dynamic timing scheme and the cross-domain coordination strategy to a roadside signal machine through the cloud edge coordination architecture to execute control. The problems that in the prior art, low-delay private network communication cannot be achieved, the data fusion efficiency is low, unmanned driving is not supported, and the deployment cost is high are solved, and the purposes of low-delay communication, high reliability and low risk are achieved.

The invention provides an edge computing traffic light intelligent decision-making method integrated with AI video analysis, and the method comprises the steps: collecting a real-time traffic video stream of a target intersection through an edge computing device, and extracting a dynamic traffic feature set in the real-time traffic video stream, calling a pre-trained space-time analysis model to carry out multi-modal fusion processing on the dynamic traffic characteristic set, generating a traffic state vector of the target intersection, matching a candidate control strategy in a preset decision rule base based on the traffic state vector, and carrying out parameter adjustment on the candidate control strategy through a strategy optimization model to obtain a target traffic state vector; generating a target signal lamp control parameter; and issuing the target signal lamp control parameters to a traffic signal control terminal of the target intersection, and monitoring traffic state change data of the target intersection in real time to update weight parameters of the space-time analysis model. According to the invention, the traffic efficiency, pedestrian safety and traffic management intelligence degree of urban intersections can be improved.

The invention provides an edge calculation traffic light emergency control method and device for a sudden traffic event. The method comprises the following steps: acquiring real-time traffic flow information of a target intersection; determining an emergency response area of the target intersection based on the position identifier of the sudden traffic event, and extracting a dynamic offset between a historical traffic flow feature and a current traffic flow feature in the emergency response area through an edge computing node; inputting the dynamic offset into a pre-trained traffic signal adaptive model, generating a traffic signal lamp control parameter set corresponding to the emergency response area, and sending a control instruction sequence to the traffic signal lamp of the target intersection according to the traffic signal lamp control parameter set, and the execution priority of the control instruction sequence is dynamically adjusted based on the real-time change trend of the vehicle density distribution data and the pedestrian movement track data. According to the invention, the intersection passing efficiency and the safety guarantee capability of traffic participants under the sudden traffic event can be improved.

The invention belongs to the technical field of intelligent traffic systems, and particularly relates to an intersection signal timing dynamic coordination system and method based on real-time traffic prediction.The method comprises the steps of collecting multi-source traffic data, generating a space-time prediction digital twin model, dynamically defining an intersection coordination cluster and executing cluster coordination optimization control. And signal timing and closed loop feedback are carried out. By adopting the technical scheme, the cooperative operation efficiency and the intelligent management level of the urban intersection group can be effectively improved, and the fundamental conversion from local and reactive active cooperative control to global and predictive active cooperative control is realized.

The invention discloses an urban traffic intelligent regulation and control system and method based on digital twinning. The method comprises the following steps: S1, collecting and fusing multi-source data in real time; s2, constructing a digital twinborn model; s3, traffic flow prediction and optimization; s4, multi-system cooperative control is carried out; the system comprises the following modules: a multi-source data real-time acquisition and fusion module which comprises a video data acquisition and processing unit, a geomagnetic sensor network construction unit and a floating car data integration unit; the digital twinborn model building module comprises a three-dimensional road network model building unit and a data fusion and calibration unit; the traffic flow prediction and optimization module comprises an intelligent prediction model unit and a real-time optimization control unit; and the multi-system cooperative control module comprises an emergency priority passing system unit, a reversible lane dynamic adjustment unit and a parking lot induction unit. The traffic efficiency can be remarkably improved, the cooperative response speed is greatly increased, and carbon emission is effectively reduced.

The invention discloses an intelligent traffic signal device and a remote control method, and relates to the technical field of intelligent traffic control. The method is used for solving the problems of low emergency traffic efficiency and global and local control imbalance under sudden traffic events. The method comprises the following steps: firstly, fusing an emergency vehicle navigation path, accident point vehicle motion abnormal parameters and pedestrian aggregation distribution data, constructing a multi-dimensional event evolution feature vector through a space-time encoder, and accurately describing a traffic situation; an event influence domain boundary is delimited based on vehicle trajectory and flow direction consistency analysis, a dynamic evolution model is constructed in combination with an acceleration abrupt change propagation path, and a space-time conflict probability matrix is generated; then executing a hierarchical control strategy, dynamically adjusting an intersection signal period starting time difference in a global level to form an emergency green wave coordination band, and inserting an adaptive full red phase in a local level according to a balance relation between a vehicle arrival rate and a dissipation rate; and finally, reversely correcting model parameters through an actual pass error, updating a conflict probability generation rule, and forming a closed-loop feedback mechanism.

