6419results about "Vehicle infrastructure communication" patented technology

An improved system and method of defining speeding severity levels, receiving over a wireless network from a vehicle device speed data and metadata, determining associated speeding severity levels, based at least in part on the determined associated speeding severity levels, determining whether an alert communication is to be transmitted the vehicle device, and in response to determining that an alert communication is to be transmitted the vehicle device, transmitting the alert communication via a wireless interface to the vehicle device, the alert communication configured to cause the vehicle device to provide a corresponding in-vehicle alert.

The invention discloses a traffic transportation operation monitoring early warning and decision analysis method based on vehicle infrastructure cooperation, and relates to the technical field of intelligent traffic. According to the method, real-time collection of dynamic traffic elements is realized through a multi-dimensional sensing network of a vehicle end, a road side and an environment and V2X communication, time-space reference unification of multi-source data is ensured, a road-vehicle-environment-event semantic network is constructed, multi-dimensional recognition of abnormal events such as accidents, congestion and severe weather is realized in combination with hierarchical feature extraction, and the method has the advantages of being high in practicability and high in practicability. The method is advantaged in that identification accuracy is improved, abnormal event propagation paths can be predicted, global road network situation prediction capability is realized, differential early warning is generated based on a comprehensive risk index, multi-level responses such as traffic signal adjustment and path planning are triggered, response time is greatly shortened, emergency response efficiency is optimized, a decision execution effect real-time feedback mechanism is established, and the method is suitable for popularization and application. The system performance is continuously optimized along with data accumulation, and the defect that a big data platform lacks an intelligent decision closed loop is avoided.

The invention provides a data transmission method and transmission device applied to a vehicle-road cloud collaborative road network system, and the method comprises the steps: collecting vehicle operation state data through a vehicle-mounted terminal, collecting real-time road network topology data through road side equipment, and transmitting the data to a cloud server for data preprocessing; performing feature extraction on the preprocessed data based on a preset time-space correlation model, generating dynamic road network feature data, calling a cooperative transmission model to perform transmission path optimization on the dynamic road network feature data, generating a path optimization parameter set, and performing dynamic adjustment on a target transmission path according to the path optimization parameter set. Generating a self-adaptive transmission path adaptive to the current road network state; and distributing the target vehicle data packet to the roadside node and the cloud node through the self-adaptive transmission path, and carrying out retransmission scheduling on the data packet which is not successfully transmitted based on a feedback confirmation mechanism. According to the invention, the communication efficiency and the system robustness in a vehicle-road cloud collaborative scene can be improved.

The invention discloses a low-altitude integrated management system based on a grid digital twin model and an intelligent algorithm, and relates to the technical field of low-altitude aircrafts. The method comprises the following steps: multi-source sensing data fusion and grid coding are carried out, and an airspace environment space-time database is constructed; constructing an airspace environment space-time database to carry out three-dimensional subdivision on the airspace; generating a multi-scale grid twinborn body, and associating a corresponding attribute for each level of grid according to a management requirement; generating an airspace risk thermodynamic diagram in the future 5-30 minutes; establishing a grid state change triggering rule; and generating a multi-target optimal path set by taking the grid navigation cost as a weight. According to the invention, by combining the real-time operation data of the low-altitude aircraft, the meteorological environment and other data with the artificial intelligence algorithm and the navigation rule base, the airspace traffic flow is analyzed, the flight plan is optimized, the flight route is intelligently distributed, the automatic route setting of any two points is realized, and the response speed of the low-altitude flight service system and the processing capability of various flight data are improved.

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.

A method of optimizing dependent task offloading in an Internet of Vehicles using deep reinforcement learning is provided, including the following steps: S1: constructing a vehicle system network; S2: constructing a task model for an application program; S3: constructing a task load model: calculating delay, energy consumption and incentive compensation for local computing, offloading to nearby vehicles and offloading to nearby RSUs three offloading methods based on the vehicle network system and the task model, respectively; S4: determining task priorities: first determining the priorities of each of subtasks according to allocation of predecessor nodes of the subtasks combining with dynamic network environment, and then scheduling according to a multi-queue algorithm; and S5: finding an optimal offloading strategy by using the deep reinforcement learning.

