2869results about "Vehicle infrastructure communication" patented technology

The invention discloses an Internet of Vehicles network selection and switching decision system supporting access of multiple operators. According to the method and the device, the timeliness and the continuity of network switching are remarkably improved through prediction and planning in advance. The module depends on a trajectory-network matching prediction sub-module, combines a vehicle navigation trajectory and a global digital twinborn model, identifies a base station coverage area in a future driving path and the network service quality of each operator in advance, and avoids the lag problem that switching is triggered only after signals are weakened in traditional passive switching. The pre-switching resource reservation sub-module further sends a resource request to a target operator base station and confirms reservation before the vehicle enters a new road section, ensures that resources such as bandwidth and time slot required during switching are in place, reduces switching failure or interruption caused by resource competition, avoids common communication interruption after signal sudden drop in traditional switching, and improves the switching efficiency. And continuous transmission of key businesses such as automatic driving control instructions and real-time road conditions is ensured.

The invention discloses a dynamic beam adaptive optimization method for the Internet of Vehicles, and the method comprises the steps: firstly obtaining the current position, speed and driving direction of a vehicle, and carrying out the prediction of a future trajectory through combining historical trajectory information and employing a Kalman filtering technology and the like; and selecting a candidate beam direction from the predefined beam codebook. The system receives channel quality feedback information of a communication link in real time, and dynamically updates a beam forming weight based on an improved recursive least square algorithm. According to the algorithm, a composite adaptive adjustment mechanism of an error size and an error change trend is introduced, a multi-objective optimization function is constructed, and performance indexes such as link quality maximization, interference power minimization and beam switching frequency minimization are effectively balanced in a dynamic environment, so that an optimal beam direction is obtained. Finally, the real-time switching of the antenna array beams is realized through a low-overhead and low-delay control protocol. According to the method, track perception and a self-adaptive feedback mechanism are fused, and good real-time performance, stability and expandability are achieved.

The invention relates to the technical field of warehouse-in and warehouse-out management, in particular to a second-hand car warehouse-in and warehouse-out management system and method. The system comprises a vehicle RFID tag module, an intelligent electronic price tag module, a warehouse-in management module, a warehouse-out management module, an inventory monitoring and alarming module, a multi-point RFID positioning network module and an inventory map visualization module, and a unique encrypted RFID tag can be configured for each second-hand vehicle; binding an intelligent electronic price tag for each second-hand vehicle, and receiving verification to generate a verification result; collecting label information when the vehicle enters the parking lot; the label information is verified when the vehicle leaves; receiving the verification result of the intelligent electronic price tag and the vehicle in-library data of the database to lock the position of the abnormal vehicle; dense RFID reader nodes are arranged in the market to collect and output positioning data; and receiving the positioning data and the vehicle state information, generating a real-time inventory distribution map, and providing a scheduling and site planning decision basis for a manager. The scheduling and planning efficiency of managers can be improved.

The invention discloses a bus and station interactive scheduling method and system based on V2X and deep reinforcement learning decision, a medium and equipment, and the method comprises the steps: collecting bus operation dynamic data, station passenger flow and environment data in real time, and combining V2X network interaction information to construct a full-dimension perception system; a deep reinforcement learning model is adopted for dynamic decision making, intelligent closed-loop control of vehicle scheduling, station service and traffic signal cooperation is achieved, the bus punctuality rate is remarkably increased, the waiting time of passengers is shortened, operation safety is guaranteed through active early warning of abnormal conditions, and intelligent upgrading of a bus system from passive response to active prevention is achieved.

Disclosed in the present invention is a three-dimensional roadside sensor deployment method for collaborative sensing in the Internet of Vehicles. The method comprises: modeling an RSN model of a three-dimensional road environment; on the basis of the RSN model, constructing sensing detection functions for single sensors, and when there are a plurality of sensors providing coverage, constructing a global sensor detection function for each deployable area on the basis of the sensing detection functions for the single sensors; taking the maximization of the proportion of deployable areas that meets or exceeds a minimum detection probability as a first objective optimization problem function, which maximization is based on the global sensor detection function, taking the minimization of the total number of sensors in each deployable area as a second objective optimization problem function, and constructing a deployment optimization reward function consisting of the first objective optimization problem function and the second objective optimization problem function; and constructing a decision sequence, and on the basis of a decision transformer, solving the decision sequence to obtain a deployment policy, wherein a cumulative reward value is obtained by performing reward reallocation on the deployment optimization reward function by means of a two-layer optimization method. The present invention implements sensor deployment in a three-dimensional road environment.

