1105 results about "Connected vehicle" patented technology

A system and method for adaptive edge-cloud data processing dynamically distributes computational tasks between edge devices and cloud infrastructure in response to changing conditions. The system continuously monitors resource availability, network parameters, and workload characteristics while predicting future conditions using hierarchical forecasting models. A multi-objective optimization approach determines optimal task distribution, balancing processing latency, energy consumption, bandwidth utilization, and result quality. The system implements a partitionable processing pipeline that enables seamless task migration through state synchronization protocols and checkpoint mechanisms. During migration, the system preserves processing continuity by establishing dependencies, creating execution checkpoints, and verifying successful state transfer. Performance metrics may be continuously collected and analyzed to improve future decision-making. The system maintains operational resilience during connectivity disruptions through local decision-making capabilities and eventual consistency protocols, making it suitable for diverse applications including industrial IoT, connected vehicles, healthcare wearables, and smart city infrastructure.

The invention relates to the technical field of wireless communication networks, in particular to a vehicle ad hoc network based on intelligent networked vehicle WiFi and a V2V interaction method, a vehicle motion state and network load data are acquired through a vehicle-mounted sensor, AP / STA modes are dynamically switched by combining a deep Q network (DQN) model and an epsilon-greedy strategy, and an adaptive topological structure is constructed. And expanding OLSR protocol message embedding path delay fields, calculating and optimizing multi-hop routing selection in combination with pheromone gradient, and realizing message priority classification and differential privacy protection through a federated learning model. And dynamically adjusting an AP / STA proportion threshold based on a particle swarm algorithm, and triggering node role reconfiguration to balance network load. According to the method, the existing WiFi hardware is reused, the deployment cost is reduced, cross-layer routing optimization and a distributed decision mechanism are combined, and the network real-time performance, the expansibility and the group cooperation capability in a dynamic scene are improved.

The invention belongs to the technical field of Internet of Vehicles, and particularly provides a federated learning optimization system and method for an intelligent network connection automobile scene, which improves an asynchronous federated learning framework, divides a plurality of vehicles into different clusters due to the isomerism of computing power, and adopts a cloud-edge-end three-layer federated learning framework, so as to realize the optimization of the federated learning of the vehicles. An edge server is deployed on a roadside unit RSU, received model parameters are subjected to pseudo-asynchronous aggregation in a self-adaptive dynamic time window, the received model parameters are subjected to real-time aggregation at a cloud end, the system model training efficiency and quality are improved, meanwhile, the data security is ensured, the user privacy is protected, and the user experience is improved. And the efficiency of the federated learning optimization system and the quality of the global model in the intelligent network connection automobile scene are ensured.

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.

The invention discloses a network connection vehicle formation control method based on a distributed MPC, and belongs to the technical field of intelligent driving, and the method comprises the steps: constructing a vehicle formation longitudinal dynamics model; constructing a formation communication topology model; designing a following distance strategy; an MPC controller is designed according to the formation communication topology model and the following distance strategy; and designing a multi-component cost function, performing multiple iterative optimization based on MPC controller characteristics, and adjusting a control strategy to realize cooperative adaptive cruise control. According to the network connection vehicle formation control method based on the distributed MPC, the problems that in the prior art, real-time response is insufficient, cooperation among vehicles is insufficient, and system robustness is poor are solved.

Examples are disclosed for systems and methods for monitoring and filtering data transmitted to a vehicle connected to a wireless network. In one embodiment, a method for an edge node of a wireless network comprises routing traffic of the wireless network to a vehicle connected to the wireless network through the edge node; examining the traffic for potentially malicious content at the edge node; transmitting data packets of the traffic without potentially malicious content to the vehicle; and not transmitting data packets of the traffic with potentially malicious content to the vehicle.

The invention relates to the technical field of intelligent networked vehicles, in particular to an intelligent networked vehicle remote control and monitoring method. The method comprises the following steps: acquiring real-time running state information and roadside sensing data of a vehicle, and generating a remote takeover request; receiving a control instruction sent by the cloud control platform, and feeding back a control execution result; calculating instruction interaction delay and network transmission delay; the stability of the communication environment is evaluated by combining the two, and the vehicle driving mode is dynamically adjusted. Furthermore, time delay is calculated according to heartbeat signals and control instruction timestamps in the communication link, the stability index of the communication environment is evaluated, and the safe driving speed is adjusted according to the stability. And when the communication environment stability is lower than the threshold value or the instruction interaction delay exceeds the threshold value, an emergency stop mechanism is triggered. According to the method, the safety and the stability of the intelligent network connection vehicle under remote control can be improved.