The invention provides a 5G technology-based fleet local area network and satellite communication cooperation system, and relates to the technical field of data processing, and the system comprises a ship terminal which is used for obtaining ship communication data, including environment perception data, ship state data and instruction interaction data, and carrying out the classification processing of the data; the fleet local area network base station is used for carrying out priority division according to data categories and transmission requirements; the satellite communication module is used for dynamically adjusting a data transmission mode according to the transmission state to obtain complete satellite transmission data; the shore-based control center is used for receiving the complete satellite transmission data and performing fusion processing on the complete satellite transmission data to obtain global fleet situation information; the shore-based control center generates a navigation adjustment instruction according to the global fleet situation information, and issues the navigation adjustment instruction to the ship terminal through the satellite communication module to obtain synchronous adjustment data; the ship terminal receives the synchronous adjustment data and adjusts a navigation strategy according to the synchronous adjustment data; according to the invention, the autonomy and accuracy of the fleet cooperation system are improved.

The invention relates to the technical field of ship communication, in particular to a multi-link converged communication method and system for a ship. The method comprises the following steps: receiving navigation information broadcasted by other ships in a preset range, evaluating the availability of a communication frequency band, and generating sea area communication channel state data; acquiring geographic position data of each ship; space communication link potential analysis is carried out according to the geographic position data of each ship and the sea area communication channel state data, and cross-chain space communication potential data is generated; constructing a multi-link ad hoc network topological structure according to the cross-chain space communication potential data; and task adaptation communication link allocation is carried out based on a multi-link ad hoc network topological structure, and a multi-link fusion transmission strategy is constructed, so that real-time communication link safe switching management is realized. According to the invention, on the basis of communication service adaptation and a multi-link fusion transmission strategy, real-time communication safe switching and high-reliability transmission of the marine formation are realized.

The invention discloses a vehicle positioning method and system based on UWB and vehicle constraint, and relates to the field of vehicle positioning, and the method comprises the steps: deploying a UWB reference station in a well mining environment, and enabling a vehicle to be provided with a UWB tag; obtaining distance measurement data of at least two UWB reference stations, and detecting and compensating the distance measurement data to obtain a vehicle UWB position; collecting vehicle-mounted IMU (Inertial Measurement Unit) data, and obtaining a vehicle attitude quaternion through attitude calculation; collecting vehicle wheel speed meter data, and correcting the vehicle wheel speed meter data according to vehicle motion constraint conditions; according to the vehicle UWB position, the vehicle attitude quaternion and the corrected vehicle wheel speed meter data, the vehicle position is obtained through an adaptive Kalman filtering fusion algorithm. Aiming at low positioning precision caused by easy accumulation of errors and drifting of a traditional inertial navigation system in a well mine closed space in the prior art, the invention reduces the influence of a multipath effect in the traditional inertial navigation system on the positioning precision.

The invention discloses an Internet of Vehicles communication resource allocation method based on federal multi-agent deep reinforcement learning, and belongs to the technical field of deep crossing of intelligent transportation, wireless communication and distributed machine learning. According to the technical scheme, in an Internet of Vehicles scene, a vehicle is taken as an agent, and local training is performed through a multi-agent depth deterministic strategy gradient algorithm: each agent decides spectrum access, power control and bandwidth allocation based on a local state; asynchronous federation learning is adopted, each agent uploads local model parameters to a global server, weights are dynamically adjusted based on model updating quality, communication quality and updating frequency, and a global model is generated through weighted aggregation; and issuing the updated global model to an intelligent agent to realize distributed privacy protection and dynamic optimization of communication resources. The method has the beneficial effects that the spectrum efficiency, the transmission success rate and the system adaptability are remarkably improved on the premise of ensuring the data security, and the method has wide application prospects and commercial values.

The invention provides an intelligent network connection automobile abnormity detection system and method based on big data analysis, and the method comprises the steps: obtaining metadata in the communication between a vehicle CAN bus and a cloud, the metadata comprising a protocol type and a load feature, carrying out the analysis of the load feature according to the protocol type, and extracting a key field of a data package; constructing a load feature matrix according to the key field, and normalizing the heterogeneous data to obtain a feature data set in a uniform format; a mahalanobis distance algorithm is adopted to calculate the distance between each data point in the feature data set and a preset base line, and if the distance exceeds a preset threshold value, abnormal traffic is judged, and an abnormal traffic mark list is generated; fingerprint features of a transmission link are obtained from the 5G network slices, the fingerprint features of different slices are subjected to standardization processing, and a cross-slice consistent fingerprint feature data set is generated; therefore, security threats in a complex Internet of Vehicles environment can be effectively handled, and the accuracy and reliability of abnormal traffic detection are improved.