The invention discloses a face recognition and encryption verification multi-grid article distribution control method and a distribution vehicle, and relates to the technical field of recognition and verification safety, and the method comprises the steps: fusing three-dimensional environment information through employing a vehicle-mounted edge calculation terminal and a navigation positioning terminal, and constructing a dynamic safety response strategy; the warehouse is analyzed through the vehicle-mounted warehouse control unified management terminal and the dynamic safety response strategy, and an edge intelligent control mechanism is obtained; processing based on a vehicle control and monitoring terminal and an edge intelligent control mechanism to obtain a real-time state; a vehicle interaction terminal and an intelligent operation terminal are utilized to obtain a face image and a visual dynamic feature, and a multi-modal detection strategy is utilized to analyze the face image and the visual dynamic feature to obtain a final authorized face image and a final authorized visual dynamic feature; and a dual-mode intelligent redundant unlocking technology is constructed, and face recognition and encryption verification are performed on the distribution vehicle based on the dual-mode intelligent redundant unlocking technology. According to the invention, compliance, high-efficiency and safe distribution of high-value materials is realized in an unmanned environment.

The invention provides a vehicle-mounted network security threat sensing system and method based on edge and cloud collaboration, and belongs to the technical field of vehicle network security. Comprising the following steps: S1, collecting vehicle multi-modal data through an edge layer; s2, preprocessing the multi-modal data of the vehicle; s3, performing feature extraction on the preprocessed vehicle multi-modal data to obtain a high-dimensional feature vector; s4, calculating a threat confidence score based on the high-dimensional feature vector; s5, performing hierarchical response decision based on the threat confidence score and a predefined response strategy library, and generating hierarchical response data; s6, extracting threat metadata based on the hierarchical response data; s7, performing global association analysis based on the threat metadata, and upgrading a defense system; and S8, based on the global threat intelligence and the upgraded defense system, carrying out edge layer updating, and then skipping to S1. The method is beneficial to eliminating response delay, relieving network bandwidth pressure and optimizing cloud computing resource load.

A system includes a distributed ledger storing one or more smart contracts; one or more 5G small cells, each having one or more antennas mounted on a housing, each small cell sending packets of data trackable with the distributed ledger; and a processor to control a directionality of the antennas in communication with a predetermined target using 5G protocols.

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 traffic detection, in particular to a traffic abnormal event cooperative detection method and system based on vehicle-road cooperation, and the method comprises the steps: collecting vehicle end data and road end data from a vehicle end and a road end respectively, and carrying out the timestamp alignment, coordinate transformation, noise filtering and missing value supplementation of the vehicle end data and the road end data; establishing target state estimation based on a state space motion model, performing recursive estimation on a target, and identifying abnormal candidates based on observation residual errors; constructing a space-time diagram based on the vehicle end data and the road end data, reconstructing node features by adopting a time sequence diagram neural network, generating an anomaly score according to a reconstruction error, and outputting an anomaly candidate; and according to the state space motion model and the anomaly candidates of the time sequence diagram neural network, confidence fusion is carried out according to confidence, and whether an anomaly alarm is triggered and whether an anomaly type and positioning information are output are judged based on a fusion result. And through confidence fusion, false alarms triggered by isolated noise can be effectively suppressed.

