1270 results about "Traffic efficiency" patented technology

The invention relates to the technical field of unmanned vehicle control, and discloses a smart city environmental sanitation unmanned vehicle path planning and real-time monitoring method. The method comprises the following steps: firstly, acquiring a multi-source environment sensing data set such as laser radar point cloud data, a camera image sequence and real-time traffic flow information; local road network features are extracted based on laser radar point cloud data, a dynamic target motion prediction map is generated according to a camera image sequence, and real-time traffic flow information is processed to generate a regional traffic efficiency evolution curve. And inputting the data into a path optimization model to generate an initial path sequence, dividing cleaning task priorities, fusing related data and generating a final path planning scheme through a reinforcement learning algorithm. In addition, operation state data of the unmanned vehicle are collected in real time, and a path correction instruction set is generated through an anomaly detection model to update the strategy network. According to the method, the rationality and the operation efficiency of the path planning of the environmental sanitation unmanned vehicle can be improved, real-time monitoring is realized, and the operation safety and the management intelligence level are enhanced.

The invention discloses a perception intelligent driving complex traffic scene dynamic risk prediction method, and relates to the technical field of risk prediction, and the method comprises the steps: obtaining the multi-source traffic dynamic data of a target region in real time; performing space-time alignment processing on the multi-source traffic dynamic data; inputting the space-time coupling feature matrix into a pre-trained depth space-time prediction model to generate a risk thermodynamic map; calculating a dynamic risk index of each traffic sub-region, and generating a risk level distribution sequence; and triggering a self-adaptive early warning response mechanism according to the risk level distribution sequence, and dynamically adjusting operation parameters of the variable information sign and the traffic signal controller. The technical problems of frequent traffic congestion and high accident risk caused by inaccurate traffic risk prediction and difficulty in dynamic adjustment according to the real-time traffic condition in the prior art are solved, and the technical effects of accurately predicting the traffic risk and improving the safety and traffic efficiency in a complex traffic scene are achieved.

The invention discloses a road traffic signal control system and method, and the method comprises the steps: collecting real-time data through a sensor, a camera and a geomagnetic device, combining historical data with emergency analysis, and predicting a flow mode and a change trend; green light time distribution is dynamically optimized, a fixed timing, inductive and adaptive control strategy is adopted, and traffic light duration and priority adjustment is achieved; through regional cooperation and linkage control, green wave bands and branch intersection red light optimization are set, and peak period traffic signals are intelligently dispatched. A machine learning model is used to predict a flow peak value, environment and accident detection data are combined, a signal lamp period is dynamically adjusted, vehicle waiting time, traffic efficiency, carbon emission and traffic satisfaction are evaluated, and a traffic control strategy is comprehensively optimized. According to the road traffic signal control system and method, the traffic flow in different time periods is predicted by adopting the flow prediction model based on big data analysis and machine learning, and the signal period is adjusted in advance.

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 discloses an expressway lane control method based on real-time traffic flow. The expressway lane control method comprises the following steps of performing fusion sensing through a distributed optical fiber vibration sensor array and a millimeter wave radar; constructing a space-time diagram convolutional network model, and dynamically mapping the road topological structure into a lane connection diagram with variable weight; a layered reinforcement learning architecture is adopted, and an emergency lane dynamic management mode is triggered; and deploying a digital twin sand table to perform multi-resolution simulation verification, and when the actual traffic flow deviates from a predicted value by more than 15%, starting a causal inference engine to backtrack a decision link and update an agent strategy library. According to the method, global dynamic lane optimization is realized through a multi-source perception fusion and hierarchical reinforcement learning architecture, simulation data shows that the morning peak traffic volume is improved by 29.3%, and the accident scene traffic efficiency is improved by 28.6%; a digital twinning and causal inference engine is adopted, the strategy updating response time is shortened to 30 seconds, and the congestion propagation distance is reduced by 35%.