Described herein are examples of a system that processes information describing movement of a vehicle at a time related to a potential collision to reliably determine whether a collision occurred and / or one or more characteristics of the collision. In response to obtaining information regarding a potential collision, data describing movement of the vehicle before and / or after a time associated with the potential collision is analyzed to determine whether the collision occurred and / or to determine one or more collision characteristic(s). The analysis may be carried out at least in part using a trained classifier that classifies the vehicle movement data into one or more classes, where at least some the classes are associated with whether a collision occurred and / or one or more characteristics of a collision. If a collision is determined to be likely, one or more actions may be triggered based on the characteristic(s) of the collision.

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 discloses an unmanned vehicle speed centralized control method and system, and relates to the field of unmanned driving, and the method comprises the steps: obtaining the environment data of an intersection through a roadside sensing device; performing multi-sensor data fusion and calculation through a roadside calculation unit to obtain a multi-vehicle speed control scheme; broadcasting the multi-vehicle speed control scheme to the unmanned vehicle at the intersection through a road side communication RSU; and the vehicle-mounted controller of the unmanned vehicle controls the unmanned vehicle according to the multi-vehicle speed control scheme in combination with the environmental data within the preset range of the unmanned vehicle acquired by the vehicle-mounted sensing equipment. Aiming at the lack of cooperative control of unmanned vehicles at the intersection in the prior art, the road side calculation unit is used for performing multi-sensor data fusion, and a multi-vehicle speed control scheme is generated by combining a trajectory prediction and model prediction control algorithm, so that the multi-vehicle speed centralized control at the intersection is realized.

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 embodiment of the invention provides an unmanned aerial vehicle task scheduling method and device for intelligent road network patrol, and precise planning of an unmanned aerial vehicle patrol route is realized by constructing a three-dimensional road network topology model and an obstacle distribution map. And designing an unmanned aerial vehicle scheduling strategy based on task fitness, and extracting patrol rules from the task template library to perform intelligent scheduling in combination with the real-time position, the endurance mileage and the load state. A grading early warning mechanism is introduced, and the event influence range is analyzed through target detection and scene recognition, so that differentiated early warning of the roadside equipment and the vehicle-mounted terminal is realized. According to the method, the defects of the traditional technology in the aspects of task planning, unmanned aerial vehicle scheduling, early warning linkage and the like are effectively overcome, and the intelligent level and the emergency disposal effect of road network patrol are remarkably improved.

The invention provides an intelligent barn automatic inspection system and method, and relates to the technical field of intelligent inspection, the system comprises an inspection robot, a 5G communication transmission module and a remote computer, the inspection robot comprises an environment sensing module for generating and updating an environment map and identifying obstacle information; the autonomous navigation control module comprises a path planning unit for generating an inspection path and performing local path adjustment; the obstacle avoidance unit is used for generating an obstacle avoidance strategy; the motion control unit controls the movement of the inspection robot; the data acquisition module is used for acquiring information in the granary; the data processing and analyzing module is used for processing and analyzing the collected multi-source data and outputting a monitoring report and early warning information; wherein the 5G communication transmission module is connected with the inspection robot and the remote computer, and is used for transmitting data output by each module in the inspection robot and receiving an instruction sent by the remote computer. According to the invention, real-time sensing, intelligent obstacle avoidance and autonomous inspection of the granary environment are realized.

The invention provides a real-time map route construction method and system. The method comprises the following steps: installing various sensors on an unmanned aerial vehicle to obtain multi-modal data of an environment; converting the multi-modal features into a high-dimensional embedding space, and constructing a real-time three-dimensional environment model based on a multi-modal point cloud reconstruction algorithm; deploying edge computing nodes, and distributing computing tasks to the edge computing nodes; planning a cooperative path among the multiple unmanned aerial vehicles; constructing a multi-unmanned aerial vehicle cooperative global adaptive adjustment mechanism to dynamically adjust the flight path of the unmanned aerial vehicle; and performing priority ranking on the tasks of the flight path of the unmanned aerial vehicle, constructing a path adjustment optimization mechanism according to the real-time flight condition to minimize the task failure risk, and updating the task priority and the scheduling strategy according to the feedback after each task is executed. According to the invention, a plurality of key problems in the prior art are effectively solved, and a technical guarantee is provided for successful application of the unmanned aerial vehicle in more complex application scenes.

A QoS-aware service migration method in an IoV system based on CVX deep reinforcement learning is provided. Firstly, the optimization problem of service migration and resource allocation is decoupled into two sub-problems; then, a new actor-critic-based asynchronous-update deep reinforcement learning method is designed to explore the optimal service migration, where a delayed-update actor makes decisions of service migration and a one-step-update critic evaluates the decisions to guide the direction of the policy update; and on this basis, an optimal resource allocation method for an MEC server based on convex optimization is proposed to achieve the optimal resource allocation. According to the QoS-aware service migration method, extensive experiments are conducted based on a real-world testbed and a dataset of city vehicle trajectories, thereby verifying the feasibility and effectiveness of SeMiR. Compared with benchmark methods, the SeMiR has the best performance of service migration under various scenarios.

