403 results about "Intelligent transportation system its" patented technology

The invention provides a dynamic path planning method and system in an intelligent traffic system, and relates to the technical field of intelligent traffic. Constructing a dynamic road network traffic capacity model based on a space-time diagram convolutional network; generating a multi-branch trajectory prediction result with a confidence score, adaptively starting an encryption parameter synchronization mechanism in combination with vehicle density, and generating a candidate path set through multi-agent collaborative game optimization; performing multi-dimensional deviation degree evaluation based on actual driving data and planning expectation; synchronously generating a multi-mode cooperative guidance signal; the system comprises a multi-source data fusion unit, a space-time modeling engine, a hierarchical decision-making system, a path verification and execution module, a closed-loop feedback controller, an event response system and a multi-mode man-machine interface. According to the method, the global resource utilization efficiency is improved through cooperation of data-driven modeling and hierarchical game decision, and the real-time performance, the safety and the system adaptability of path planning are improved through a closed-loop feedback and event response mechanism.

Disclosed in the present invention are a multi-agent vehicle-road-cloud integrated collaborative decision-making and control architecture system and method based on federated reinforcement learning. A multi-agent federated reinforcement learning decision-making and control framework having embedded vehicle dynamics characteristics is used, so as to solve the problem of in-depth integration of an intelligent traffic system and intelligent vehicles, and realize autonomous driving with vehicle-traffic in-depth decision-making and control collaboration; a semantic matrix is generated at a road side to serve as an input for vehicle-side reinforcement learning, so as to construct vehicle-side global and local trajectory planning guided by the road side; an integrated reward function for vehicle-side reinforcement learning is designed on the basis of a driving safety field constructed by the road side, so as to realize comprehensive consideration of vehicle-side safety and comfort; on the basis of road-side federated learning, vehicle-side neural network parameters are uploaded by means of V2I communication, so as to solve the problem of vehicle-road information asymmetry caused by privacy awareness; and for different environmental sample distributions, a local optimal policy for a current environment is selected by means of neural network screening, so as to synthesize a shared model benefiting from different environments, thus realizing a balance between sample efficiency and model robustness.

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 provides a non-stationary traffic prediction method based on wave flow decomposition and time delay perception. The method mainly comprises the following steps: constructing a traffic network diagram and inputting historical traffic flow data; constructing a decoupling flow layer, and decoupling the original flow into a wave component and a flow component; constructing a time gating convolution module, and capturing short-time dependency; constructing a space-time causal chain of time-delay perception directed graph attention and processing wave components; constructing an adaptive graph convolutional network, and processing global steady-state features of flow components; constructing a time gating convolution module, and capturing long-term time dependence; constructing an adaptive event fusion module; constructing a full connection layer; and outputting the predicted traffic flow data. The method overcomes the limitation of a traditional method in the aspects of prediction accuracy and coping with a complex traffic network, and effectively solves the problems that a traditional traffic flow prediction method is insufficient in prediction accuracy in an intelligent traffic system, cannot reflect the influence of the traffic network and the like.

The invention relates to the technical field of traffic data processing, and discloses a traffic edge computing data security sharing method and system, and the method comprises the steps: collecting traffic edge computing network multi-source heterogeneous data, and carrying out the classification standardization processing to generate a structured data set; designing a dynamic data sharing security protocol based on a multi-party security computing protocol and a homomorphic encryption algorithm; verifying the authority of a requester in a multi-level manner by using an attribute-based access control model and a zero-knowledge proof mechanism, and generating a dynamic access token; storing data by adopting a fragmentation storage and redundancy encryption strategy, and recording storage information through a hash chain; and dynamically adjusting the encryption strength and the sharing strategy according to the network threat level and the data sensitivity. The method effectively guarantees safe sharing of traffic data, accurately controls access authority, improves storage and sharing efficiency, adapts to complex network environment changes, and provides powerful support for development of an intelligent traffic system.