The invention relates to a multi-type emergency vehicle signal dynamic priority control method based on vehicle-road cloud cooperation, and belongs to the technical field of intelligent traffic control. The method comprises the following steps: acquiring an emergency vehicle state and traffic environment data in real time through cooperation of a vehicle-mounted terminal, roadside equipment and a cloud control platform; the cloud control platform dynamically calculates priority scores based on vehicle types, task emergency degrees, predicted arrival time, real-time traffic influences and path complexity multi-dimensional factors; when multiple vehicles have conflicts, collaborative decision making is carried out based on scores; and finally, an optimized signal control strategy is generated and executed. The system effectively solves the problems that a traditional priority control mode is extensive, and traffic jam and multi-vehicle conflicts are easily caused, achieves the purpose that interference to social traffic is minimized while efficient passing of emergency vehicles is guaranteed, and improves the overall efficiency and safety of an urban traffic system.

A method for managing spectrum allocation in a heterogeneous wireless communication network includes employing artificial intelligence (AI) to predict interference between users and services; andadjusting spectrum allocations for users and services in the network based on the interference predictions to minimize interference.

The invention discloses an engineering vehicle safety simulation and prediction system based on digital twinning, and the system comprises a data collection layer which collects multi-source heterogeneous data in real time; in the knowledge graph layer, a streaming inference engine is constructed based on an Apache Jena graph database, and an entity-relationship-attribute triple dynamic graph structure is adopted; according to the AI model layer, a physical rule serves as a loss function constraint term to be embedded into a neural network through a physical information neural network, a digital organ model concept is combined to split a vehicle into key organs for heterogeneous modeling, a simplified physical model is adopted in the core physical process, and an LSTM-AI model is adopted in external behaviors; the explanatory analysis layer is used for integrating an SHAP / LIME explanatory tool to output a visual evidence chain during fault prediction, and deploying an online incremental learning framework to allow the model to learn from new data and dynamically adjust normal range definition; and the visualization and application layer is used for performing three-dimensional visualization rendering based on WebGL or Three.js, and ensuring data transmission security through block chain evidence storage and end-to-end encryption.

Embodiments are provided wherein a mobile device that is associated with a cellular system is configured to communicate with a second device that is associated with the cellular system subject to a time-varying, dispersive, multipath fading environment. The mobile device comprises an antenna system comprising a plurality of polarizations and a processor that is configured to reduce a cross-polarization interference caused by said time-varying, dispersive, multipath fading environment while the mobile device uses a first polarization and a second polarization of the plurality of polarizations to increase a transmission throughput of information and / or a reception throughput of information. The mobile device is further configured to perform operations including processing first and second signals received respectively on the first and second polarizations and to use a plurality of receive channel coefficients to perform first and second mathematical operations on the first and second signal, respectively.

The invention discloses an intelligent chassis preview control system and method based on a roadside sensor, and the system comprises a roadside sensing unit which is used for obtaining the road condition information of a line road, and transmitting the road condition information to a roadside communication base station; the roadside communication base station is used for preprocessing the road condition information and packaging the road condition information into a set format; the vehicle-mounted communication module is used for sending the road condition information with the set format to the vehicle-mounted controller; the vehicle-mounted controller is used for performing fusion processing on the road condition information and the vehicle state data, and analyzing the fused data by adopting a preview control algorithm model to generate a control instruction of the intelligent chassis executing mechanism; and the intelligent chassis executing mechanism is used for performing preview control on the vehicle chassis based on the control instruction. The method has the advantages that pre-judgment adjustment of the vehicle chassis is achieved, the safety and stability of vehicle driving are effectively improved, and the system reliability is enhanced especially under complex road conditions and severe weather conditions.

The invention discloses a heterogeneous Internet of Vehicles network selection method based on fuzzy logic and a related device. The method comprises the following steps: acquiring network attributes of candidate networks; inputting the network attribute of each candidate network into a membership function to obtain input membership of low, medium and high levels of the network attribute; performing fuzzy reasoning on the input membership degrees of the low, middle and high levels of the network attribute to obtain an aggregation membership degree of the output level of the network attribute; performing defuzzification on the aggregation membership degree of the output level of the network attribute to obtain a network evaluation value of each candidate network; according to the method and the related device, the network selection problem of a V2I communication scene can be solved, the optimal network can be selected, and efficient information transmission with road infrastructures is realized.