The invention discloses an Internet of Vehicles vehicle information interaction system based on a YTS engine, and relates to the technical field of vehicle information interaction, a data acquisition module fuses multi-source sensing data, and an AI computing power optimization module adopts a dynamic computing power scheduling strategy; the 3D modeling and physical simulation module ensures the real-time performance of modeling based on an AI-driven 3D environment reconstruction and physical simulation technology; the cloud cooperative computing module processes multi-vehicle data in a distributed manner by using a cloud computing architecture of a YTS engine, optimizes a local model and improves the accuracy of an automatic driving decision; and the intelligent interaction module adaptively adjusts a modal man-machine interaction mode according to the dynamic change of the automatic driving decision, so that the driving experience is improved. The technical bottlenecks of information interaction delay, insufficient 3D modeling precision, poor decision stability and the like of the existing vehicle networking system in a high-speed dynamic environment are broken through; efficient energy consumption management, intelligent cloud optimization and accurate interaction are realized, and the safety and reliability of automatic driving are improved.

A real-time traffic signal optimization system that includes: (a) a traffic data acquisition module that collects real-time traffic data from sensors, cameras, GPS and vehicle-to-infrastructure communication; b) a machine learning-based traffic prediction engine that analyses historical and real-time data to predict congestion patterns; c) an adaptive signal control unit that dynamically adjusts traffic light switching times based on real-time traffic flow and predictive insights; (d) an emergency and priority vehicle management system that overrides normal signal operation to give priority to emergency vehicles and public transport vehicles; (e) a central traffic monitoring and control dashboard for real-time monitoring, manual interventions and system configuration; and (f) a vehicle-to-infrastructure communication interface enabling data exchange between traffic signals and connected vehicles to improve driver awareness and traffic coordination.

The invention relates to the technical field of automatic driving and the technical field of digital twinning, in particular to a remote control simulation driving system based on digital and physical twinning. Comprising a sensing layer, a twinborn layer and a decision execution layer, the sensing layer comprises automatic driving vehicle sensing based on an intelligent network connection vehicle and environment sensing based on a road side unit, and is used for collecting vehicle operation data and traffic environment data and using the collected data to construct a twin layer; according to the invention, a multi-mode sensor is used for collecting data, and a digital twin environment is remotely constructed, so that a safety officer can accurately recognize the environment where a vehicle is located; the real motion state of the vehicle is restored through remote simulation of the cockpit, physical twinning of vehicle motion is achieved, and the control precision of a safety officer is improved; by utilizing a simulated driving environment, the safety and comfort of remote takeover are ensured, accurate and efficient remote operation is realized, and finally the driving takeover level is improved.

The invention discloses an intelligent networked automobile AEB system based on an improved TTC model and a control strategy of the intelligent networked automobile AEB system. The control strategy comprises the following steps that S1, a data acquisition module acquires sensor data in real time and transmits the sensor data to a safe automobile distance judgment module; s2, calculating collision time Tttc by a safe vehicle distance judgment module; s3, calculating a dynamic safe distance Dego by a safe vehicle distance judgment module; s4, the safe vehicle distance judgment module calculates the braking distance which should be reserved in the vehicle driving process; s5, a safe vehicle distance judgment module calculates graded braking threshold values Ti, Tj and Ts; s6, a safe vehicle distance judgment module judges whether a collision risk exists or not according to the improved TTC trigger logic; s7, if the risk threshold is triggered, entering a grading early warning control module, and outputting corresponding strategies under different risks according to risk grades; the method solves the problems that a dynamic safety distance is lacked and a graded braking threshold value is statically set, and is suitable for more complex scenes with different vehicle speeds, road conditions and driving environments.

The invention discloses a V2V-based intelligent networked automobile road adhesion coefficient estimation method and system, and relates to the technical field of intelligent networked automobiles. The method comprises the steps that physical parameters and motion state information of a front vehicle are received based on V2V, an event triggering communication mechanism is adopted in the sending process of the physical parameters and the motion state information of the front vehicle, and meanwhile data packet loss is considered; constructing a three-degree-of-freedom vehicle dynamics model of the front vehicle; and establishing a road adhesion coefficient estimator based on an event triggering strong tracking unscented Kalman filter considering packet loss. According to the method, the front vehicle is regarded as a sensor of the vehicle, the parameters and motion information of the front vehicle are obtained through vehicle-mounted wireless communication, the problems of data packet loss and limited communication bandwidth during communication are considered, the robustness of an estimator to model parameter perturbation is improved through a strong tracking algorithm, and the estimation accuracy is improved. And finally, establishing a road adhesion coefficient estimator based on an event triggering strong tracking unscented Kalman filter considering packet loss, and ensuring that the road adhesion coefficient is accurately obtained in advance.