The invention discloses a traffic light intelligent adjustment method and system fused with Internet of Vehicles information, and relates to the technical field of intelligent traffic control, and the method comprises the steps: collecting multi-source traffic data through roadside equipment, and carrying out the time-space alignment and abnormal value filtering; constructing a deep learning prediction model based on the filtered data, and predicting a traffic demand in combination with historical and real-time Internet of Vehicles information; generating a dynamic timing strategy according to prediction, and synchronizing the dynamic timing strategy to an associated intersection through V2X; deploying an edge computing node to adjust traffic light parameters in real time, and sending a suggested speed to the vehicle; a closed-loop feedback mechanism is established, and strategies and models are corrected on line based on trajectory data and efficiency indexes. The technical problem that an existing traffic light timing system cannot fuse multi-source Internet of Vehicles data in real time to carry out accurate demand prediction and adaptive adjustment in a dynamic traffic environment is solved, and the technical effects of remarkably improving the traffic efficiency and shortening the emergency response delay in a complex traffic scene are achieved.

The invention relates to the technical field of traffic control, in particular to a traffic signal cooperative control method based on multi-agent reinforcement learning, and the method comprises the following steps: modeling each signal lamp intersection in a road network as an agent, and constructing a distributed multi-agent control environment; dividing the whole road network into a plurality of small subnets according to spatial correlation, and sharing and aggregating traffic information in the subnets through a neighborhood information sharing mechanism; utilizing a space-time diagram attention network to extract traffic state characteristics including a space-time dependency relationship in an intersection agent and a neighborhood thereof; and defining a traffic state, a traffic signal control strategy, a reward and punishment function, a network architecture and a target function required by training of the distributed intelligent agent, and carrying out joint training on the distributed intelligent agent until a training target is achieved. According to the invention, the real-time sensing capability of the signal control intelligent agent to the traffic flow dynamic state can be improved, and the collaborative decision-making capability among multiple intelligent agents is enhanced.

The invention provides an intelligent traffic dynamic green wave control method and system, a storage medium and a program product, and relates to the field of traffic control systems.The method comprises the steps that real-time traffic data of all lanes and emergency data published by a traffic management department are obtained; obtaining traffic flow prediction results of different directions of each road section in a future preset time based on a neural network model; outputting the congestion level and the congestion duration of each road section in combination with the emergency situation data and a traffic flow theoretical model, and further determining an adjustment sequence; and on the basis of the traffic flow prediction result, calculating the optimal green wave bandwidth of the signal lamp at each intersection in each target road section by using a particle swarm optimization algorithm to obtain an optimal timing combination scheme of the signal lamp in each target road section, and performing real-time timing control. By implementing the method, the traffic flow can be dredged more efficiently, the parking frequency and delay time of vehicles on the road are reduced, the overall traffic capacity of the urban road is improved, and energy consumption and environmental pollution are reduced.

The invention relates to the technical field of smart city traffic management, in particular to an urban traffic jam intelligent prediction and dispersion system and a method thereof, and the system integrates a data acquisition module, a traffic prediction module, an event influence evaluation module and an intelligent dispersion module. The data acquisition module updates multi-source traffic data in real time, and a solid foundation is provided for prediction; and the traffic prediction module accurately predicts the traffic flow by using a deep learning model combining a graph neural network and a space-time attention mechanism based on the received data. The event influence evaluation module analyzes the influence of emergencies on traffic and ensures the pertinence of a dispersion strategy; the intelligent dispersion module optimizes a traffic signal lamp control scheme in real time by using deep reinforcement learning in combination with the prediction result and the event influence; the system comprehensively senses the traffic condition through multi-source heterogeneous data fusion, the traffic flow prediction accuracy is remarkably improved, and intelligent and efficient traffic management is achieved.

