358 results about "Connected vehicle" patented technology

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.

An example operation includes one or more of establishing a secure channel between a host platform and a vehicle based on a transport layer security (TLS) handshake between the host platform and the vehicle, downloading an authorization code to the vehicle through the secure channel between the host platform and the vehicle, receiving the authorization code from a mobile application installed on a mobile device, generating a mobile application certificate for the mobile device and transmitting the mobile application certificate to the mobile application on the mobile device, and establishing a secure connection between the host platform and the mobile application on the mobile device based on the mobile application certificate.

The invention discloses a multi-modal sensing fusion-based safety detection method and system for an intelligent networked automobile, and relates to the technical field of safety detection of intelligent networked automobiles. The multi-modal sensing fusion-based safety detection method for the intelligent networked automobile comprises the following steps: acquiring multi-source sensing data of the intelligent networked automobile in a driving process based on a set sampling period; a multi-mode driving feature set of the intelligent network connection automobile is constructed; based on the multi-modal driving feature set, analyzing a driving state index set of the intelligent networked vehicle; analyzing a driving state fusion index of the intelligent networked automobile; according to the method, the image connectivity feature, the approach evolution feature and the attitude fluctuation feature are constructed and are subjected to joint analysis to form judgment indexes of the regional connectivity index, the relative displacement index and the attitude response index, so that judgment deviation caused by distortion or missing of single sensing data is effectively avoided.

The invention discloses a multi-port hybrid charging pile power pool reinforcement learning scheduling method and system, and relates to the technical field of computer data processing. The reinforcement learning scheduling method and system for the multi-port hybrid charging pile power pool comprises the following steps: S1, periodically collecting port scheduling data, and preprocessing the port scheduling data; s2, constructing and training a vehicle target SOC prediction model, outputting a vehicle target SOC, evaluating charging urgency of each accessed vehicle, and dividing scheduling priority levels; s3, constructing a port power distribution model, outputting a power distribution value of each charging port, and issuing the power distribution value to a power control interface for execution; and S4, evaluating power scheduling execution deviation, and entering a protection mode when evaluation is abnormal. The problems that the total power of the charging pile cannot be flexibly scheduled due to fixed-proportion power distribution and topological isolation of alternating-current and direct-current ports, high-power resources are occupied by high-electric-quantity vehicles, and charging of low-electric-quantity vehicles is limited are solved.

The invention discloses a road test route design method and system for an intelligent connected automobile, and the method comprises the steps: determining a full path based on starting point coordinate information and terminal point coordinate information, and processing the full path in combination with user path selection preference and historical test path preference data to generate a plurality of test paths; acquiring historical operation parameters and current operation parameters of path points in the test paths, inputting the parameters into a preset safety evaluation model to output a safety evaluation result, comparing, determining credible path points, and determining candidate test paths from the plurality of test paths according to the credible path points; and selecting a final test path from the candidate test paths according to a preset selection rule. According to the method, pertinence of path design is improved by combining user path selection preference and historical test path preference data, accuracy of safety evaluation is ensured by combining historical and current operation parameters, and scientificity and optimality of final test path selection are realized through a multi-stage objective screening mechanism.

The invention discloses a networked vehicle queue safe cruise method for coping with hybrid network attacks, the vehicle queue comprises a head vehicle and a following vehicle, the following vehicle collects the running state data of the vehicle, obtains the running state data of other vehicles through a communication network, continuously detects the state of the communication network, and sends the detected state to the vehicle queue. Detecting whether a network attack is currently suffered or not and the type of the suffered attack, and calculating the minimum characteristic value of the topological matrix in a normal communication state, a spoofing attack state or a replay attack state according to the identified attack type; and then solving the constructed linear matrix inequality which enables the vehicle closed-loop system to be stable to obtain a feedback gain, and generating a control input signal according to the feedback gain obtained by calculation to realize accurate control of the vehicle. The feedback gain is dynamically adjusted according to the real-time state of the vehicle, so that the safety cruise control of the vehicle queue system is more effectively realized.

