14791results about "Vehicle wireless communication service" patented technology

Aspects presented herein relate to perception data collection and curation. In one aspect, a network entity receives, from a first set of user equipments (UEs), a first set of perception data collected by the first set of UEs. The network entity configures, for a second set of UEs based on the first set of perception data, at least one of: a set of planned routes and time plans for collecting a second set of perception data or a set of embeddings centroids. The network entity receives, from the second set of UEs, the second set of perception data based on at least one of the configured set of planned routes and time plans or the configured set of embeddings centroids.

A system receives vehicle metric data from a gateway device connected to a vehicle via a network connection. In a first power mode, the vehicle gateway device gathers vehicle metric data related to operation of the vehicle and / or location data and sends requested vehicle metric data to the system. Based on a lack of input, the vehicle gateway device enters a standby power mode to reduce the power consumption of the vehicle gateway device. The vehicle gateway device reduces the power consumption by reducing activity of the vehicle gateway device. Instead of reinitiating a network connection periodically to receive requests for vehicle metric data from the system, the vehicle gateway device maintains the network connection by sending keep alive messages between the vehicle gateway device and the system. The system uses the maintained network connection to request vehicle metric data.

The invention relates to the technical field of communication optimization, in particular to a wireless communication method and system of a vehicle diagnosis system. The method comprises the following steps: identifying a plurality of signal sources in an environment and three-dimensional space data of a current position of a vehicle; performing scene interference intensity comprehensive evaluation on the plurality of signal sources in the environment to obtain a current scene interference intensity evaluation value of the vehicle; environment obstacle space layout analysis is carried out on the three-dimensional space data of the current position of the vehicle, environment three-dimensional modeling is carried out, and a vehicle environment three-dimensional point cloud model is constructed; signal multi-path propagation evolution is carried out according to the current scene interference intensity evaluation value of the vehicle and the vehicle environment three-dimensional point cloud model, and optimal signal frequency band analysis is carried out, so that optimal signal frequency band parameters are extracted; and carrying out dynamic transmission frequency modulation and signal adaptive coding based on the optimal signal frequency band parameter, and constructing a dynamic signal transmission strategy. According to the invention, efficient and stable wireless communication is realized, and the vehicle-mounted communication performance is improved.

A map updating method and apparatus, and an autonomous moving apparatus and a storage medium, which relate to the technical field of autonomous navigation. The method comprises: acquiring a pre-constructed global map, a semantic map, the pose of an autonomous moving apparatus, and sensor data, wherein a static region and a dynamic region in the global map are labeled in the semantic map (S110); on the basis of the pose and the sensor data, periodically constructing a local map, so as to define the coverage range of the local map (S120); aligning the global map with the local map, so as to obtain a target local map aligned with the global map (S130); and on the basis of the global map, the semantic map and the target local map, updating the global map (S140). The consistency of a global map can be ensured, the quality of the global map is improved, and the efficiency and accuracy of updating a map are improved.

The invention relates to the technical field of traffic management, in particular to a smart city intelligent traffic management system based on big data, which comprises a multi-source data acquisition module, a data fusion module, a dynamic feature generation module, a situation prediction module, a multi-target optimization module and an instruction execution module, the multi-source data acquisition module acquires multi-source data in real time; the data fusion module generates a standardized traffic data flow and outputs a three-dimensional data cube; the dynamic feature generation module outputs a traffic feature matrix; the situation prediction module generates a dynamic situation map; the multi-objective optimization module outputs an optimization instruction set; and the instruction execution module issues a control instruction to the traffic signal controller, the variable information board and the vehicle-mounted terminal. According to the invention, traffic jam can be relieved, road traffic efficiency can be improved, bus priority scheduling can be optimized, vehicle delay can be reduced, and emergency response speed can be improved.

A system for timing or zone alert determination from vehicle events and / or an accident database includes an interface and a processor. The interface is configured to receive a location of a translating object. The processor is configured to trigger a warning to the translating object based at least in part on its location and the existence of an associated hazard.

