925 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 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.

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.

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 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.

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.

The invention discloses a high-end equipment information safety risk assessment method based on a self-attention layer, and relates to the field of intelligent network connection automobiles and information safety. The method comprises the following steps: creating a threat analysis and risk assessment attack behavior knowledge base; establishing a threat detection model; training a threat detection model; evaluating the risk according to the threat prediction result of the model; and carrying out security reinforcement on high-end equipment and an information system. The method can improve the safety protection capability in the core controller of the intelligent networked automobile, and can be widely applied to the fields of ship engine controllers, aviation unmanned aerial vehicle navigation and positioning systems and the like.

A method of utilizing vehicle telematics to create a vehicle wagering network by connecting vehicles to mapping and traffic data. Connected vehicles transmitting real-time telemetric data form the basis for wagers.

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

A signal sending method, a signal processing method, and a radar apparatus are provided which are, related to sensor technologies. The radar apparatus includes at least three transmit antennas. The signal sending method includes: determining at least two transmit antenna groups of the radar apparatus, wherein each transmit antenna group includes at least one transmit antenna; sending signals by using the at least two transmit antenna groups, wherein the at least two transmit antenna groups send signals in a TDM manner, and a plurality of transmit antennas included in each transmit antenna group including a plurality of transmit antennas in the at least two transmit antenna groups send signals in a CDM manner. Embodiments of this application may be applied to related fields such as autonomous driving, assisted driving, intelligent driving, intelligent connected vehicle, intelligent vehicle, and electric mobile / electric vehicle.

The invention relates to an active network defense method and system based on watermark and moving target fusion, and the method comprises the steps: obtaining the input and output data of a communication system, and constructing a system model and a holosymmetric polytope observer model according to the input and output data; constructing an attack detector based on the system model and a holosymmetric polytope observer model; and based on the attack detector, the system model and the holosymmetric polytope observer model, watermarking input and output data through a pseudo-random number generation method and a preset target defense function. Through the watermark and moving target removal technology, the problem that the system performance is influenced by an existing watermark method is solved; and meanwhile, false data attacks, denial of service attacks, replay attacks and the like can be detected. In addition, the method is suitable for application scenes needing active attack detection, such as an intelligent connected vehicle system and an intelligent power grid system, and has very high application value.

The invention provides a CAN message data flow anomaly detection method, system, device, medium and product, and belongs to the technical field of automobile network and data security. An automobile end anomaly detection model of a to-be-detected automobile performs anomaly detection on ID information entropy of a CAN message data flow based on an information entropy threshold interval, and reports the CAN message data flow if no anomaly is detected; the cloud extracts byte data streams corresponding to each byte of the CAN message from the obtained CAN message data streams and sends the byte data streams to the cloud; and inputting the byte data stream into the corresponding cloud anomaly detection model, and fusing detection data output by each cloud anomaly detection model to obtain an anomaly detection result. Two-stage cooperative detection of the to-be-detected automobile is realized, abnormal conditions such as CAN message data flow violation reporting and information tampering simulated by network attacks such as DoS and injection of the intelligent networked automobile can be accurately detected, the capacity of coping with hidden attacks is improved, and the network security and the data security of the intelligent networked automobile are further improved.

The invention provides a networked automobile risk scene analysis method based on a multi-modal corpus and related equipment, and the method comprises the steps: receiving the multi-modal corpus containing various types of test data, and carrying out the format standardization and time synchronization processing of the multi-modal corpus; performing risk scene analysis on the basis of association information among different types of test data in the multi-modal corpus, identifying abnormal behaviors occurring in the test process, and generating corresponding structured event description information; and extracting key data fragments and image frames associated with the abnormal behaviors, carrying out image-text association on the key data fragments, the image frames and the event description information, and generating a test report based on a preset template. According to the method, a multi-modal corpus analysis mechanism is introduced, risk scene analysis and abnormal behavior recognition are executed, and image-text matching and report generation are automatically completed. The manual arrangement burden is reduced, and the intelligent degree of risk scene identification and the automation level of test report generation are improved.