The invention discloses an unmanned aerial vehicle highway intelligent patrol method and system based on patrol demands, and belongs to the field of unmanned aerial vehicle technologies, intelligent traffic systems and artificial intelligence, and the method comprises the steps: obtaining a natural language instruction, and carrying out the semantic analysis to generate a structured task vector; constructing and generating a self-adaptive inspection strategy based on the task vector and fusing real-time data; distributing the strategy to the unmanned aerial vehicle for autonomous inspection, and identifying and generating a classification event alarm; and finally responding to an alarm and triggering a processing mode to generate a closed-loop inspection task report. According to the method, a technical path of combining multi-level reasoning based on a large language model and dynamic behavior strategy synthesis is adopted, and a complete cognition and action framework from semantic intention understanding, behavior decision making to closed-loop response is constructed, so that autonomous generation and intelligent execution of a complex and fuzzy inspection task can be realized; and the automation level, the response speed and the decision-making intelligence of highway inspection are obviously improved.

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.

A method by which a first device performs maneuver driving in an intelligent transportation system (ITS) according to various embodiments may comprise: receiving a vehicle-to-everything (V2X) message related to a maneuver sharing and coordinating service (MSCS) from a second device; and determining whether to perform cooperative driving with the second device on the basis of the V2X message.

The invention discloses an RFID multi-label information fusion and unmanned vehicle path real-time correction method, system and device based on improved whale optimization, and a storage medium, and belongs to the technical field of the crossing field of the Internet of Things and an intelligent traffic system, and the method comprises the steps: collecting received signal strength indication information and label position information, constructing a label signal propagation model and a positioning probability distribution model; performing fusion processing of multi-label positioning information, adopting a dynamic weighting strategy to adjust signal contribution, obtaining an unmanned vehicle position estimation value, taking the unmanned vehicle position estimation value as state input of path optimization, constructing a path cost function, introducing a whale optimization search mechanism based on dynamic update parameters, performing iterative optimization on a path candidate solution, and obtaining a path optimization result; outputting a path correction result; and a real-time path instruction is issued to the vehicle control module, and the unmanned vehicle is guided to complete path correction and navigation control in a dynamic environment. According to the method, the problems of poor path adaptability, low fusion precision and local optimum in the existing method are solved.

The invention relates to a highway vehicle trajectory reconstruction method based on a car-following model and dynamic time warping, belongs to the technical field of trajectory reconstruction, and is particularly suitable for networking and automatic driving vehicle (CAV) environments. The method comprises the following steps: firstly, extracting CAV and detected motion characteristics of front and back common vehicles, and reconstructing a candidate track set by using a car-following model and an inverse car-following model; then, calculating the similarity between the candidate trajectory and the known trajectory by adopting a DTW algorithm; and finally, performing weighted fusion according to similarity scores, and finally generating a more accurate reconstruction trajectory. Experiments show that the method can effectively improve the trajectory reconstruction precision under the condition that the CAV permeability is low, and further reduce errors along with the increase of the permeability, thereby providing data support for the application of automatic driving and intelligent traffic systems.

The invention discloses a congestion management system and method based on vehicle-road cooperation and dynamic group path optimization, and relates to the technical field of intelligent traffic systems. The method comprises the following steps: S1, collecting original traffic data through a multi-modal sensor array to carry out space-time alignment, and constructing a microscopic traffic flow data set; s2, bidirectional information interaction is carried out through a vehicle-road cooperative communication network; s3, calculating a multi-modal fusion confidence coefficient parameter based on the data quality index, and generating traffic state information and road section real-time saturation; s4, fusing historical and static road network data, constructing a dynamic digital twinborn model and a road network state feature map, and generating a road network load balancing index; and S5, making a decision by adopting a hierarchical-distributed architecture, and generating a group path induction strategy through multi-objective optimization. The efficient collaborative decision is realized through the graph convolutional network and the attention mechanism, and the road network traffic efficiency is comprehensively improved through stable and reliable multi-objective optimization on the premise of guaranteeing the user fairness.