A method for providing a sensing service, performed by a user equipment (UE), includes: sending a sensing request to an access management function (AMF); receiving sensing parameters returned by a target sensing function (SF) selected by the AMF; and providing the sensing service based on the sensing parameters.

The invention relates to the technical field of traffic flow regulation and control, and discloses an electronic information traffic flow automatic regulation and control system based on a wireless sensor network. The multi-source sensing module collects road network multi-dimensional traffic data through a wireless sensing network, and traffic situation characteristics are generated through a specific data fusion method. The spatio-temporal feature fusion module utilizes a spatio-temporal attention mechanism to associate cross-modal features, and the collaborative decision-making module inputs fusion features into a pre-trained distributed decision-making model to generate a regulation and control instruction set. The dynamic game optimization module constructs a multi-target game optimization model, and traffic signal parameters are optimized by adopting a dynamic game strategy decomposition algorithm. And the hierarchical execution module executes regulation and control instructions in a distributed manner through a three-level control architecture of a central decision-making layer, a regional coordination layer and an intersection execution layer. The system can comprehensively collect and fuse traffic data, realizes scientific decision making and accurate regulation and control, effectively improves the road passing efficiency, balances the road network load, and relieves traffic jam.

A computer system can remotely manage a vehicle's operational settings. The computer system can cause operational settings data to be downloaded to a vehicle to control one or more operations of the vehicle. The computer system can generate download event information indicating the operational settings data downloaded to the vehicle and a user associated with the vehicle. The computer system can access a pricing structure associated with operational settings. The computer system can charge the user based on the pricing structure and the download event information.

The invention provides a distributed AGV system traffic control and scheduling method realized based on ROS2, and the method comprises the steps: building AGV vehicle nodes, building a traffic control module, developing a scheduling module, building a communication network, integrating a sensor data processing module, providing a central control board / man-machine interaction interface, and building a database / log module. According to the method, efficient data interaction and collaborative operation between AGVs are realized by using a distributed communication framework of ROS2, and by constructing a traffic control module and an optimized scheduling algorithm, the position and state of each AGV can be monitored in real time, the running path of each AGV can be dynamically adjusted, and traffic congestion and collision events are avoided. Compared with a traditional centralized control system, the system has the advantages that the expandability and fault-tolerant capability of the system are improved, the system is suitable for a complex and changeable industrial logistics environment, and the efficiency and reliability of logistics transportation are remarkably improved.

An apparatus comprising an interface and a processor. The interface may be configured to receive data about an interior of a vehicle. The processor may be configured to process the data, determine a number of occupants in the vehicle in response to an analysis of the data and generate a counter signal comprising the number of the occupants in the vehicle. The counter signal may be generated in a format compatible with a toll interface of a toll tag. The toll tag may be implemented in the vehicle. The toll tag may be configured to generate a readable occupant count in response to the counter signal. The readable occupant count may be configured to be scanned by an automatic roadway system.

Methods, systems, and devices for leveraging automotive and wearable-based data are described. For example, a system may include a wearable device, a user device associated with the wearable device, and a vehicle. The system may determine when a user is within a proximity of the vehicle based on communications between the vehicle and the wearable device, between the vehicle and the user device, or both. The system may retrieve physiological data measurement from the user via the wearable device based on determining the user is positioned in a proximity of the vehicle, such that one or more operational parameters of the vehicle may be adjusted based on the physiological data. Additionally, or alternatively, the system may receive telemetry data from one more sensors of the vehicle and may provide feedback to the user based on the telemetry data satisfying one or more trigger conditions.