The invention relates to the technical field of remote monitoring, in particular to a wireless vehicle state monitoring system which comprises a state change evaluation module, a data transmission adjustment module, a power anomaly detection module, a health state evaluation module and a risk early warning optimization module. According to the method, the vehicle operation parameters are obtained, the change amplitude and the rate of the parameters are analyzed in multiple time windows, the change amplitude and the rate are compared with the preset threshold values, the transmission frequency is adjusted according to the stability of the state, important data are updated in time, and the abnormality of the power system is detected by analyzing the data transmitted in real time. By matching specific data of engine torque and gear shifting modes, prediction of potential faults of the vehicle is more accurate, data of main components of the vehicle are integrated to be compared with health standards, accurate evaluation of the overall health state of the vehicle is achieved, risk early warning can be given out in advance, driving safety is optimized, and the driving safety is improved. And the service life and the operation efficiency of the vehicle are improved.

An unmanned aerial vehicle (UAV) agent for forming a two-tier hybrid smart agriculture network architecture is provided. The UAV agent is configured to support two communication protocols, a short-range communication protocol and a long-range communication protocol. The agriculture sensor gathers agriculture data such as soil temperature and participates in a first-tier short range wireless communication network to send the collected data to at least one UAV agent. A UAV agent participates in one or more first-tier short-range communication networks to pick up sensor data from agriculture sensors in one or more clusters and also participates in a second-tier long-range communication network to route the collected sensor data to at least one cloud server. The tasks in both tier networks are formulated as optimization problems to achieve optimal data delivery and solved by using multi-agent reinforcement learning (MARL), which is implemented by the invented Focus Coordination Multi-Agent Deep Deterministic Policy Gradient (FC-MADDPG) algorithm.

The invention discloses an unmanned sweeper and cloud platform data interaction system based on hybrid encryption, and particularly relates to the technical field of communication interaction information security, which comprises an authority authentication module, a data encryption module, an information processing module, a master-slave authentication control module, a transverse trust management module and a communication scheduling module, an encryption strategy and a key template are dynamically matched through a task score and a mapping model, off-line identity authentication, transverse trust signature and a channel switching mechanism are supported, and the security of data transmission, the fault tolerance of the system and the cooperative reliability are improved under the scenes of task switching, network abnormity and the like; according to the method, a task-driven dynamic encryption matching mechanism is realized, and the data security and the resource efficiency are improved; a multi-level authentication and role signature map is constructed, and the collaborative credibility and fault-tolerant capability of the equipment are enhanced; and a priority index scheduling strategy is introduced to ensure that key data is preferentially transmitted in a high-fluctuation network environment, so that the overall stability and safety of the system are improved.

The invention provides an anonymization processing method and system for emotion data in a vehicle. The anonymization processing method comprises the steps of collecting multi-mode emotion data; correspondingly carrying out local preprocessing and multi-modal alignment processing on the multi-modal emotion data; corresponding sensitive emotion feature information in the preprocessed multi-modal emotion data is extracted through a lightweight recognition algorithm, and the sensitive emotion feature information at the recognized position is packaged in a unified mode; performing desensitization processing on various types of emotion modal data, and encapsulating and synchronizing desensitization results with labels; deep emotional feature extraction is performed on the image, the voice and the physiological signal through a multi-modal fusion model, and the extracted three types of deep features are fused to obtain an emotional representation vector; and inputting the emotion representation vector into a pre-trained emotion recognition model, and outputting the current emotion state of the passenger, including the specific emotion category and the corresponding confidence coefficient. According to the method, emotion analysis and transmission are performed after data anonymization is realized, and accurate judgment of the system on the emotion state is not influenced while privacy security of the user is ensured.

Disclosed are a method and device for a first terminal to transmit a positioning reference signal (PRS) through a physical sidelink feedback channel (PSFCH) in a wireless communication system supporting sidelink communication according to various embodiments. Disclosed are a method and device, wherein the method comprises the steps of: allocating a first frequency resource region for a PRS within a resource region for the PSFCH so that the PRS is multiplexed with a feedback signal; transmitting allocation information on the first frequency resource region; and transmitting the PRS and the feedback signal through the PSFCH on the basis of the allocation information, wherein the size of the first frequency resource region is determined on the basis of the size of a pre-configured frequency resource for the feedback signal and the type of the multiplexing, and the allocation information includes information on the multiplexing type, the size of the first frequency resource region, and the start frequency of the first frequency resource.