The invention discloses an end-cloud cooperative detection method and system for traffic anomalies, and relates to the technical field of intelligent traffic control. The method comprises the following steps: collecting traffic video streams through edge equipment, and identifying abnormal behaviors and generating structured event data by using a lightweight YOLOv3-tiny model; when the confidence exceeds a dynamic threshold and the event type is a high-risk type, uploading a video clip and data to a cloud; the cloud integrates a historical road condition map, meteorological data and a real-time traffic flow state, reconstructs a three-dimensional event scene by adopting a space-time attention pyramid network, and verifies the authenticity of an event in combination with a traffic flow sudden change detection algorithm; and generating a signal lamp forced switching instruction for the risk level overrun event, and issuing the signal lamp forced switching instruction to roadside equipment within 3 seconds to execute emergency response. According to the invention, full-link closed-loop control of traffic accidents from identification to response is realized, and the false alarm probability is greatly reduced while the identification accuracy is guaranteed.

The invention relates to the technical field of traffic communication control, and discloses a vehicle-road cooperative communication optimization system for intelligent traffic. The system comprises a multi-source data sensing module for collecting multi-source heterogeneous data; the communication feature extraction module is used for analyzing the multi-dimensional features; the space-time fusion modeling module is used for constructing a combined space-time feature space; the double-layer resource scheduling library is used for storing an optimization strategy; and the dynamic collaborative decision module is used for generating a real-time scheme and instruction. The method also relates to the functions of abnormal track detection, path re-planning, communication link stability prediction and the like. According to the system, deep fusion and efficient processing of multi-source data are realized, communication resource scheduling and traffic flow regulation and control are optimized, abnormal behaviors of vehicles can be processed in time, the stability of a communication link is guaranteed, the operation efficiency, safety and communication reliability of an intelligent traffic system are effectively improved, and intelligent traffic development is promoted.

The invention relates to an arterial traffic coordination control real-time optimization method. The method comprises the following steps: S1, collecting traffic data and converting the traffic data into input data suitable for a graph neural network; s2, constructing a graph data structure, setting intersections as nodes, setting road sections as edges, and adjusting the weight of the weighted adjacent matrix at a fixed frequency according to the traffic data of the road and the historical traffic flow; s3, taking the graph data structure in the S2 as input, constructing a graph neural network model, and capturing spatial-temporal characteristics among nodes; and S4, formulating or adjusting an objective function according to the spatial-temporal characteristics, the actual traffic demands and the constraint conditions, solving the objective function by a genetic algorithm, carrying out continuous iteration on crossover and variation, and evaluating individuals according to the objective function and the constraint conditions in each iteration to obtain an optimal signal timing scheme. According to the method, spatial-temporal characteristics are effectively captured by adopting a graph neural network technology, signal timing optimization is carried out in combination with a genetic algorithm and reinforcement learning, and a signal timing scheme can be dynamically adjusted according to real-time traffic demands.

The invention discloses an Internet of Vehicles traffic management method based on Beidou positioning, and belongs to the field of traffic management, and the method comprises the steps: obtaining Beidou positioning data and sensor network local sensing data, and extracting global coordinate information and local traffic state information; performing time synchronization processing on the Beidou positioning data and the sampling data of the sensor network by adopting a timestamp alignment method, and eliminating the difference in time scale; whether signal interruption or drifting exists in the Beidou positioning data is judged, and if signal abnormity exists, missing positioning data is estimated through an interpolation algorithm according to local sensing data of the sensor network; according to the fused data, constructing a dynamic distribution model of vehicles in a global road network, and identifying a relationship between traffic density and road network distribution; and a multi-source data regression model based on a convolutional neural network is adopted to predict the vehicle position during signal interruption or drifting, and continuous global positioning information is generated.

The invention relates to the technical field of traffic management, in particular to a road traffic dispersion and emergency command system construction method based on multi-source data fusion, and the method comprises the steps: collecting traffic flow sensor data, video monitoring data, meteorological data, social media user feedback data and the like through a multi-source data collection module; a data fusion and processing module is used, and data-level, information-level and decision-level fusion algorithms are adopted to carry out standardization processing, key information extraction and comprehensive evaluation and prediction on multi-source data. And constructing a traffic flow prediction model based on machine learning, such as a long and short-term memory network model, and accurately predicting the traffic flow. And according to the prediction result and the real-time traffic condition, a traffic dispersion strategy making module is used for adjusting signal lamp timing, issuing traffic guidance information and allocating traffic police resources. In the aspect of emergency command, multi-source data are integrated to evaluate event severity, deploy rescue resources and determine an optimal rescue route, and multi-department information sharing and cooperative work are achieved.