The invention relates to the technical field of intelligent traffic systems, in particular to a linear intelligent monitoring system for a bridge fabrication machine, which comprises a multi-source sensing module, a data processing module, a data processing module, a data processing module, a data processing module, a data processing module and a data processing module, and is characterized in that the multi-source sensing module comprises strain sensors, inclinometers, GPS positioning units and machine vision units; the edge computing node is internally provided with a self-adaptive data fusion algorithm and is used for denoising sensor data in real time; the cloud digital twin platform is used for synchronizing the BIM design model and generating a millimeter-level linear deviation early warning signal; the PLC control interface is used for receiving the early warning signal and automatically adjusting parameters of the template system and the walking mechanism; according to the method, environmental noise interference is effectively eliminated through multi-source sensor fusion and a self-adaptive data filtering technology, the monitoring precision is improved to a millimeter level, real-time comparison of construction data and a design model is realized based on a BIM and digital twinning dynamic interaction system, and the timeliness and accuracy of linear control are remarkably improved.

The invention relates to the technical field of image processing, and discloses a traffic behavior real-time identification method based on multi-modal spatial-temporal feature fusion. Traffic scene videos, images and traffic signal lamp state information are collected through a road monitoring camera, and target detection and tracking are carried out on video frames to obtain a target spatial-temporal trajectory; respectively extracting a target visual feature sequence and a signal feature sequence after signal lamp state coding by using a time sequence-space perception module, inputting the two sequences into a multi-modal semantic coupling and fusion network, and generating a fusion feature vector through feature alignment; and based on the vector, target behaviors are discriminated in real time through a behavior classifier, and a normal or abnormal detection result is output and an alarm is given. According to the method, multi-modal information is fused, semantic understanding and dynamic expression are enhanced, recognition robustness is improved, real-time performance and flexibility are achieved, misjudgment and missed judgment can be effectively reduced, and the method has great significance in improvement of the efficiency and the safety level of an intelligent traffic system.

The invention provides a traffic control decision-making method, device and equipment based on data analysis, and aims to solve the technical problems of high subjectivity, insufficient data value mining, disjunction of strategy and practical application and lack of continuous learning optimization capability in the traditional traffic control decision-making. Through standardized fusion processing of multi-source traffic data and reverse optimization configuration of a traffic feature library, in combination with a progressive effect evaluation and reverse deduction verification mechanism, a multi-time scale effect tracking and strategy evolution trajectory acquisition system is innovatively established, and a decision cycle mechanism with autonomous learning and dynamic adjustment capabilities is constructed. The association relationship between the traffic state evolution rule and the control strategy is systematically analyzed, and finally an intelligent traffic control decision framework based on operation state data driving is formed; scientific and reliable decision support and technical basis are provided for application scenes such as urban traffic fine management, intelligent traffic system optimization control, traffic jam treatment and traffic safety guarantee.

The invention discloses an intelligent traffic system and digital twinborn technology integration method under a dual-carbon strategy. A data acquisition module, a data preprocessing module, a carbon emission model, an air quality model, a traffic flow model, a carbon fusion model, a digital twinborn visualization module and a dynamic simulation and prediction module are included. The system also comprises data synchronization and integration, and real-time monitoring and feedback. According to the invention, the monitoring and prediction precision of carbon emission, air quality and traffic flow is improved; real-time dynamic monitoring and optimization of the traffic system are realized, and the intelligent level of traffic management is enhanced; through a multi-objective optimization algorithm, traffic flow, air quality and carbon emission are comprehensively considered, and an optimized traffic management strategy and an optimized carbon emission reduction measure are provided; and by adopting a digital twinning technology, visualization and real-time feedback of data are realized, and transparency and operability of traffic system management are improved.

The invention provides an urban traffic flow prediction method and system, and relates to the technical field of intelligent traffic systems, and the method comprises the steps: receiving traffic data, and carrying out the linear transformation processing through a full connection layer; the processed traffic data are input into multiple layers of time-space modules of the same structure for feature extraction, each layer of time-space module is composed of a time convolution module and a mixed time-varying graph module, and time features output by the time convolution module and spatial features output by the mixed time-varying graph module are spliced to form output of the time-space modules; parameterized learning is carried out on the mixed time-varying graph module; performing jump connection on the outputs of all the space-time modules to generate fused space-time features; and processing the fused spatial-temporal characteristics through a ReLU activation function, and transmitting a processing result to a full connection layer to obtain a final prediction result. According to the method, the urban traffic condition and the change trend can be accurately judged, and effective decision support is provided for urban planning and traffic management.