The invention discloses an intelligent road alarm early warning patrol rescue linkage system and a method thereof, and relates to the technical field of road safety. An intelligent hectometer board, an unmanned aerial vehicle cluster and a mobile terminal are used for collecting accident alarm information and road multi-source data; performing accident target detection classification and abnormal condition classification based on the preprocessed data by utilizing edge calculation, and sending to the cloud; importing real-time road multi-source data into a digital twinborn model to obtain traffic situation spatial-temporal characteristics, and combining accident target detection classification and abnormal condition classification analysis to obtain an accident risk thermodynamic diagram for emergency decision making; and according to the accident risk thermodynamic diagram, generating alarm early warning information of different accident types and different risk levels, according to an emergency decision, generating a linkage mechanism of different departments, and sending the linkage mechanism to different department systems to realize automation of a disposal process. According to the invention, a full-chain linkage system of perception-analysis-early warning-linkage is constructed, rapid response and rescue are realized, the management cost is reduced, and the traffic efficiency is optimized.

The invention discloses a road intelligent induction and dynamic early warning method and system integrated with meteorological perception, and relates to the technical field of intelligent traffic and road safety. The method comprises the following steps: acquiring real-time weather, traffic and road data, performing multi-source data fusion by adopting an improved Kalman filtering and attention mechanism, and generating unified state estimation; dynamic risk assessment is carried out in combination with Bayesian reasoning and a Markov model, and speed-limiting adaptive adjustment is realized based on safety, traffic efficiency and energy consumption multi-objective optimization; and further calculating the length and position of the dynamic early warning area, and controlling devices such as intelligent spikes to issue induction information. The system comprises a data acquisition unit, a fusion estimation unit, a risk prediction unit, a speed adjustment unit, an early warning calculation unit and an induction unit. According to the invention, real-time monitoring, risk prediction and intelligent regulation and control of the road traffic environment in complex weather are realized, and the driving safety and the traffic efficiency are improved.

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 discloses a highway data fusion management method, and relates to the technical field of intelligent traffic management, and the method comprises the steps: collecting the global operation data of a highway in real time through a multi-source heterogeneous sensing network; performing space-time alignment and semantic annotation processing on the operation data to construct a fusion data set; inputting the feature subset into a lightweight space-time fusion model to form a multi-target decision set; executing the traffic control instruction sequence in a preset space-time window according to the multi-target decision set; and generating a self-optimization report including a model iteration path and road network health assessment based on the deviation degree of the execution feedback data and the expected optimization target. According to the expressway data fusion management method provided by the invention, collaborative optimization of passing efficiency and safety of the expressway is guaranteed.

The invention relates to the technical field of intelligent driving, and discloses an intelligent networked automobile multi-modal sensing and decision-making system based on deep learning, and the system comprises a multi-modal data collection module which is used for collecting image data and radar time sequence data around a vehicle through a vehicle-mounted camera and a vehicle-mounted radar sensor, the surrounding environment of the vehicle can be sensed in all directions; the image data processing module is used for processing the image data by using an improved deep learning model and extracting target object feature information in the image; the radar data processing module is used for analyzing the change trend of the radar time sequence data and making an optimal driving decision according to different traffic scenes in combination with the feature information of the target object; and the optimal decision execution module is used for executing an optimal decision action according to the optimal driving decision. According to the invention, high-precision perception and quick response to environment information in a complex traffic scene can be realized, the driving safety is improved, and the traffic efficiency is optimized.

The invention provides a traffic participant behavior analysis-based edge calculation signal lamp control method and system, and the method comprises the steps: obtaining video stream data of a target intersection, carrying out the frame-by-frame behavior state analysis of the video stream data through a spatial-temporal feature coding network, extracting the behavior state features of each traffic participant, and carrying out the analysis of the behavior state features of each traffic participant; and inputting the behavior state characteristics into a preset behavior matching model, generating a behavior triggering identifier of the traffic participant in the current time window, generating a signal lamp control instruction of an edge computing node according to the time sequence relevance between the behavior triggering identifier and the phase state of the current signal lamp, and sending the signal lamp control instruction to the edge computing node. And finally, a signal lamp phase switching time sequence of the target intersection is adjusted based on the signal lamp control instruction, so that the traffic participants meeting the traffic rule conflict condition obtain the traffic priority under the signal lamp phase switching time sequence. According to the method, the traffic accident risk is reduced, and meanwhile, the overall traffic efficiency of the intersection is optimized by dynamically adjusting the minimum response period and the phase duration parameters.