The invention provides a dynamic traffic flow distribution method based on multi-agent reinforcement learning, and the method achieves the dynamic optimization and real-time response of a large-scale vehicle path through a cloud-edge collaborative architecture, and comprises the steps: constructing a multi-agent traffic simulation environment, and carrying out the real-time traffic flow distribution according to the real-time traffic flow distribution. Generating an intelligent body vehicle with a random starting point through the Poisson distribution model; constructing a multi-dimensional observation vector comprising road topological coding, current road density, a neighborhood speed mean value, a target distance ratio and a congestion coefficient; adopting a near-end strategy optimization algorithm to carry out parallel strategy optimization on the multi-agent enhanced strategy network under a Ray RLlib framework; designing a multi-target reward mechanism based on path efficiency, congestion punishment and progress reward; a steering decision is generated according to the real-time observation state, and a vehicle driving route is dynamically updated through a Nash equilibrium solution of the game theory; and a cooperative emergency mechanism is triggered when the traffic jam suddenly occurs. According to the method, the problems of path convergence, response delay and secondary congestion of a traditional method are solved.

The invention discloses a traffic operation and maintenance fault intelligent scheduling method and system based on an AI large model, and belongs to the technical field of traffic control. The method comprises the following steps: collecting a vehicle driving track GPS coordinate set, a traffic flow density matrix, a vehicle-mounted camera monitoring image frame sequence and a fault vehicle owner speed anomaly detection result in real time; constructing a traffic operation state analysis model, and outputting real-time traffic operation state characteristics; generating a traffic fault probability distribution curved surface in a future time window; generating a comprehensive fault positioning confidence coefficient matrix; and planning an optimal maintenance resource path according to the pheromone updating rule, and updating the optimal maintenance resource path to the visual scheduling platform in real time. According to the method, space-time diagram convolutional network dynamic modeling is constructed according to multi-source data, so that the limitation of a space blind area of a single data source is broken through, the fault positioning speed is improved, and the problems of incomplete coverage and low positioning speed in the prior art are solved.

The invention discloses a traffic signal and direction-variable lane cooperative control method based on multi-agent learning, and the method comprises the steps: making a decision through the iteration of a traffic signal control module and a lane guiding control module, and carrying out the operation at the same time to control the traffic; the lane guiding control module adopts a dual depth Q network algorithm to enhance a learning model, an intelligent agent selects actions based on traffic flow and traffic signal states to maximize the lane utilization rate, and an award is calculated based on lane queuing length difference and the maximum direction demand traffic flow ratio; the traffic signal control module agent selects an action based on the observed lane density and the current lane state and obtains an award according to the traffic flow satisfaction of the selected phase. According to the method, the lane utilization rate is improved by controlling lane guidance, and the dynamically changing traffic flow is controlled in space and time dimensions by using a reinforcement learning framework; higher stability and efficiency are shown, and the traffic capacity of the intersection is improved.

The invention discloses a vehicle-road cooperation path planning decision-making system based on deep learning, and belongs to the technical field of intelligent traffic. According to the system, global traffic flow data, vehicle positions and vehicle driving destinations are collected, traffic flow prediction of each road section is carried out in combination with a GNN-LSTM fusion model, and a basis is provided for global path planning; the motion trails of the participants are predicted through a Transform model, and real-time interactive decision making between the vehicles and the participants is achieved through a DQN algorithm; vehicle state data, traffic light time sequence data and intersection geographic parameters are fused based on an MPC algorithm to generate a vehicle intersection passing track, whether a dangerous space-time window exists or not is detected through an IoU algorithm, whether the MPC algorithm with constraints is adopted to correct the vehicle intersection passing track or not is determined, and intersection collaborative decision making is achieved. According to the invention, the dynamic adaptability of vehicle path planning is improved, the safety of vehicle-road cooperation and interaction with surrounding participants is enhanced, and the traffic efficiency is effectively improved.

The invention relates to the technical field of traffic control, in particular to an intelligent traffic signal lamp parameter configuration method and system, and the method comprises the following steps: obtaining a vehicle passing behavior, extracting a transverse offset and longitudinal delay time sequence before and after a steering action, recognizing a signal behavior delay section, and extracting the waiting time length and the release completion time point of a vehicle at the tail of a channel. And calculating a time difference, detecting a longitudinal distance of vehicles in a queue in a real-time period, updating a tail-section control period, and obtaining a tail-section green light continuous regulation result. According to the invention, through careful identification and analysis of vehicle behaviors and position features, optimization of signal scheduling, dynamic adjustment of a signal lamp period through real-time data, reduction of traffic congestion caused by untimely response, calculation of waiting duration and release time of tail vehicles, and allocation of a variable green light time period for each channel, the traffic congestion caused by untimely response can be reduced. The green light time of the tail section can be flexibly regulated and controlled according to actual requirements, traffic delay is further reduced, and the road utilization efficiency is improved.