The invention discloses an OTA parallel downloading method and system based on a domain controller, and the method comprises the steps: an OTA cloud platform judges whether a vehicle has an upgradable task or not according to the information reported by the vehicle; if the upgradable task exists, the OTA cloud platform feeds back upgrading task information to the OTA upgrading controller; the OTA upgrading controller informs each domain controller of upgrading task information; and after each domain controller obtains the upgrade file downloading link, synchronously downloading the upgrade package file of the lower hanging node corresponding to each domain controller in parallel. According to the OTA parallel downloading method and system based on the domain controller and the domain controller, the upgrade package of the node ECU hung below the domain controller can be directly downloaded from the OTA cloud platform, so that the number of times of transmission of the upgrade package at a vehicle end is reduced, the overall time of downloading and distributing an upgrade package file is shortened, meanwhile, the probability of occurrence of a data transmission problem is reduced, and the reliability of an OTA system is improved.

The present disclosure is related to Intelligent Transport Systems (ITS), and in particular, to service dissemination basic services (SDBS) and / or collective perception service (CPS) of an ITS Station (ITS-S). Implementations of how the SDBS and / or CPS is arranged within the facilities layer of an ITS-S, different conditions for service dissemination messages (SDMs) and / or collective perception message (CPM) dissemination, and format and coding rules of the SDM / CPS generation are provided.

The invention discloses a multimedia content preloading method based on vehicle cloud collaboration, and particularly relates to the technical field of multimedia content preloading, and the method comprises the steps: carrying out the modeling through a multi-dimensional space-time driving path, and combining with historical road communication coverage features; a network availability prediction result and a user historical consumption mode are deeply mapped, and a weight fusion mechanism of content timeliness, capacity characteristics and scene correlation is introduced, so that a generated multi-level priority sequence better meets the instant requirements of vehicles at different time and different positions; through dynamic comparison of an initial plan, a real-time path, a network and user interaction data, a superposition out-of-control situation of prediction deviation can be rapidly identified, a scheduling priority offset mode under multiple tasks is identified in combination with vehicle calculation, caching and bandwidth occupation states, and then a pre-loading dislocation cooperative imbalance fault network is constructed through bidirectional correlation analysis. And visual diagnosis and quantitative evaluation of the imbalance state of the prediction layer and the execution layer are realized.

The invention relates to the technical field of data interaction, in particular to a data interaction method and system for a shipborne intelligent terminal and a port control system. Acquiring an electromagnetic interference signal of the current communication frequency band through an electromagnetic interference monitoring device configured by the shipborne intelligent terminal; extracting interference characteristic data of the electromagnetic interference signal and determining a final interference type according to the interference characteristic data and the interference characteristic template; querying and determining an anti-interference target frequency band from a dynamic spectrum database according to the final interference type; and switching the shipborne intelligent terminal from the current communication frequency band to the anti-interference target frequency band, retransmitting the data with abnormal data interaction in the current communication frequency band through the anti-interference target frequency band, and continuing to perform subsequent data interaction with the port control system. And the reliability of ship-port data interaction in a complex electromagnetic environment is effectively improved.

The invention discloses a vehicle path planning interaction method based on vehicle-road cloud collaboration, and the main conception is that a communication link is decomposed into multiple paths of signals, the signals are dynamically switched according to real-time signal quality, seamless degradation is realized, and based on a three-stage elastic architecture of cloud global planning, marginal layer region collaboration and vehicle end local execution, the vehicle path planning interaction method based on the marginal layer region collaboration is realized. A communication degradation control and computing power resource dynamic allocation strategy is utilized to avoid contradiction generated by communication delay and computing power allocation; besides, based on a vehicle-road cloud collaborative architecture and in combination with a space-time consistency guarantee scheme, on the premise of ensuring that the vehicle end can autonomously change a lane during communication interruption, the safety problems that the deviation between the vehicle end autonomous decision and a global path is too large and rapid correction is difficult after communication recovery are solved. According to the invention, aiming at the specific working condition that the communication link is unstable and the computing power resource is limited, the cost benefit of the automatic driving algorithm is obviously optimized, and the driving safety is effectively improved.

