9450results about "Vehicle infrastructure communication" patented technology

A system disclosed herein applies a parameterized sensing and localization error model for use by connected devices to improve cooperative sensor fusion by estimating errors using key prediction factors (e.g., distance and velocity) at different tiers. The parameterized sensing and localization error model implements a local sensor fusion process for fusing measurements from sensors of a connected device and a global sensor fusion process for fusing measurements from many connected devices. The local sensor fusion process generates a covariance matrix that incorporates a measured distance and the global sensor fusion process generates a covariance matrix that incorporates a measured velocity combined with the local sensor fusion result. Results show an average improvement of 1.42× in RMSE vs. a typical fixed error model on a 1 / 10 scale test-bed.

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.

A method of wireless communication at a wireless repeater device controlled by a base station in a wireless communication network includes receiving, from the base station, control signaling comprising control information, in association with traffic relayed through the repeater device between a first wireless communication device and a second wireless communication device; and configuring a relay unit of the repeater device to communicate the traffic between the first wireless communication device and the second wireless communication device using at least one of: the time division duplex state, the fronthaul-link TCI state index, the access-link TCI state index, or the time domain resource allocation.

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.

Disclosed is a method for using deep reinforcement learning to optimize dependent task offloading in the Internet of Vehicles. The steps of the present invention comprise: S1, constructing a vehicle system network; S2, constructing a task model for an application; S3, constructing a task load model: on the basis of the vehicle network system and the task model, calculating time delay, power consumption, and paid incentive rewards for each of the following three offloading modes: local computing, offloading to a nearby vehicle, and offloading to a nearby roadside unit; S4, determining task priorities: first, on the basis of the allocation of predecessor nodes of sub-tasks in combination with a dynamic network environment, determining the priority of each sub-task, and then carrying out scheduling on the basis of a multi-queue algorithm; and S5, searching for an optimal offloading strategy by means of deep reinforcement learning. The method of the present invention does not require excessive prior knowledge, has good reusability in similar application scenarios, and has high practical value.

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 relates to the technical field of traffic safety management, in particular to a vehicle abnormal driving recognition and early warning method and system. The method comprises the following steps: firstly, acquiring vehicle position, speed, direction and driver operation data in real time, then correcting time sequence data by adopting a smooth denoising method of adaptive segmented clustering, generating an information confirmation code through standardization, and then carrying out weighted fusion on multi-source data to obtain a multi-source data fusion model; the strategy decision-making module is used for extracting vehicle speed change rate, lane deviation degree and driver behavior characteristic key indexes, identifying and grading abnormal driving behaviors by setting a threshold value, calculating a comprehensive abnormal score, and generating a grading intervention strategy for slight, medium and serious anomalies according to a comparison result of the abnormal score and the set threshold value. And finally, combining automatic intervention in the vehicle with global coordinated intervention of a road manager. According to the invention, the intervention strategy is generated based on multi-source data acquisition, processing, analysis and decision, so that the vehicle driving safety and the road management efficiency are improved.

The invention relates to a communication computing resource allocation and vehicle digital twinning deployment method in the Internet of Vehicles, and belongs to the technical field of mobile communication. The method comprises the following steps: S1, constructing a digital twin-assisted Internet of Vehicles communication, inductance and calculation integrated network; s2, designing a vehicle dynamic frame based on the sensing integration technology; s3, constructing a utility evaluation function of the vehicle perception data, and solving perception information utility; s4, constructing a vehicle digital twinborn deployment strategy, and calculating vehicle digital twinborn deployment cost and migration effectiveness; and S5, establishing an optimization model for minimizing the perceptual data information age and the vehicle digital twinborn deployment and migration cost, and solving an optimal vehicle sensing calculation resource allocation and vehicle digital twinborn deployment scheme by adopting multi-agent deep reinforcement learning. Through the flexible dynamic frame design and the vehicle digital twinborn deployment strategy, the average information age of the sensing data and the digital twinborn migration rate are reduced, and the sensing data effectiveness and the edge computing resource load balance are effectively improved.

The invention discloses an Internet of Vehicles multi-parameter monitoring method and system based on 5G multi-access edge computing. The method comprises the following steps: collecting multi-source heterogeneous data in real time by using a vehicle-mounted sensor; transmitting the collected multi-source heterogeneous data to the nearest edge computing node in real time by using a 5G network; preprocessing the multi-source heterogeneous data at an edge computing node; inputting the fused data into a local model deployed on an edge node, and carrying out real-time monitoring and real-time evaluation on a vehicle state and a driver state; the obtained evaluation result is uploaded to a cloud platform, and the cloud platform dynamically adjusts a driving assistance decision and a control instruction according to the vehicle driving working condition and the environment data to generate an optimal decision result; and issuing the optimal decision result to a vehicle-mounted execution unit through a 5G network. According to the method, key parameters such as the vehicle speed and the physiological state of a driver are monitored, and the real-time performance and accuracy of a monitoring system are improved.