The invention relates to the technical field of vehicle-mounted communication, and discloses a CAN network-based vehicle-mounted equipment data transmission and interaction method, which comprises the following steps of: S101, acquiring a message sent by vehicle-mounted equipment, and analyzing to extract vehicle operation data; s102, receiving the vehicle operation data by the CAN gateway controller, extracting signal content, performing classification optimization, and performing compression packaging; s103, sending the packaged vehicle operation data to an external terminal device; s104, the external terminal device receives and analyzes the packaged vehicle operation data; and S105, the remote terminal sends an encrypted control instruction to the vehicle-mounted gateway, the gateway verifies the encrypted control instruction, converts the encrypted control instruction into a CAN message and executes the CAN message, the ECU generates a feedback signal and returns the feedback signal, and the gateway feeds the feedback signal back to the remote terminal through wireless communication to complete closed-loop interaction. According to the invention, the real-time transmission of safety key data is ensured, and the stability of wireless communication is improved.

The invention provides an AmBC-NOMA communication resource joint optimization method for a high mobility V2X scene, and relates to the technical field of communication resource joint optimization. Aiming at channel dynamic change caused by high-speed motion of a vehicle and the influence of the channel dynamic change on short packet communication reliability and rate performance, the method combines reflection type communication and non-orthogonal multiple access technologies, and on the basis of fully considering channel time-varying characteristics, a reflection coefficient, a power distribution coefficient and block length resource distribution in a system are jointly optimized, so that the reliability and the rate performance of short packet communication are improved. Therefore, the communication rate performance and the resource utilization efficiency under the requirements of short time delay and low error rate are improved. Through the method, the short packet transmission rate of the system can be remarkably improved under the conditions of limited time delay and channel uncertainty, and the actual application requirements of the future Internet of Vehicles system for efficient, low-time-delay and high-reliability short packet communication are met.

The invention discloses a vehicle data wireless interaction system, and particularly relates to the technical field of wireless interaction, and the system comprises a node relation model building module which is used for building a node model through analyzing the data flow and logic relation among a vehicle terminal, a network side node, a cloud end and a data server end; the multi-source data collection module is used for collecting data from each node to form a multi-source data set and cleaning the multi-source data set; the initial abnormal node extraction module is used for screening out abnormal nodes by monitoring key indexes and calculating node abnormal indexes; the abnormity diagnosis module is used for positioning a fault reason by analyzing the abnormity characteristic incidence matrix; the system further comprises a diffusion risk assessment module which is used for providing decision support for network management by analyzing risk factors and propagation paths of nodes, calculating abnormal diffusion risk coefficients and generating a network risk report, has a self-adaptive optimization mechanism, continuously optimizes risk assessment and a decision tree model according to a disposal effect, and improves the reliability and safety of the system.

The invention discloses a control system for two-wheeled vehicle communication equipment, which relates to the technical field of communication equipment control and comprises a weak signal detection module, a signal fluctuation analysis module, a connection interruption evaluation module, a dynamic control optimization module and a self-adaptive adjustment module. The signal fluctuation analysis module is used for analyzing the network signal intensity fluctuation degree of the communication equipment in a weak signal environment based on the acquired network signal fluctuation information, and acquiring connection interruption risk information of the communication equipment in real time if the fluctuation degree exceeds a preset threshold value; and the connection interruption evaluation module is used for evaluating the connection interruption risk of the communication equipment under the condition of network signal fluctuation when the two-wheeled vehicle enters the weak signal environment. The problems of unstable connection, frequent disconnection and high power consumption of two-wheeled vehicle communication equipment in a weak signal environment are solved, intelligent detection, accurate evaluation, dynamic optimization and adaptive adjustment are realized, and the communication stability and the equipment cruising ability are improved.

Disclosed herein is a method of transmitting a BSM message of V2X communication device provided in a vehicle in an autonomous driving system. The method of transmitting a BSM message of a V2X communication device according to an embodiment of the present invention may multiplex sensor data and GPS data received from in-vehicle sensors, respectively, and use the multiplexed sensor data to generate BSM data. As a result, it is possible to enhance stability of the V2X system and more efficiently correct errors or the like occurring between sensors.An autonomous vehicle according to the present invention may be linked with an artificial intelligence module, a drone (unmanned aerial vehicle (UAV)), a robot, an augmented reality (AR) device, a virtual reality (VR) device, devices related to 5G services and the like.

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.