The invention discloses a multi-agent reinforcement learning multi-task agent decision-making method and system for a crossroad without signal lamps, and belongs to the technical field of intelligent network connection vehicle multi-task agent decision-making control. According to the method, vehicle-road cooperation is achieved through wireless communication, an automatic driving vehicle uploads state and task data to a road side unit, the road side unit adopts a multi-task mixed attention mechanism, data are processed through a self-attention module and a cross attention module, and decision features are generated and input into a strategy network. And finally generating a longitudinal control instruction by combining an offline and online mixed sampling strategy training model, and issuing and executing the longitudinal control instruction through wireless communication. According to the method, the sensing range can be expanded, the key target can be dynamically focused, the training efficiency and the decision-making stability are improved, and the collaborative decision-making capability and the traffic safety of the automatic driving vehicle in the mixed traffic environment are remarkably enhanced.

The invention provides a surrounding vehicle track prediction method and system based on an attention mechanism in a network connection environment, and the method comprises the steps: constructing a vehicle network which comprises a road side unit and a vehicle; cooperative driving data of the target vehicle and surrounding vehicles are obtained through a road side unit and a sensor on the vehicle; the driving data is preprocessed; inputting the preprocessed driving data into the trained Transform trajectory prediction model based on the attention mechanism to obtain a trajectory prediction result of the surrounding vehicles; calculating a root-mean-square error between the trajectory prediction result and the actual running trajectory of the vehicle; constructing a vehicle collaborative decision according to the trajectory prediction result and the root-mean-square error, and controlling the operation of the vehicle based on the vehicle collaborative decision; according to the invention, an attention mechanism is adopted to effectively capture a complex dynamic interaction relationship between vehicles, and the precision of trajectory prediction is remarkably improved.

The present application relates to the technical field of intelligent traffic control, and particularly relates to a rescue emergency light linkage control method combined with a vehicle system, comprising: S1: environment perception, collecting road topological structure, potential congestion obstacle distribution, visibility level and vehicle density value in real time through the vehicle system of a rescue vehicle; S2: dynamic decision, generating an emergency light control instruction based on the output of S1; S3: multi-vehicle linkage, predicting a future path based on the position and historical trajectory of the rescue vehicle, and broadcasting a V2X instruction containing real-time coordinates, path vector and avoidance direction identifier to vehicles within a preset radius; S4: closed-loop execution, issuing the instruction of S2 to a light control unit, and capturing an actual flashing sequence through a camera for time domain matching verification, and triggering instruction retransmission when the matching degree is lower than a reliability threshold. The method not only improves traffic safety and rescue efficiency, but also enhances the intelligence and reliability of the system, and adapts to modern rescue needs.

The invention relates to the technical field of intelligent traffic, in particular to a tunnel emergency parking strip safety guiding method and system. The method comprises the following steps: the smart workstation supports a unified vehicle-road cooperation communication protocol, so that multi-dimensional data acquired by a radar, a range finder, a telephoto camera, a snapshot camera, a vehicle-road cooperation terminal and a vehicle-mounted OBU device in real time are all sent to the smart workstation; dynamically predicting the average vehicle speed, the traffic flow and the vehicle density of the emergency parking strip and the adjacent lanes of the parking strip through a machine learning algorithm; according to a preset driving habit model, the real-time multi-dimensional data and the historical traffic data, identifying a rear-end collision risk, a side collision risk and a lane change conflict risk; and generating an information board control strategy according to the dynamic prediction result and the risk identification result. According to the tunnel emergency parking strip safety guiding method and system, the information board control strategy is generated, and it is ensured that the vehicle can safely and efficiently drive away from the emergency parking strip.

The invention belongs to the technical field of traffic control, and provides a micro-excitation-triggered multi-mode road adhesion coefficient real-time detection method and system, and the method comprises the steps: 1, carrying out the excitation triggering condition judgment; 2, generating a multi-mode excitation signal; 3, collecting dynamic response data; 4, feature fusion and attachment coefficient calculation; and step 5, integrating output and control. By applying high-frequency and low-amplitude micro-torque pulses, dynamic response of the tire is actively excited, and dependence on natural slippage is avoided; tire deformation, driving shaft torque attenuation and suspension vibration frequency spectrums are synchronously collected, and multi-dimensional characteristics of mechanics, acoustics and kinematics are covered; a grouping convolutional neural network and an attention mechanism are adopted to realize efficient feature extraction and fusion, and the bottleneck of traditional road adhesion coefficient detection is broken through.