The invention relates to the technical field of intelligent traffic, in particular to an intelligent network connection vehicle driving risk assessment method and a visualization system based on multi-source data fusion. According to the method, multi-source data such as vehicle perception, communication and maps are fused, an artificial potential field theory is combined, a gravitational force risk potential field function, a repulsive force risk potential field function and a road boundary repulsive force potential field function are constructed respectively, and a unified total risk potential field TPF is formed by a gravitational force risk potential field, a repulsive force risk potential field and a road boundary repulsive force potential field; according to the method, the driving risk of the intelligent networked vehicle is obtained, risk grade division and real-time visual display are realized through clustering analysis, the accuracy and dynamic response capability of risk assessment are improved, the method is suitable for safety decision support of advanced driving assistance and automatic driving systems, real-time assessment of the driving risk can be realized more accurately and efficiently, and the risk assessment efficiency is improved. And the risk assessment precision and the dynamic response capability are improved.

The invention discloses a perception practical training platform and method based on intelligent edge calculation and heterogeneous data fusion. The platform comprises a sensor module, an interaction and display module, a calculation module, a movable base and a supporting rack. The sensing practical training platform adopts a modular design and a standardized interface; the sensor module, the interaction and display module and the calculation module can be conveniently disassembled, assembled and replaced, and convenient indoor and outdoor movement can be realized through a movable base with a stopper; through a data fusion technology of a sensor module, in combination with a GPU hardware acceleration strategy and a time synchronization technology, targets around the practical training platform are accurately detected in real time based on multi-source data, and a sensing result of the intelligent networked automobile to the targets in the surrounding environment is displayed. According to the invention, edge calculation provides a more convenient extensible path, and powerful support is provided for intelligent traffic through data acquisition expansion calculation and communication capability of road end data acquisition equipment and cloud end data acquisition.

The invention relates to the technical field of farm irrigation, in particular to a multi-source fusion and V2X networked vehicle high-precision positioning system and method, and the system comprises a vehicle-mounted terminal which integrates the following modules: a multi-source sensor group which comprises a laser radar (LiDAR), a binocular vision camera (BVC), a global positioning system (GPS), an inertial measurement unit (IMU) and a wheel speed sensor; the V2X communication module supports vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication and is used for receiving positioning data of an adjacent vehicle and roadside equipment; the system effectively solves the problems that in the prior art, a traditional RTK technology depends on a ground base station, a positioning blind area exists in a signal shielding area, data conflicts are easily caused by independent work of multiple sensors, RTK needs to be covered and supported by dense base stations, and the positioning accuracy is poor. And high-precision positioning cannot be realized in signal shielding scenes such as remote areas, tunnels and mountainous areas, so that an automatic driving function fails.

The invention relates to the technical field of predictive driving, in particular to a decision-making method for intelligent predictive driving of a networked automobile based on machine learning. Comprising the following steps: acquiring and preprocessing multi-source data of a networked automobile, real-time data of the Internet of Vehicles and historical driving data, and performing feature extraction on the preprocessed multi-source data of the networked automobile and the preprocessed real-time data of the Internet of Vehicles to obtain preliminary feature data; introducing a multi-layer recursive feature fusion algorithm to perform fusion processing on the preliminary feature data to obtain final fused data; and constructing and training a predictive driving model by using a networked automobile intelligent predictive driving prediction algorithm based on deep cross adaptability, and performing prediction processing on the finally fused data by using the predictive driving model to obtain a driving prediction result and generate a driving decision. The technical problems that processing of multi-source heterogeneous data is not accurate enough in intelligent predictive driving of the networked automobile, the predictive adaptability of driving decisions is poor, and the accuracy rate is low are solved.

An automotive cybersecurity platform receives vulnerability alerts that may impact a connected vehicle. Software components of the connected vehicle are identified and listed. Software components that are affected by a vulnerability are identified using information from a vulnerability alert. An overall risk score of the vulnerability is determined based at least on whether the vulnerability can be triggered, how the vulnerability affects the connected vehicle when the vulnerability is triggered, and an intrinsic risk posed by the vulnerability. Remediation of the vulnerability is prioritized based at least on the overall risk score of the vulnerability.