The invention discloses a traffic network multi-mode perception abnormal event early warning method and system, and relates to the technical field of data processing, and the method comprises the steps: obtaining a path accident frequent occurrence data set of a target traffic network, constructing an abnormal event prototype set, obtaining the vehicle state information of a target vehicle based on V2X communication, and obtaining the abnormal event prototype set; probability matching is carried out in combination with the multi-modal sensing data, and a target abnormal event prototype is determined; and calling a historical abnormal event trigger feature set to perform feature enhancement, and performing real-time abnormal event early warning on the driving process of the target vehicle by taking the enhanced abnormal event trigger feature as an early warning condition. According to the invention, the technical problems of incomplete traffic abnormal event detection and early warning delay caused by dependence on a single data source and lack of real-time dynamic response capability in the prior art are solved, and the technical effects of comprehensive and real-time traffic abnormal event detection and dynamic early warning are achieved through multi-modal sensing and V2X communication technologies.

An intelligent reflecting surface assistance-based task unloading and resource allocation method for an unmanned aerial vehicle mobile edge computing network system, comprising: constructing an intelligent reflecting surface-assisted unmanned aerial vehicle mobile edge computing network system model; constructing an intelligent reflecting surface-assisted unmanned aerial vehicle mobile edge computing network communication model ; according to the communication model, obtaining a time delay model and an energy consumption model; establishing a multivariable joint optimization model so as to minimize the energy consumption of an unmanned aerial vehicle; and using a deep reinforcement learning method to solve the objective optimization model. By means of intelligent reflecting surface assistance, joint optimized unloading decision and resource allocation, the present application effectively reduces the energy consumption of the unmanned aerial vehicle and improves the performance of the unmanned aerial vehicle network system.

A user device, UE, for a wireless communication system, like a 3rd Generation Partnership Project, 3GPP, system is described. The UE is to communicate using a sidelink, SL, using resources allocated by a base station of the wireless communication system. The UE is configured or preconfigured with at least one first SL resource pool including resources in an unlicensed spectrum. The UE is to provide to the base station occupancy information regarding the unlicensed spectrum.

The invention relates to a digital twinning dynamic deployment and migration optimization method based on vehicle-infrastructure collaborative awareness, and belongs to the technical field of Internet of Vehicles and digital twinning. The method comprises the following steps: constructing a digital twin network oriented to the Internet of Vehicles; a three-party utility model driven by heterogeneous sensing resources is constructed, and sensing quality, task completion time and node energy consumption are comprehensively considered; the sensing area overlapping degree of the vehicle and the road side unit is dynamically quantified, and the data acquisition redundancy rate is reduced in combination with a preset threshold value; a parallel pre-migration mechanism is designed according to the computing load, the storage capacity and the communication resource state of the edge server, and independent fragmentation transmission of the digital twin model is achieved through a multi-path concurrent transmission strategy; establishing a joint optimization model with maximized comprehensive utility; a multi-agent near-end strategy optimization algorithm is adopted to realize collaborative optimization of a digital twin deployment strategy, migration path selection and perception task distribution. According to the invention, the real-time performance and reliability of the digital twinning service in the Internet of Vehicles scene are improved.

The invention discloses a method and a system for evaluating the running risk of an automatic driving taxi, and relates to the field of intelligent traffic and automatic driving. Aiming at the problems of insufficient dynamic traffic environment adaptability, rigidity of space decision, lack of vehicle operation rule compatibility and the like in the prior art, the evaluation method comprises the following steps: dynamically dividing an urban road network into hexagonal honeycomb units, adjusting the honeycomb radius according to a real-time travel demand, and selecting key nodes; accessing traffic flow density, conflict event number, communication delay and environment parameters in real time to construct a key node risk index; a hidden Markov chain model is adopted to simulate behavior response of an automatic driving taxi at a key node, an S1 safety state and an S2 risk state are defined, and a Viterbi algorithm is combined to deduce a hidden state probability; and finally, comprehensively judging the risk level according to the risk index and the state probability. According to the method, through multi-source data fusion and scene behavior modeling, dynamic evaluation and control of the operation risk of the automatic driving taxi are realized.