The invention relates to a city-level path guidance method and system based on dynamic clustering in a vehicle-road cloud cooperation scene, and the method comprises the steps: constructing a vehicle-road cloud cooperation architecture, and collecting traffic data; the traffic management center updates the road weight and initializes a penalty matrix, and performs congestion detection at the same time; when a congested road section is detected, vehicles possibly affected by congestion are screened out, and a to-be-planned vehicle set is formed; performing spatial clustering on the to-be-planned vehicle set to obtain a plurality of vehicle clusters, and constructing an independent path planning sub-graph for each vehicle cluster; distributing priorities for the vehicles in each vehicle cluster; planning an alternative path for the vehicle; performing batch dynamic adjustment on the road weight and the penalty value on the path planning subgraph; the traffic management center issues the planned alternative path to the corresponding vehicle through the road side unit, and the vehicle runs according to the new running path to realize path induction; finally, reasonable distribution of traffic flow, improvement of road network traffic efficiency and balanced allocation of time-space resources are realized.

The invention discloses a smart city planning design method, and relates to the field of city planning, and the method comprises the steps: collecting monitoring data; determining two targets of land utilization rate maximization and traffic jam minimization according to the preprocessed monitoring data; an epsilon-constraint method is adopted to convert two targets of land utilization rate maximization and traffic jam minimization into a single-target optimization problem; solving a single-target optimization problem by adopting a swarm intelligence algorithm MOEAD to obtain a Pareto non-inferior solution set; performing clustering analysis on the Pareto non-inferior solution set by adopting a K-means clustering algorithm, and dividing the Pareto non-inferior solution set into K clusters according to a clustering result; and according to the preprocessed monitoring data, constructing a three-dimensional city information model by using a smart city CIM technology, mapping each solution in the representative solution set to a corresponding planning parameter in the three-dimensional city information model, and generating a corresponding city planning design scheme. The contradiction between the land utilization rate and the traffic efficiency in urban planning is balanced.

The invention relates to the technical field of smart city traffic, in particular to a smart city traffic indication system and a working method thereof. According to the technical scheme, the system comprises a multi-source heterogeneous data acquisition module, a data preprocessing and fusion module, a feature extraction and analysis module, a traffic flow prediction and signal timing decision module and a signal display and communication module, the acquisition frequency is dynamically adjusted according to the traffic flow. Traffic and meteorological data are dynamically collected through multiple types of sensors, the quality of data preprocessing and fusion is guaranteed, features are accurately extracted, signal timing is optimized through reinforcement learning, the display and communication module effectively transmits information and cooperates with surrounding traffic, emergency situations can be dealt with, tail gas emission is reduced, pedestrian waiting time is shortened, and traffic safety is improved. And a basis can be provided for long-term traffic planning, and the traffic efficiency, the environmental protection property and the pedestrian friendliness are comprehensively improved.

The invention discloses an intelligent signal lamp phase optimization method and system based on vehicle infrastructure cooperation. According to the invention, through the dynamic game model, the system can sense multi-dimensional states such as traffic flow and pedestrian waiting time in real time, dynamically adjust demand weights of vehicles, pedestrians and roadside facilities, flexibly allocate green light duration, i.e., multi-direction multi-passing time of vehicles and multi-pedestrian time period to preferentially guarantee the street crossing demand; the average waiting time of vehicles and pedestrians is effectively shortened, the overall traffic efficiency of the intersection is obviously improved, and the suitability of daily traffic operation is obviously improved. The dynamic game model rapidly recognizes special requirements through a state value function, dynamically improves the weight priority of emergency vehicles or pedestrians, guarantees the efficient passing of key scenes (such as the rapid passing of emergency vehicles), avoids the long-time overstock of vehicles in other directions, and improves the safety of emergency vehicles or pedestrians. And the response capability and coordination level of the system to complex and emergent scenes are obviously enhanced.