A method for privacy preservation for machine learning networks includes splitting a trained neural network into a first part and a second part. The first part is a privacy preservation (PP) encoder and the second part is a PP machine learning (ML) model. The method further includes retraining the PP encoder and the PP ML model. The method can improve artificial intelligence (AI) systems, optimize performance and support decision making in use cases including, but not limited to medical / healthcare, fraud detection, image recognition, predictive maintenance and connected vehicles.

The invention relates to the technical field of network security of networked automobiles, and discloses an intelligent detection and self-adaptive repair method for network security vulnerabilities of networked automobiles, and the method comprises the steps that a security probe collects bus communication data, application layer protocol data and system operation logs in a vehicle, and generates a security state data set; the security management and control platform receives real-time data to construct a security state matrix, extracts abnormal behaviors, protocol violation and other characteristics by analyzing the matrix, combines attack behavior modeling, attack intensity gradient calculation and an adaptive repair model, outputs space-time propagation characteristics of security threats, updates a data set, and identifies a potential attack mode; and dynamically deploying a protection strategy according to an attack mode, wherein the protection strategy comprises operations such as sub-domain mapping, access control updating, communication encryption and resource isolation. According to the method, intelligent detection and self-adaptive repair of network security vulnerabilities of networked automobiles are realized, and the accuracy and real-time performance of security protection are improved.

The application discloses a traffic data sharing method and device, a nonvolatile storage medium and an electronic device. The method comprises the following steps: a first edge computing node in a blockchain network sends an edge computing node public key of the first edge computing node to an authoritative trusted node in the blockchain network, and obtains a verification certificate generated by the authoritative trusted node according to the edge computing node public key; the first edge computing node verifies the verification certificate, and after the verification result is verified, obtains traffic sharing data in the blockchain network, wherein the traffic sharing data comprises traffic data uploaded to the blockchain network by a second edge computing node; and the traffic sharing data is broadcasted through a base station where the first edge computing node is located. The application solves the technical problem that the road information corresponding to different operators in the region cannot be integrated and shared due to the fact that the networked vehicles corresponding to different operators cannot directly transmit data in the prior art.

The invention discloses a security situation awareness method based on an intelligent networked automobile, and relates to the technical field of new energy intelligent automobiles. S2, establishing a vehicle real-time communication link; s3, early warning communication of adjacent vehicles; and S4, protecting the driving operation. According to the security situation awareness method based on the intelligent networked automobile, a vehicle-mounted external sensor network is constructed through a Sudoku topology, the awareness range of a single vehicle is expanded to the whole domain covered by the Sudoku, and information interaction is performed between the vehicle in the Sudoku and the adjacent vehicle through a communication link. Therefore, the sensing range is greatly expanded, the sensing blind area is effectively eliminated, and the early warning information is transmitted through V2X, so that the vehicle in the middle lane can respond in time when the vehicle in the side lane encounters the ghost probe condition, and the vehicle in the middle lane does not need to respond when sensing the ghost probe condition. Therefore, the advanced response time is greatly prolonged according to the early warning information of the surrounding vehicles.

The invention discloses an intelligent networked automobile safety situation assessment method based on multi-source information fusion. The method comprises the following steps: 1) acquiring training parameters of a speed prediction model; 2) training a neural network by using the speed prediction model training parameters, and constructing a speed prediction model; 3) predicting the speed of the vehicle at the next moment by using the speed prediction model; 4) acquiring driving safety information, and processing the safety information to obtain a safety situation assessment parameter; and 5) processing the safety situation assessment parameter to obtain a current safety situation assessment value so as to determine the driving safety state of the vehicle. According to the method, the self-vehicle speed prediction model and the driving safety situation evaluation model are constructed, and the importance of the safety situation value on the driving safety of the intelligent networked vehicle is clarified by taking the prediction of the self-vehicle speed as a link, so that theoretical and technical support is provided for realizing the safe driving technology of the intelligent networked vehicle.

Aspects of the presently disclosed technology may be implemented to provide systems and methods which integrate electric vehicles (EVs) into vehicular micro clouds (VMCs) by reducing the impact of VMC participation on the state-of-charge of integrated EVs. For example, an EV of the presently disclosed technology may join a first VMC comprising a group of connected vehicles that share resources to complete a task. The EV may predict that performance of one or more sub-tasks assigned to the EV by the first VMC will deplete a state-of-charge of the battery over a threshold amount. Based on the prediction, the EV may withhold performance of the one or more sub-tasks. The EV may then become leader of a second VMC, and as leader of the second VMC, assign the one or more sub-tasks to one or more vehicles of the second VMC.