The invention relates to the technical field of intelligent network connection, and discloses a mixed traffic flow-oriented highway ramp confluence optimization control method, which comprises the steps of selecting different control methods according to state information of vehicles entering a formation control area, indirectly guiding manual driving vehicles to form a mixed formation mode through intelligent network connection vehicles, and controlling the formation control area according to the mixed formation mode. The driving states of all vehicles in the motorcade are monitored in real time; and dynamically calculating a confluence sequence and confluence time according to the driving state of the vehicle team entering the confluence control area, and dynamically adjusting the driving path and speed of the vehicle according to the confluence sequence and the confluence time. According to the method, the passing efficiency in the confluence process is greatly improved, and particularly in a complex traffic flow environment, the problem that in a traditional method, complex dynamic interaction during vehicle team confluence cannot be processed is solved.

The invention relates to the technical field of traffic scheduling, in particular to a road traffic analysis scheduling method and system based on event driving. The method comprises the following steps: collecting traffic state data in real time, and generating an original traffic data flow; feature extraction is carried out on the original traffic data flow, a space-time traffic flow graph is constructed, and a dynamic traffic network graph is generated; performing analysis and space-time correlation analysis on the dynamic traffic network diagram to generate a traffic event list; triggering an intelligent scheduling response based on the traffic event list, calling a corresponding scheduling strategy template according to the event type, and generating a candidate scheme set; a multi-objective optimization model is constructed for optimization, and an optimal scheduling scheme is generated; issuing the optimal scheduling scheme to road control equipment, and executing traffic scheduling; and collecting traffic feedback data, evaluating a scheduling effect, and if an expected target is not reached, adjusting scheduling parameters and regenerating an optimization scheme until the traffic state is improved. According to the invention, accurate analysis and efficient scheduling of road traffic can be realized.

The embodiment of the invention discloses a tunnel construction scene traffic scheduling method and system based on intelligent traffic, and the method comprises the steps: obtaining a vehicle state data set of each traffic node in a tunnel construction region, carrying out the dynamic scheduling processing of the vehicle state data set, generating a traffic light control instruction set corresponding to each traffic node, scheduling strategy generation processing is executed according to the traffic light control instruction set, a global traffic scheduling strategy of the tunnel construction area is obtained, and the global traffic scheduling strategy is fed back to a traffic light control system of the tunnel construction area so as to indicate the traffic light control system to execute switching operation of traffic node passing states according to a preset time window. Therefore, the problems of scheduling instruction lagging and low resource utilization rate can be improved.

The invention relates to the technical field of traffic control, and provides a regional green wave coordination control method and system for traffic safety early warning. The method comprises the following steps: traversing a target traffic area to collect traffic data, and constructing a regional dynamic traffic characteristic matrix; performing green wave coordination analysis based on the matrix, and generating a sub-region division scheme; traffic flow characteristics of the subareas are extracted, and green wave coordination control parameters are calculated; formulating and executing an initial control scheme according to parameters, and collecting feedback data; and compensating the optimization parameters according to a feedback result to obtain a final regional coordination control scheme. The technical problem that efficiency and safety are difficult to cooperate due to the fact that traditional green wave control only statically optimizes traffic efficiency and cannot dynamically respond to traffic safety risks is solved, deep coupling of safety early warning and green wave control is achieved through real-time traffic data feedback and dynamic parameter compensation, and the traffic safety is improved. And the traffic efficiency is ensured, and the accident risk is obviously reduced.