The invention belongs to the field of artificial intelligence and intelligent traffic systems, particularly relates to a vehicle path planning system based on multi-agent collaboration, and aims to solve the problems of path response lag, insufficient global optimization ability and conflict between individuals and group targets in a dynamic traffic environment. The system adopts a three-level collaborative architecture of a central coordination server, a regional scheduling agent and a vehicle-mounted agent, realizes macroscopic regulation and local resource allocation through a global guidance potential field and an improved auction algorithm, and improves the dynamic adaptive capacity by combining event-driven replanning and a robustness verification mechanism. Different scheduling and multi-time scale coupling operation of heterogeneous vehicles are supported, and the road network passing efficiency and the path reliability are effectively 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 highway management and control, in particular to a highway hard shoulder opening management and control method based on artificial intelligence. The scheme comprises the steps of collecting traffic flow and vehicle speed in real time; transmitting the preprocessed data to a central control system through a high-speed communication network; testing and optimizing the intelligent decision algorithm by using traffic simulation software, evaluating the performance of the algorithm by simulating different traffic scenes, and adjusting parameters according to a test result; testing and optimizing the optimal strategy by using traffic simulation software, and adjusting parameters according to a test result; integrating the optimized intelligent decision algorithm into a control system of the expressway; the performance of the expressway control system is continuously monitored, the performance comprises the passing efficiency and the accident rate, and the optimal strategy for opening the hard road shoulder is optimized online. According to the scheme, road side sensing is combined with automatic road shoulder development control dynamic adjustment, and safety and passing efficiency of a road system are achieved.

The invention relates to the technical field of intelligent traffic safety management and control, and discloses a highway high-risk road section early warning method and system, and the method comprises the steps: collecting real-time environment and vehicle data through a vehicle side and a roadside sensor, uploading the real-time environment and vehicle data to a cloud platform for data fusion, and obtaining fusion data; establishing a risk judgment model on the cloud platform, updating the risk judgment model in real time through incremental training, analyzing the fused data by using the risk judgment model, and generating a comprehensive risk score; and triggering graded early warning according to the comprehensive risk score, carrying out early warning in advance at a vehicle end, and carrying out visual display on an early warning result. According to the method, the adaptive capacity of the safety detection system to variable weather and vehicle behaviors is improved, finally, accidents are effectively reduced, and the driving experience and the traffic efficiency are improved.

The invention relates to the technical field of intelligent high-speed vehicle passing control, and provides a method for predicting a vehicle driving track under highway passing control based on digital twin tracking simulation, and the method comprises the steps: collecting highway digital twin system, target vehicle and environment data in real time, and carrying out the integration to form an input data set; a future driving track of a vehicle is predicted by using a pre-trained LSTM deep neural network model, and an actual track is collected through devices such as a GPS and the like for verification. And when the prediction deviation exceeds the preset range, the model is dynamically adjusted to improve the accuracy. Prediction results and deviation analysis are transmitted to a background control terminal and a driver mobile terminal in real time, and traffic control, accident early warning and driving aid decision making such as front road condition prediction and suggested driving speed providing are supported. The highway traffic efficiency and safety are effectively improved, the driver experience is enhanced, the development of an intelligent traffic system is promoted, the operation cost is reduced, and remarkable social and economic benefits are achieved.

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 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 relates to an intersection lane division and signal timing cooperative control method in an automatic driving environment, and belongs to the field of intelligent traffic. According to the method, lane function division schemes in corresponding directions are published by using lane function adjustment marks, signal timing schemes in corresponding directions are published by using lane-level signal lamps, and personalized suggestion schemes are pushed to network-connected automatic driving vehicles by using a network-connected communication technology, so that lane-level and vehicle-level refined intelligent management and control are realized, and the intelligent management and control efficiency is improved. The intersection traffic efficiency is improved, and intersection road traffic resources are utilized to the maximum extent. According to the invention, the problem of insufficient management refinement of the plane intersection can be overcome, the traffic capacity of the intersection is improved, traffic congestion is reduced, and the utilization rate of road traffic resources is improved.

In order to solve the defects in the prior art, the invention discloses a mixed traffic flow cooperative control method based on double-layer parameterization deep reinforcement learning. According to the method, traffic high-dimensional spatial features are extracted from an intersection environment through a convolutional neural network, a first-layer model CNN-MPDQN is constructed in combination with a multi-channel parameterization depth Q network algorithm, and the main task of the method is to select a passing priority lane and determine passing duration. And then, on the basis of the first-layer algorithm, a second-layer DQN algorithm is adopted to optimize and coordinate the passing sequence of the vehicles on the lane, and the problem of vehicle track conflict is solved. Through continuous loop training and iteration of a two-layer deep reinforcement learning algorithm model, model parameters are optimized step by step, and finally an optimal convergence model is obtained; the model can effectively cope with various traffic conditions and vehicle behaviors, improves the traffic efficiency and safety of the intersection, and is especially suitable for a mixed flow intersection without signal control.