The invention discloses a simulation test method for a vehicle-mounted communication network. The method comprises the following steps: S1, initializing a vehicle-mounted communication environment; s2, generating a dynamic network topology; s3, environment adaptability dynamic interference is generated; s4, co-simulation of the dynamic network and interference; s5, testing and evaluating communication performance; and S6, visualizing and exporting a test result. According to the invention, the network topology reflecting the real-time motion state of the vehicle is dynamically generated, the communication change between the vehicles can be reflected in real time in different traffic environments, and the interference conditions in various complex environments are simulated in combination with the environment adaptability dynamic interference generation module, so that the performance of the vehicle-mounted communication network can be comprehensively evaluated.

The invention discloses a safety device for reducing the back-up collision risk in the automatic driving test process, and belongs to the technical field of automatic driving test safety. Aiming at the collision risk problem caused by rapid movement of dynamic obstacles and communication delay when a vehicle backs up in a closed test site, the device obtains millimeter-level position coordinates and course angles through positioning units deployed on a test vehicle and a target object; a C-V2X protocol is utilized to construct a real-time data link to share vehicle position, speed, backing track prediction data and target object outer contour feature point coordinates, potential conflicts are identified based on future minimum spacing calculation and dynamic threshold, and a cooperative collision avoidance path is generated in combination with a distributed optimization algorithm. And the high-risk vehicle preferentially preempts a communication channel to issue deceleration, course adjustment and braking instructions to surrounding vehicles. The device can effectively improve the reversing safety in a multi-vehicle cooperative test scene, and is suitable for collision risk real-time early warning and active intervention of an automatic driving vehicle closed test site.

Various embodiments herein provide techniques related to resource selection for sidelink transmissions. In some embodiments, the resource selection procedure may related to identification of whether to use a first or second resource selection procedure. In some embodiments, the resource selection procedure may relate to removal of non-preferred resources from a list of candidate resources. Other embodiments may be described and / or claimed.

A system and method including a vehicle having a first network zone and a second network zone of a different type than the first network zone; a converged network device (CND) interposed between the first network zone and the second network zone, the CND including a policy manager circuit structured to interpret a policy comprising a network regulation description; a configuration circuit structured to configure a first network interface circuit in response to the network regulation description; and where the first network interface circuit is structured to regulate communications between end points of the first network zone and end points of the second network zone.

An example system includes a vehicle having a first network and a second network of a different type than the first network; and a communication device interposed between the first network and the second network and structured to: generate network status data by monitoring communications of one or more devices on the first and / or the second networks; and transmit the network status data to a sensor device external to the vehicle.

The invention relates to a multi-vehicle intelligent driving cooperative training method based on a block chain in the Internet of Vehicles, and belongs to the technical field of mobile communication. Aiming at the problems of data privacy leakage, too high synchronous federated learning time delay, inaccurate node contribution degree evaluation, insufficient single-chain block chain expandability and the like existing in intelligent driving cooperative training in the Internet of Vehicles, an asynchronous federated learning framework with fusion of a cloud-edge-end three-layer architecture and a double-layer block chain is constructed. A local training strategy is optimized through multi-agent reinforcement learning to minimize the total time delay of a system, a dynamic reputation evaluation mechanism based on training interaction timeliness, confirmed site occupancy and model quality contribution is designed to screen high-reputation nodes, and asynchronous model verification and PBFT main chain global aggregation are realized by adopting a DAG block chain. According to the method, on the premise of guaranteeing data privacy, the model sharing efficiency is improved by more than 30%, the training time delay is reduced by 40%, the convergence stability of a global model is effectively enhanced, and the expansibility bottleneck of a traditional architecture is solved.

A blockchain of block entries that can be requested by users from user devices is maintained in a distributed network of nodes. Block entries include a plurality of data portions that are each associated with an access level. A request from an auditor to view one or more data portions of a block entry can includes an access code associated with at least one access level can be evaluated to identify one or more data portions associated with the access level. A customized view of the block entry which includes the one or more data portions associated with the access level can be generated. An artificial intelligence engine can review entries within the distributed ledger, identify earnings information associated with the sales of the commercial inventory, determine tax based on earning information, and pay the tax via fiat or cryptocurrency to government authorities based on earnings information.