The invention discloses a traffic sign recognition and lane line detection method and system for extreme weather, and relates to the technical field of computer vision and artificial intelligence. According to the method, an efficient image restoration algorithm is designed, the influence of haze, rain and snow and other weather noise on the image quality is eliminated, and key detail information of traffic signs and lane lines is reserved; secondly, a YOLOv8 target detection network is improved, the problems of missing detection of small targets and false detection of complex backgrounds are solved, and the detection accuracy of the multi-scale traffic signs is improved; furthermore, a lightweight lane line detection algorithm is designed based on an improved line anchor mechanism, and the detection precision and real-time performance of the lane line in a complex scene are improved; and finally, an image restoration module, a traffic sign recognition module and a lane line detection module are integrated, multi-modal support of images, videos and real-time camera data is realized, an engineering solution adaptive to extreme weather is formed, and practical application of an intelligent traffic system in a complex environment is promoted.

The invention discloses a multi-pedestrian target detection method based on an unmanned aerial vehicle, and the method comprises the steps: (1) collecting pedestrian video data of the unmanned aerial vehicle, carrying out the frame extraction preprocessing, and marking a pedestrian bounding box, a type, and a confidence coefficient; (2) constructing a YOLOv8 optimization network, wherein a neck network of the YOLOv8 optimization network is integrated with a feature enhancement module, a channel reweighting module and a spatial context sensing module; (3) designing a ternary loss function fusing intersection-to-union ratio, center distance and length-width ratio constraints, wherein the ternary loss function comprises position, category and confidence loss; (4) training the network to converge by adopting a gradient descent method; and (5) deploying the model to the unmanned aerial vehicle to realize real-time detection. According to the method, by improving the network structure and the loss function, the pedestrian detection precision in a complex scene is remarkably improved, efficient data support is provided for an intelligent traffic system, and pedestrian safety guarantee and traffic management efficiency are enhanced.

The invention discloses a lane line detection and correction method based on a millimeter-wave radar, relates to the technical field of road traffic, and aims at the data characteristics of sparse point cloud data, large noise interference and difficulty in accurate lane recognition of the millimeter-wave radar, the quality of lane point cloud is improved by adopting a DBSCAN clustering method based on two dimensions of space and time, and the accuracy of lane line detection and correction is improved. The ICP algorithm is used for matching the point cloud number of the reference lane, the conversion matrix is accurately calculated, lane correction is achieved, then the lane shape is fitted through the principal component analysis method, smoothness and stability are enhanced, and therefore the smooth and stable lane line can be obtained through fitting of the point cloud data of the millimeter wave radar. The method can stably operate in different environments, has the advantages of high stability, high robustness, automation and low cost, can be suitable for multi-lane detection, complex traffic scenes and intelligent traffic systems, and provides a more reliable lane correction scheme for automatic driving.

The invention belongs to the field of intelligent traffic systems, and particularly relates to a congestion scene emergency decision-making method based on dynamic traffic environment modeling, which comprises the following steps: by introducing a congestion potential field concept, fusing multiple factors such as traffic density, speed, cart proportion and the like into a quantifiable risk index, and distinguishing a current congestion index from a future congestion index; the layout problem of the monitoring points is converted into a mathematical optimization problem with the aim of improving the decision effect from experience design; in the decision-making process, not only is passing efficiency considered, but also dimensions such as safety, user experience and resource utilization rate are included; according to the closed-loop intelligent management system integrating traffic flow long-time-sequence evolution prediction, congestion risk assessment based on the physical potential field theory, multi-dimensional comprehensive decision and monitoring point layout collaborative optimization, the opening and closing opportunity of an emergency lane is scientifically and prospectively determined, and the layout of sensing equipment is synchronously optimized; the traffic jam is relieved to the maximum extent, the road passing efficiency and safety are improved, and meanwhile energy consumption is reduced.

The invention relates to a personalized federal learning method for cross-regional vehicle trajectory anomaly detection, which belongs to the technical field of intelligent traffic systems and comprises the following steps: constructing a federal system architecture consisting of a cloud server and a plurality of geographical distributed clients, each client holding local trajectory data; issuing a global model and constructing a double-model structure comprising a messenger model and a personalized model; the client simultaneously trains double models based on local data, and realizes knowledge migration through mutual distillation; protecting gradient information of the messenger model by adopting a differential privacy mechanism; uploading the protected model to a server, and executing asynchronous aggregation weighted according to the number of samples; and repeating the model issuing and local training process until a termination condition is met. The method gives consideration to global generalization and local adaptation, has the characteristics of asynchronous communication, differential privacy protection and the like, and can effectively improve the trajectory anomaly detection performance in data heterogeneous and communication limited environments.