The invention relates to the technical field of intelligent traffic, in particular to an intelligent traffic accident prediction and emergency response system and method based on vehicle-mounted data. The method comprises the steps of collecting vehicle-mounted data, and then performing denoising processing on the vehicle-mounted data by using an adaptive convolutional neural network; constructing an intelligent traffic accident prediction model based on the de-noised data, capturing key features in time sequence data by using a bidirectional LSTM, an attention mechanism, residual connection and a Dropout layer, and predicting an accident occurrence probability; according to a prediction result, an early warning threshold value is dynamically adjusted, alarms of different levels are triggered, and corresponding measures are taken through automatic emergency response or driver reminding; through collecting data before an accident, a prediction model and an emergency response strategy are optimized, prediction precision is continuously improved, and traffic management efficiency is enhanced. According to the invention, vehicle-mounted data and an intelligent algorithm are comprehensively utilized, accurate accident prediction and efficient emergency response are provided, and traffic safety and traffic efficiency are improved.

The invention aims to provide a large-range urban road network intelligent management and control method based on mobile phone signaling data, and belongs to the technical field of traffic management and control. Real-time traffic flow parameters are obtained by preprocessing the mobile phone signaling data, a high-fidelity microscopic traffic simulation environment is constructed, and on the basis, the real-time traffic flow parameters are obtained; the method comprises the following steps: establishing a multi-dimensional evaluation index system containing operation safety and efficiency, constructing a macro-micro collaborative double-layer planning model, adopting a deep reinforcement learning algorithm, taking continuous-discrete mixed decision variables such as intersection signal timing and a variable lane strategy as optimization objects, carrying out strategy learning and iterative optimization through an Actor-Critic architecture, and carrying out optimization on the optimization objects. And outputting the optimal control strategy combination. According to the invention, dynamic and accurate cooperative management and control of the large-range urban road network are realized, and the traffic efficiency is remarkably improved while the operation safety is guaranteed.

The invention provides a vehicle-road cooperative control method and system for a double-layer ecological city in a mixed traffic flow environment, and the method comprises the steps: taking a signal lamp at each intersection as an intelligent agent, and obtaining the real-time driving information of a vehicle through infrastructure communication; the intelligent agent calculates the vehicle number and the driving speed of each motorcade according to the headstock distance between the vehicles; the upper perceptron predicts the time required by the motorcade to arrive at an intersection stop line by stacking a full-connection layer and a long-short-term memory network; the predicted time information is input into a strategy network and a value network of the intelligent agent to generate actions, and the strategy network is updated through delayed feedback rewards; the optimized suggested driving speed is sent to the head vehicle of the motorcade so as to optimize the driving track of the motorcade, relieve road network congestion and reduce pollutant emission; according to the invention, cooperative control is carried out through distributed decision making and cooperative learning by the intelligent agent, and the vehicle-road cooperative control capability and the traffic passing efficiency of the urban road network are improved.

The invention provides a vehicle-road cloud collaborative mixed traffic flow optimization method, system and device, and relates to the technical field of intelligent traffic, and the method comprises the steps: extracting a mixed traffic flow feature set containing a road environment, a vehicle state and a driver behavior through obtaining and synchronously fusing the multi-source traffic data of a cloud end, a road end and a vehicle end; according to the method, the intention of a driver is predicted by using a double-layer LSTM model, a vehicle trajectory is predicted in combination with an RNN-LSTM model, confidence fusion analysis of the intention and the trajectory is performed on this basis, accurate prediction of traffic conflict events is realized, and global optimization of traffic flow is supported. According to the method, the intention recognition and trajectory prediction precision in a complex mixed traffic environment is effectively improved, the intelligent decision-making capability of a traffic management system is enhanced, and an efficient technical means is provided for relieving traffic congestion, improving the traffic efficiency and guaranteeing traffic safety.