The invention provides a situation awareness traffic control method and system based on a time sequence large model, and a medium, and belongs to the technical field of traffic control. The method comprises the following steps: processing a road monitoring video stream through a time sequence large model to determine a situation awareness data set corresponding to a first preset time period; and analyzing a sensing data change result of the situation awareness data set based on the historical situation awareness time sequence data, and matching each associated road section corresponding to the monitored road section of the first monitoring equipment so as to construct a road network space of a corresponding associated type. And according to the similar road correlation characteristics corresponding to the road network space, matching a correlation weight set corresponding to the multi-source data type so as to perform weighted fusion on the multi-source data of the monitoring equipment group in the road network space, and obtaining a road control grade judgment index corresponding to the road network space. And determining a corresponding traffic control strategy according to each road control grade judgment index and a preset situation awareness control data set, and sending each traffic control strategy to a traffic control management terminal.

The invention discloses an intelligent traffic light coordination control method and system for traffic flow optimization, and relates to the technical field of intelligent control. The method comprises the following steps: collecting traffic flow data of lanes in all directions in a target area in real time through heterogeneous sensor networks deployed at all intersections; identifying the traffic flow data to obtain tidal traffic flow features; performing dynamic intensity evaluation based on the tidal traffic flow characteristics to obtain a tidal direction intensity index; a multi-agent cooperative network is constructed, and a continuous intersection green light phase offset sequence is generated based on cooperative calculation of the multi-agent cooperative network; and when the tide direction intensity index is greater than a preset threshold value, inputting the continuous intersection green light phase offset sequence into a traffic signal lamp control system so as to start a tide green wave mode. The technical problems that in the prior art, traffic flow control is not flexible enough, and traffic lights cannot be intelligently adjusted according to the traffic flow condition are solved, and the technical effects of improving the road passing efficiency and reducing congestion are achieved.

The invention relates to the technical field of intelligent sensing control, in particular to an intelligent sensing control method and system based on multi-modal sensor fusion. According to the method, vision, millimeter-wave radar, geomagnetic and other multi-mode sensors are deployed to collect traffic data, feature information is extracted after preprocessing, an adaptive weighted fusion algorithm is adopted for fusion, and an intelligent control decision is generated according to a fusion result. The method comprises the steps of signal lamp timing optimization based on fuzzy logic, traffic flow induction based on reinforcement learning and traffic event detection and response based on event rules, and finally an instruction is sent to traffic control equipment to adjust the flow. The advantages of various sensors are integrated, the limitation of a single sensor is overcome, traffic information is accurately and comprehensively obtained, intelligent decision is made on the basis, the traffic condition can be comprehensively and accurately sensed, traffic control is intelligently optimized, the self-adaptive adjusting capacity is high, the traffic operation efficiency can be improved, and the traffic safety can be enhanced.

The invention belongs to the field of intelligent transportation, and particularly relates to a non-signalized intersection multi-agent scheduling optimization method based on deep reinforcement learning. The method comprises the following steps: establishing an online network and a target network based on an Actor-Critic structure, wherein the online network and the target network are used for dispatching and controlling intelligent networked vehicles; a joint reward function is designed by combining the characteristics of the non-signalized intersection, and factors such as safe distance, traffic efficiency, speed deviation and collision punishment are comprehensively considered; historical interaction data of the vehicle is stored through an experience playback pool, time sequence correlation is broken, and the sample utilization rate is increased; through a deadlock detection and processing mechanism, it is ensured that the vehicle is prevented from being deadlocked in a complex traffic environment; and providing an optimal scheduling strategy for each intelligent network connection vehicle by using the trained model, wherein the optimal scheduling strategy comprises driving speed and passing time suggestions. According to the method disclosed by the invention, the traffic safety of vehicles is effectively ensured, and efficient scheduling and traffic control of the non-signalized intersection are realized while the waiting time is shortened and the traffic jam is relieved.

The invention discloses an intelligent interpretable traffic signal adaptive control method. The method comprises the following steps: training an intelligent agent through reinforcement learning according to environment state information of an intersection; a timing decision of each phase of the intersection is generated by using the intelligent agent; guiding the first large language model to generate pre-training data by the timing decision and the cue word to perform LoRA fine tuning on the second large language model, and inputting the cue word into the fine-tuned second large language model to enable the second large language model to generate a plurality of reasoning tracks to generate positive samples and negative samples; performing all-parameter fine tuning on the second large language model to obtain a traffic control signal decision model; and inputting the constructed cue word into a traffic control signal decision model to obtain each phase timing scheme of the intersection. According to the method, the defects that an intelligent traffic signal control algorithm based on deep reinforcement learning lacks interpretability and the cross-scene generalization ability is poor are overcome, and the method has important significance in improving the decision credibility and the deployment efficiency.