A system receives, from a plurality of connected vehicles, respective GPS data indicating an absolute location of a respective connected vehicle and on-board sensor data indicating locations of nearby vehicles and objects relative to the respective connected vehicle. The system processes the data to create a shared-world model that includes at least the locations of the plurality of connected vehicles and the nearby vehicles and objects. Some implementations of the shared-world model further include the speeds and trajectories of each of the plurality of connected vehicles and the nearby vehicles and objects. The system transmits a representation of the shared-world model to one or more of the plurality of connected vehicles, which may utilize the shared-world model to provide advanced warnings for drivers or to provide improved path planning for autonomous vehicles. Some representations of the shared-world model include lane-level traffic functions such as traffic density, traffic speed, and traffic throughput.

The invention relates to the technical field of Internet of Vehicles information authentication, in particular to a lightweight authentication system for an Internet of Vehicles vehicle pseudonym. The system obtains vehicle information and generates a pseudonym through an intermediate unit, then generates a corresponding secret key based on the pseudonym through a secret key generation unit, obtains the secret key through a road side unit and decrypts the secret key to obtain the pseudonym corresponding to a vehicle, and counts the authentication duration used by the road side unit to verify the pseudonym in a preset period through a timing unit. Then the analysis unit obtains a plurality of authentication durations, judges whether the operation of the authentication system is qualified based on the authentication durations, and generates a corresponding instruction based on the reason of disqualification, and the processing unit determines the enabling ratio of the verification equipment and the channel number of the verification equipment or optimizes the key generation process of the key generation unit based on the instruction; therefore, the authentication time is shortened, the operation efficiency of the authentication system is improved, the traffic operation efficiency is improved, and the traffic flow is optimized.

The invention relates to the technical field of vehicle carbon emission prediction, and particularly provides a digital twinning-assisted intelligent network-connected vehicle carbon emission prediction system and method, which combines digital twinning and deep learning, applies the combined digital twinning and deep learning to a carbon emission prediction link, and simulates various driving behaviors to obtain a carbon emission prediction result. And finally, giving an optimal carbon emission reduction mode based on the current driving state according to comparison, and feeding back the optimal carbon emission reduction mode to a user. Meanwhile, the whole prediction part can continuously compare a prediction result with an actual execution result to optimize the whole structure, so that the prediction accuracy and generalization are improved, and the user requirements are met.

The invention relates to the technical field of automatic driving, and discloses an intelligent network connection automobile track planning method based on combination of a space-time corridor and real-time aggressiveness. According to the intelligent networked vehicle trajectory planning method, driving behavior information of other vehicles and predicted trajectories thereof in a dynamic traffic environment are fused into trajectory planning of a main vehicle in real time by constructing a space-time corridor and considering the other vehicles and the predicted trajectories thereof. On this basis, a path is generated by applying a sampling-based seven-degree polynomial method, and a sequential quadratic programming optimization technology with soft constraints is combined to balance the safety, efficiency and comfort of the trajectory. In this way, aggressive changes in the traffic environment can be effectively predicted and dealt with, the track can be adjusted in real time, and the safety and comfort of the vehicle in the driving process are guaranteed, so that the complex urban traffic environment can be processed, it is guaranteed that the driving path of the main vehicle can be dynamically adjusted in different driving scenes, and the driving efficiency is improved. Therefore, changes of surrounding vehicles and obstacles can be handled, and more efficient and stable driving experience can be provided.

The invention discloses a mixed traffic scene-oriented different-intelligence network-connected vehicle collaborative decision-making method and system, and relates to the technical field of multi-vehicle collaborative decision-making, and the method comprises the specific steps: building a mixed traffic simulation environment for multi-agent reinforcement learning training; constructing a different intelligence network vehicle physical model, and performing function module configuration based on the grade of each vehicle; the different-intelligence networked vehicle physical model comprises a sensing module, a decision planning module, a control module and a communication module. A heterogeneous multi-agent deep reinforcement learning method is adopted to carry out cooperative training on the different-intelligence network vehicle simulation queue based on a mixed traffic simulation environment, and an optimal cooperative decision strategy is obtained; and deploying the optimal collaborative decision-making strategy to the different intelligence network connection vehicle queue for collaborative decision-making information generation. Through the parameter sharing and independent optimization mechanism of the high-level and low-level intelligent vehicle strategy and state value networks, hierarchical cooperation between different-intelligence vehicles is realized; and the overall traffic efficiency in the complex mixed traffic scene is improved.