The invention provides an intelligent network connection device remote cluster control method and system based on vehicle-road cooperation, and relates to the technical field of intelligent traffic, and the method comprises the steps: obtaining vehicle end state information and road side sensing information, carrying out the cross-domain fusion based on a vehicle-road space-time alignment rule to obtain a global view, grouping vehicle end devices according to the road segment attribution and task association degree, and carrying out the clustering of the vehicle end devices; and generating a hierarchical control instruction according to the grouping structure and the global view, executing remote control, and adaptively adjusting an alignment rule by using feedback information. According to the invention, the accurate control of the equipment cluster in the vehicle-road cooperation environment is realized, and the operation efficiency and safety of the vehicle-road cooperation system are improved.

The invention discloses a vehicle state sensing method and system in a vehicle-road cooperation environment, and relates to the technical field of intelligent traffic, and the method comprises the steps: collecting the real-time traffic data of a target intersection; establishing a temporary V2X communication link; receiving real-time traffic data and K pieces of real-time vehicle state information of K running vehicles; if the one-dimensional risk detection results of the roadside edge computing device on the real-time traffic data and the K pieces of real-time vehicle state information are set to be 0, compensating the K pieces of real-time vehicle state information by adopting the real-time traffic data to obtain K pieces of compensated vehicle state information, constructing a global dynamic traffic model, and performing track conflict prediction and potential collision risk positioning; and according to the risk avoidance priority, broadcasting the potential collision risk to K running vehicles through a V2X communication link. The technical problems of inaccurate traffic modeling and untimely collision risk identification caused by incomplete vehicle-mounted sensing information in the prior art are solved, and the technical effects of improving the vehicle state sensing accuracy and the collision risk early warning timeliness are achieved.

The invention relates to the technical field of vehicles, in particular to a scene perception-based vehicle-road cloud cooperative positioning power consumption optimization method and related equipment, and the method comprises the steps that a vehicle end forms a vehicle end feature vector through collected vehicle end perception data and then sends the vehicle end feature vector to a cloud end, and a field end forms a road end feature vector through collected field end perception data and then sends the road end feature vector to the cloud end; the cloud terminal identifies the current scene type of the vehicle according to the received vehicle terminal feature vector and the road terminal feature vector, and sends a positioning mode switching instruction to the vehicle terminal according to the identified scene type so as to instruct the vehicle terminal to be switched to a positioning mode corresponding to the current scene type; after receiving the positioning mode switching instruction, the vehicle end controls the working state of each sensor of the vehicle end according to the positioning mode switching instruction so as to realize power consumption optimization; the power consumption of the vehicle-mounted equipment can be reduced while the positioning requirement is met, and the cruising ability of the vehicle is improved.

The invention relates to the field of intelligent traffic systems, in particular to an intelligent intersection multi-vehicle cooperative control method. The method comprises the following steps: S1, sensing a vehicle state and sharing vehicle node information by adopting a dual-mode communication mechanism; s2, fusing state data at edge computing nodes; establishing a dynamic traffic model according to the fused state data; s3, acquiring node data of the edge calculation node, calculating a vehicle passing priority index according to the node data, and performing vehicle priority decision according to the passing priority index; s4, performing grading processing on the vehicles with the overlapped tracks; s5, the RSU device broadcasts the control instruction, and if the vehicle-mounted unit receives the control instruction within the set time, an execution report is transmitted back to the RSU device; and if the vehicle-mounted unit does not receive the control instruction within the set time, a redundancy execution mechanism is started to detect and control the running state of the vehicle. According to the invention, millisecond-level synchronous transmission and high-precision perception of information are realized, and the real-time performance and accuracy of traffic environment modeling are improved.