The invention relates to the technical field of intelligent traffic systems, and provides a vehicle trajectory prediction method and system, and the method comprises the steps: obtaining an RGB image and three-dimensional point cloud data of a vehicle driving environment; obtaining driving fusion data based on the RGB image of the vehicle driving environment and the three-dimensional point cloud data; based on a graph neural network GNNs algorithm and the driving fusion data, driving multi-modal data is extracted, a graph structure relation is constructed based on the driving multi-modal data, space-time interaction relation coding is carried out, and first driving track prediction is generated; based on a neural network framework of a multi-layer attention mechanism, more accurate prediction of a plurality of driving tracks is generated; performing weighted fusion on the plurality of driving track predictions, and outputting final driving candidate track predictions in combination with respective confidence scores; according to the method, efficient fusion of multi-source information is realized through the neural network, the dynamic space-time interaction relationship between the vehicle and the road environment is constructed, the high adaptive capacity and the pre-judgment capacity of driving track prediction are improved, and the method is suitable for an intelligent traffic system.

The invention relates to the technical field of intelligent traffic systems, in particular to a traffic volume prediction method under adverse weather conditions, which comprises the following steps: acquiring and preprocessing multi-source heterogeneous data of roads in a target area; the multi-source heterogeneous data comprises meteorological data, historical traffic flow data, real-time traffic sensor data, dynamic road event data and road network topology data; performing space-time alignment on the preprocessed multi-source heterogeneous data, and determining a fusion feature matrix; based on a pre-trained CNN-LSTM-Transformer hybrid prediction model, inputting the fusion feature matrix, and outputting a traffic volume prediction result in a specified time period in the future; and generating a dynamic traffic control instruction set according to the traffic volume prediction result. The problems that in the prior art, the data dimension is single, spatial-temporal feature extraction is insufficient, and prediction control is disjointed are solved.

The invention discloses a multi-vehicle queue cooperative control method based on dynamic model predictive control and a medium. The method comprises the following steps: establishing a vehicle dynamics model, a vehicle motion state equation, a communication topological structure and a queue state space model; establishing a two-stage control model which comprises a vehicle cut-in optimization model and a distributed model prediction controller; the vehicle cut-in optimization model comprises a target function and a queue stability constraint; the objective function comprises an adjustment cost function during cut-in; the optimization direction is to minimize the adjustment cost; solving and obtaining the optimal cut-in time and position of the cut-in vehicle; and dynamically controlling the vehicle by using the distributed model predictive controller. According to the cut-in vehicle control model, the energy consumption in the cut-in process is remarkably reduced while the cut-in efficiency and the safety are guaranteed, the stability and the cooperative capability of a queue system are enhanced, and an efficient and reliable solution is provided for multi-queue control in an intelligent traffic system.

The invention relates to a multi-source data fusion monitoring system linked with tunnel events and warning illumination, and belongs to the technical field of intelligent traffic systems, and the system comprises a multi-source data sensing and preprocessing module which is used for obtaining original data inside and outside a tunnel, and carrying out the normalization processing of the obtained original data, so as to generate a structured data stream; the dynamic event feature comprehensive evaluation module is used for receiving the structured data stream and performing quantitative evaluation to generate an event severity score and an event influence range radius; the heterogeneous linkage strategy generation module is used for receiving the event severity score and the event influence range radius, and constructing a space-time warning field model to generate normalized warning intensity; and the gradient control instruction execution module is used for receiving the normalized warning intensity, converting the normalized warning intensity into an equipment control instruction, and issuing the equipment control instruction to the terminal controller.