The invention provides an intelligent networked vehicle intrusion detection method based on a time sequence and vehicle dynamics, belongs to the technical field of intelligent networked vehicle information security, and fuses a deep learning time sequence prediction model, a vehicle dynamics model and expert experience. A high-level vehicle state of the automatic driving system is predicted through a deep learning model, and a physical state is estimated by combining vehicle dynamics and expert experience to form comprehensive state estimation. And fusing the real-time measurement value by using extended Kalman filtering to obtain optimal state estimation. The precise intrusion detection is realized by calculating the residual error of the optimal estimation and the real-time measurement value, constructing a historical residual error data set, learning normal residual error distribution by adopting a clustering method, and defining a hyper-sphere boundary envelope normal sample. According to the method, the vehicle state estimation precision, the abnormal behavior detection accuracy and the system safety are remarkably improved, and a technical support is provided for safety protection of the intelligent networked vehicle.

An OTA security update method and system for intelligent connected vehicles are provided. The method includes: the downloaded software update packet from a CDN to UC-master is decrypted, and verified for the first time through signature verification; for the secondary verification, decomposing a current software update packet into a plurality of sub-packets; calculating HASH values of the sub-packets in sequence; thereafter, constructing a Merkle tree according to these HASH values; calculating a root HASH value of the Merkle tree; after the sub-packets are transferred from a UC-master to ECU-UAs through a gateway or a domain controller, recalculating the HASH values of the sub-packets; reconstructing the Merkle tree according to these HASH values; recalculating the root HASH value of the Merkle tree; comparing the two HASH values; if they are equal, starting a normal update procedure; otherwise, terminating this OTA update by the UC-master immediately.

A traffic management system and method for operating the same includes a plurality of roadside devices associated a plurality of intersections. A control module is programmed to receive priority requests from priority connected vehicles that comprise a priority level associated with a type of vehicle. The control module generates average estimated arrival time for an intersection for the vehicles based on historical data, receives roadside device data from a plurality of roadside devices, estimates congestion and flow dynamics for the priority connected vehicles based on the average estimated arrival times and real-time data from the vehicles and data from the roadside devices, determines adapted arrival times based on the congestion and flow dynamics and adjusts a signal phase and timing for a traffic signal of the intersection for the priority connected vehicles based on the priority and adapted arrival times. A traffic signal operates with the signal phase and timing.

The invention relates to a vehicle MEC task unloading and resource allocation optimization method and system. The method comprises the following steps: establishing a road scene in which an unmanned aerial vehicle assists in vehicle edge calculation; defining a resource matrix and time delay according to the road scene; comprehensively proposing an optimization problem of minimizing task processing time delay based on a task unloading mode; describing a task unloading decision process through an MDP model; communication links between the network connection vehicle and the road side unit and the unmanned aerial vehicle in the communication coverage range where the network connection vehicle is located are established, and the total time delay of the calculation task and the size of cache resources are calculated; generating a task unloading decision and a resource allocation strategy by using a DDPG algorithm; and optimizing a task unloading decision and a resource allocation strategy in real time to obtain an optimal unloading decision and an optimal resource allocation strategy. Compared with the prior art, the method has the advantages that the resource utilization rate is increased, the system time delay is reduced, the method adapts to complex scenes of vehicle high-speed movement and network load dynamic change, and continuous and reliable calculation service is provided for time delay sensitive vehicle-mounted application.

A sidelink communication method and an apparatus are disclosed, and are applicable to fields such as V2X, vehicle-to-everything, assisted driving, or autonomous driving. The communication method includes: A first terminal device determines a resource pool corresponding to each of at least two carriers in a first carrier set, where a sidelink synchronization signal block S-SSB resource is configured on each carrier in the first carrier set, and any resource in a resource pool corresponding to any carrier in the first carrier set does not overlap, in time domain, an S-SSB resource on any carrier in a second carrier set; and the first terminal device selects at least one first resource from the resource pool to send data.

The invention relates to the technical field of knowledge maps, in particular to an intelligent networked automobile data processing system based on data elements and knowledge maps, and the system comprises a data element definition module which analyzes original intelligent networked automobile data elements, distinguishes the original intelligent networked automobile data elements into a vehicle static parameter set and a vehicle dynamic parameter set, and forms a structured map blueprint. According to the method, element analysis is carried out on the intelligent network connection automobile data, and the intelligent network connection automobile data is divided into the static parameter set and the dynamic parameter set, so that the data has hierarchical structural characteristics, and the controllability and definition of subsequent data processing are improved. And in combination with differentiated structure construction of the static storage layer and the dynamic storage layer, a hierarchical data configuration model with clear logic is formed, and the data scheduling efficiency and the system load stability are improved. A geographic grid coding mode is introduced, space division is carried out on entity data with position attributes, spatial dimension information is made to have high-density indexability, and positioning and regionalization operation can be achieved easily.