The invention relates to a vehicle-mounted edge computing network scheduling method based on information age, and belongs to the field of mobile edge computing. The method is oriented to a vehicle-mounted edge computing network in which a fixed server and a vehicle-mounted mobile server coexist, and aims to minimize the long-term average information age of the system by selecting a device node combination scheduled by the server in each time slot. The position constraint, the link conflict constraint and the server computing resource constraint of a vehicle-mounted mobile server are considered, an optimization problem is modeled as a Markov decision process, and a deep breach Q network is improved by using a bifurcation structure and an action mask to obtain a scheduling strategy of an equipment node. According to the method, the bifurcation structure and the action mask are introduced into the deep breach Q network, so that the problem solving complexity is reduced, and the data delivery freshness of the vehicle-mounted edge computing network system is guaranteed.

The invention relates to a data processing method for iterative sensor fusion. The method comprises receiving sensor data from a first data source type and a second data source type. The first data source type is adapted for providing sensor data for exclusively determining an entity and the second data source type is adapted for providing sensor data of a tracked entity. The method further comprises generating a data source identifier for each of the sensor data. The data source identifier comprises a data source type identifier corresponding to a data source type from which the sensor data is received. A first data source type identifier corresponds to the first data source type and a second data source type identifier corresponds to the second data source type. The method further comprises compiling a dataset that comprises newest sensor data received, and iteratively performing a data association step on the dataset.

The invention relates to a resource allocation system for 5G-A Internet of Vehicles network slices, which belongs to the technical field of intelligent traffic systems and comprises a network slice architecture module, a management and control plane module, a programmable module, a safety and fault-tolerant module, a performance monitoring and optimizing module, a mobility management module, a resource arbitration engine and a cross-domain collaborative interface. And the network slice architecture module is used for dividing Internet of Vehicles communication into a plurality of virtual slices based on a 5G-A network slice technology. According to the resource allocation system and method oriented to the 5G-A Internet of Vehicles network slices, accurate control over end-to-end performance indexes is achieved through the slice definition template library and a QoS dynamic mapping mechanism, the problem that key service performance is degraded due to one-step resource allocation of a traditional network is solved, distributed agent collaboration based on the MAPPO algorithm, and the resource allocation efficiency is improved. LSTM load prediction and a heuristic time delay equalization algorithm are combined, so that the system can respond to business demand changes at a millisecond level, and the resource utilization rate is improved.

Provided is a full-duplex NOMA-based transmit power control device, the device including a state information collector configured to set a role of each of vehicle user equipments (VUEs) constituting a sub-system in a hyper-fractionated zone of a Manhattan mobility model and collect network state information from the sub-system, an actor network configured to determine transmit power of each of the VUEs on the basis of the network state information, a reward calculator configured to calculate a reward value for the network state information and the transmit power, a replay memory configured to store the network state information collected by the network state information collector, the transmit power determined by the actor network, and the reward value calculated by the reward calculator, and a critic network configured to evaluate the transmit power determined by the actor network and give feedback to the actor network.

The present disclosure relates to methods and system for acquiring and analyzing biosignals or physiological signals of a person sitting in a vehicle and predicting (in real-time) time-varying attention, engagement level or alertness level using the biosignals. The biosignals may be acquired using one or more clusters of electrodes together with a wearable user device or from a sensing device that is embedded in the seat of the vehicle. In some embodiments, the biosignals may be utilized to predict restedness level of the subject, to monitor or predict physiological state of the subject, to detect a distress situation and to adapt a vehicle control accordingly. In some other embodiments, the biosignals can be transformed into communication, for example, speech signals or instructions for the vehicle. One or more actions may be triggered based on the analysis of the biosignals including engaging the person, generating alerts, or adapting the vehicle control.