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.

Methods, computer-readable media, systems, and / or apparatuses for evaluating movement data to identify a user as a driver or non-driver passenger are provided. In some examples, movement data may be received from a mobile device of a user. The movement data may include sensor data including location data, such as global positioning system (GPS) data, accelerometer and / or gyroscope data, and the like. Additional data may be retrieved from one or more other sources. For instance, additional data such as usage of applications on the mobile device, public transportation schedules and routes, image data, vehicle operation data, and the like, may be received and analyzed with the movement data to determine whether the user of the mobile device was a driver or non-driver passenger of the vehicle. Based on the determination, the data may be deleted in some examples or may be further processed to generate one or more outputs.

A system (1100) and method (1200) for connecting a mobile device to a CVD of a known vehicle with an unknown connection protocol. The system (1100) comprises a mobile device (110) for a vehicle (1000), a connected vehicle device (135) and a server (11). The mobile device (110) is configured to select between an inquiry to the server (11) for a connection protocol, availability based on a detection of a broadcast SSID, or based on a locally determined best connection procedure.

Various embodiments of a system and method for a smart work zone are described. In one example, the system includes smart cone devices configured to transmit smart cone device localization data, a wearable device comprising a processor configured to transmit worker localization data of a worker wearing the garment device and to receive an alert, and a base station. The base station includes an internal edge computing system configured to process the smart cone device localization data to define a virtual geofence boundary of a safe area, process the worker localization data, broadcast a location of the worker wearing the wearable device, and in response to a vehicle or the worker approaching the virtual geofence boundary, provide an alert to at least one of: the worker wearing the wearable device, a passing motorist, or a connected and automated vehicle (CAV).

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.

A system comprising a capture device and a database. The capture device may be configured to capture video, perform video analysis on the captured video to identify characteristics about objects in the captured video and determine an absolute location of the objects, store metadata separate from the captured video in response to the video analysis and communicate with a wireless communication device. The database may be configured to communicate with the wireless communication device, store the metadata received from the wireless communication device, re-create a scene in response to the characteristics and absolute location of the objects in the metadata and generate an animation based on the scene using the metadata received over time. The metadata may be communicated without the captured video. The animation may provide a visualization similar to the captured video using only the metadata.

It is disclosed a vehicle transportation safety monitoring system based on driver holographic management. In the system, a driver identity verification unit reads driver certificate information and / or collects driver biometric information, and compares the information with biometric information corresponding to authorized driver identity information to complete driver identity verification, a vehicle control unit determines, according to an identity verification result, whether a vehicle can be started; emotion recognition and / or behavioral intelligent analysis and / or sensor monitoring data analysis and recognition is performed, and when the result triggers a warning, an operation such as automatic speed control is performed to control the movement of the vehicle. This system can bind a driver identity with a driving permission of a designated operating vehicle, monitors a driver emotion, a driving behavior, and a fatigue status, and controls a vehicle driving status and / or sends a safety warning to a driver according to a monitoring result.

Systems and methods for automating the management of a warehouse are disclosed. The method includes monitoring. in real-time, a plurality of items in the warehouse, a plurality of tasks associated with the warehouse, and / or incoming vehicles to the warehouse. The method also includes receiving, via a plurality of sensors, image data and / or video data associated with the plurality of items, the plurality of tasks, and / or the incoming vehicles. The method further includes inputting the image data and / or the video data into a machine learning model to automate the management of the warehouse, wherein the machine learning model has been trained based on training data to generate predictions for the inputted data, and wherein the machine learning model utilizes the predictions for the inputted data for retraining to generate predictions for newly inputted data and improve accuracy of error handling.

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.

A method implemented by first user equipment (UE) include transmitting a first sidelink position reference signal (SL Pos-RS) to a second UE at a first time (t1), receiving a second SL Pos-RS from the second UE at a fourth time (t4), receiving a timing measurement report from the second UE, wherein the timing measurement report contains information regarding a second time (t2) when the first SL Pos-RS was received by the second UE and regarding a third time (t3) when the second UE transmitted the second SL Pos-RS to the first UE, and calculating a distance between the first UE and the second UE based on the first time, the fourth time, and the timing measurement report.

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.