The invention belongs to the technical field of urban traffic planning and intelligent traffic systems, particularly relates to an intersection phase structure optimization method based on a large language model, and aims to improve the intelligent level and operation efficiency of traffic signal control. According to the method, semantic mapping cues of traffic flow and a phase structure are constructed, and a large language model is guided to generate a phase structure scheme adapted to an actual traffic state. Compared with a traditional scheme depending on artificial experience and a fixed structure, the method can automatically generate diversified and data-driven phase structure combinations, and has higher adaptability and generalization ability. In the aspect of technical implementation, the method fuses prompt engineering and guides a large language model to generate an initial phase scheme, and performs evaluation and feedback by using a value network, so that optimization of a phase structure is realized, and the overall operation efficiency of a traffic system is improved.

The invention relates to the technical field of intelligent traffic systems, in particular to a personalized traffic travel route recommendation method based on multi-source data. The method mainly solves the problem that in the prior art, real-time collaborative optimization of a dynamic traffic environment and user personalized requirements is difficult. Real-time information is collected through a multi-source heterogeneous traffic data acquisition module, and a standardized data set is generated through data fusion processing; a dynamic user portrait is constructed in combination with machine learning modeling; adopting a multi-target constraint path to search and calculate personalized candidate routes; dynamically re-planning an optimal route based on the real-time traffic flow; and finally, continuously updating the user portrait through an interactive feedback closed loop. The collaborative optimization of traffic state perception and user preference dynamic matching is realized, and the accuracy and timeliness of route recommendation are remarkably improved.

The invention relates to the technical field of Beidou positioning, in particular to a vehicle Beidou positioning data abnormity repairing method based on deep learning. Comprising the following steps: multi-source pretreatment; generating an abnormal mask; trajectory completion: constructing a deep learning model which is based on an Encoder-Decoder structure, fuses an attention mechanism and introduces assistance of an auto-encoder, and adopts an end-to-end training frame to pass through a multi-objective loss function comprising a mean square error loss item of a position error, a trajectory curvature continuity constraint loss item and a physical rationality constraint loss item; and real-time output and system deployment are fused. According to the method, the space-time association relationship and long-range dependency in the vehicle trajectory data are captured through the deep learning technology, the cooperative processing logic of anomaly detection and trajectory completion is combined, intermittent missing segments and sudden jump points can be accurately identified, the physical rationality of the repaired trajectory is ensured, and the vehicle trajectory repairing efficiency is improved. Therefore, the reliability and the operation precision of a downstream intelligent traffic system are improved.

The invention provides a multi-intersection coordinated traffic control synchronization method, device and equipment, and aims to solve the technical problems of inconsistent coordination timing among intersections, lack of a unified decision-making mechanism, fracture of coordination effect and insufficient fault recovery capability in traditional single-intersection independent control. By establishing an inter-intersection communication connection network and a shared data processing system, a weighted coordination framework and an elastic multi-point synchronous control mechanism based on priority distribution are innovatively constructed, and a self-adaptive reconstruction and efficiency-increasing optimization system is established in combination with coordination effect chain analysis and a flow relay adjustment strategy. Layered management of intersection coordination decision and effect relay transmission amplification are systematically realized, and finally a multi-intersection integrated control framework with unified coordination, elastic adjustment, self-adaptive fault tolerance and continuous synergy capabilities is formed. Scientific and reliable technical support and technical basis are provided for application scenes such as urban regional traffic coordinated management, intelligent traffic system multi-point control, traffic jam coordinated treatment, road network efficiency optimization and the like.

The invention discloses a mixed traffic flow-oriented automatic driving taxi dynamic scheduling system and a collaborative decision-making method, belongs to the field of intelligent traffic systems, and solves the problem that in the coexistence environment of manual driving vehicles and automatic driving taxies, the automatic driving taxies cannot be automatically scheduled. The technical problem of how to efficiently and cooperatively dispatch vehicles, accurately predict demands, optimize energy management and improve the overall operation efficiency of the system is solved. The system comprises a scheduling server which is connected with a road side sensing unit, a vehicle-mounted control unit and a charging station management platform. The scheduling server comprises a traffic flow analysis module; a demand prediction module; a dynamic scheduling module; and an energy collaboration module. The system is mainly used for realizing real-time, dynamic and intelligent scheduling and energy management of the automatic driving taxis in the mixed traffic flow, improving the operation efficiency, relieving the traffic jam and optimizing the charging resource utilization.