Systems and methods for implementing a balanced client selection for decentralized machine learning (BCS-DL) that can be utilized in both decentralized machine learning and hybrid machine learning for vehicle environments are described. For example, a vehicle can include a processor device training a machine learning model using local data, and a controller device performing balanced client selection in real-time to communicate the local machine learning model with a connected vehicle for decentralized machine learning. Hybrid machine learning combines aspects from federated learning and decentralized machine learning approaches. The disclosed BCS-DL system is designed to execute balanced client selection in real-time for vehicles that are acting as clients in a hybrid machine learning infrastructure. The balanced client selection also calculates a training contribution estimation (TCE) and a model weight computation (MWC) to mitigate imbalance in the data distribution incurred by non-Independent, Identically Distributed (non-IID data) related to decentralized and hybrid machine learning.

The invention discloses a multi-vehicle cooperative three-dimensional target detection method for a heterogeneous intelligent network connection vehicle group, and the method comprises the following steps: carrying out the preprocessing of obtained multi-source heterogeneous vehicle-mounted sensor data, and extracting the features of multi-mode heterogeneous data; constructing a dynamic pose compensation network, learning and predicting a pose compensation amount by using a time sequence, correcting a pose error in multi-vehicle cooperative perception, and ensuring a space reference of multi-vehicle features; performing cross-modal feature fusion on the multi-vehicle multi-modal heterogeneous data features through a global-local cross-modal attention mechanism; designing a multi-scale feature distillation strategy, and reducing the semantic fusion gap of the cross-modal features; an end-to-end combined training framework is constructed to integrate dynamic pose compensation, cross-modal feature fusion and multi-scale feature distillation strategies, and a multi-vehicle collaborative three-dimensional target detection model is optimized through an adaptive weighted loss function. The technical problems of multi-vehicle heterogeneous sensor fusion, pose drift correction and cross-modal semantic alignment are solved.

The invention discloses a high-definition image optimization method for an intelligent networked automobile, relates to the technical field of image optimization, and solves the problems of numerical deviation and blurring of the contour edge of an object caused by gray value overlapping of corresponding areas of front and rear frames of images due to object movement. According to the method, the pixel value difference between adjacent frames is fully utilized, gradient calculation and contour area judgment are combined, the dynamic object can be accurately positioned, compared with a traditional method, misjudgment and missed judgment in a complex environment can be reduced, a reliable basis is provided for subsequent optimization, and the sensing accuracy of an intelligent networked automobile to the surrounding dynamic environment is improved; abnormal contour lines can be quickly identified by comparing gradient features of contour areas of similar dynamic objects in continuous frames; according to the method based on gradient column difference analysis, gray scale deviation generated by pixel point overlapping can be sensitively captured, contour blurring caused by foldover is avoided, and the definition and integrity of the edge of a dynamic object in the image are remarkably improved.

The invention discloses a road damage detection method based on an unmanned aerial vehicle and an intelligent networked automobile, and the method specifically comprises the following steps: (1) controlling the unmanned aerial vehicle and a vehicle-mounted camera to synchronously collect road surface images, inputting the collected road surface images into a road damage detection model through the unmanned aerial vehicle and the vehicle-mounted camera, and recognizing the road damage information of a current road section and the confidence coefficient through the road damage detection model; the damage information comprises a road damage type, a road damage coverage area and a road damage position; and (2) guiding the flight action of the unmanned aerial vehicle based on the low-confidence road damage position, so that the unmanned aerial vehicle flies towards the low-confidence road damage position, and returning to the step (1). Road damage identification is carried out based on the road surface images synchronously acquired by the unmanned aerial vehicle and the vehicle-mounted camera, more detail features of the road damage are captured by guiding the unmanned aerial vehicle to approach the road damage with low confidence, and the road damage can be identified more accurately.