The invention discloses a network attack detection system and a network attack detection method for a digital-aware-fused intelligent connected automobile, and relates to an intelligent vehicle technology. The system comprises a vehicle end data generation module, a road side unit data acquisition module, a cloud end attack detection module and a detection result display module. The method comprises the two steps of online real-time detection and offline training, during offline training, a training database containing multiple attack types is firstly constructed, then a data driving model is constructed based on a hierarchical vector Transformer model, and finally constraints in vehicles, between vehicles and between vehicles and lanes are fused, so that the data driving model is constructed. Weak, medium and strong constraints are divided according to the relevance between the constraints and the attacks, and a differential fusion scheme is designed. According to the method, deep fusion of data driving and knowledge guiding is realized, spatio-temporal features are extracted in a layered manner, high-precision map information is fused, attack detection precision and interpretability are improved, various network attacks can be effectively detected, and safe and efficient operation of an intelligent networked automobile is guaranteed.

This application provides a method and apparatus for sending and receiving information, which can be applied to vehicle-to-everything (V2X), LTE-V, V2V, etc., or to fields such as D2D, intelligent driving, and intelligent connected vehicles. When the uplink channel corresponding to the first uplink information and the uplink channel corresponding to the second uplink information overlap completely or partially, the terminal sends the higher-priority information between the first and second uplink information according to priority order. This fully considers the importance of different uplink and sidelink information and the tolerance for latency, reducing the probability of communication errors while maximizing the reliability of uplink transmission in scenarios where Uu links and sidelinks coexist.

This invention relates to the field of glass production technology, specifically disclosing a glass edging system. The invention includes a distributed sensing unit, a closed-loop control unit, an active execution drive unit, and a low-power wireless communication unit. The distributed sensing unit is used to collect real-time data on glass edge stress and clamping gap for glass edging adaptation. This invention achieves real-time, comprehensive, and accurate collection of core operating conditions of the glass edging system across all dimensions through the distributed sensing unit, effectively capturing early hidden dangers in the glass and edging system. Through the coordinated linkage of the closed-loop control unit and the active execution drive unit, a complete closed loop of "monitoring-judgment-execution-feedback" is constructed, enabling proactive intervention and precise elimination of abnormal operating conditions. The low-power wireless communication unit enables bidirectional data interaction between the system and external operation and maintenance platforms, seamlessly connecting to intelligent operation and maintenance systems in scenarios such as smart buildings, intelligent connected vehicles, and rail transit.

The application relates to the unmanned driving technical field and discloses an intelligent networked vehicle environment sensing method based on multi-sensor fusion, which comprises the following steps: when it is judged that an obstacle appears in the driving direction of a target vehicle, a camera image set in a current time range is acquired; whether the obstacle is a target obstacle is judged according to the camera image set in the current time range, and a judgment result is obtained; when the judgment result is the target obstacle, the motion trajectories of the target vehicle and the target obstacle in the current time range are respectively acquired; the motion trajectories of the target vehicle and the target obstacle in the current time range are respectively input into a preset LSTM model, and the motion trajectories of the target vehicle and the target obstacle in a future time interval are obtained; whether the target vehicle and the target obstacle will collide is predicted according to the motion trajectories in the future time interval, and a prediction result is obtained; and the application is favorable for avoiding frequent occurrence of vehicle emergency obstacle avoidance events.

This application relates to the field of network detection technology, and discloses a method, apparatus, device, and medium for identifying in-vehicle applications. The method includes: reading the behavior sequence of the in-vehicle application within a detection time window; determining discrete anomaly information of the behavior sequence based on discrete outliers of each behavior feature in the behavior sequence; dividing the behavior sequence into multiple sequence segments when the discrete anomaly information meets preset conditions; performing temporal analysis on the behavior features in the sequence segments to generate temporal anomaly information of the behavior sequence; reading and parsing a preset temporal rule chain, and performing temporal and feature matching between the behavior features in the behavior sequence and the rule features in the temporal rule chain to obtain rule matching information of the behavior sequence; and determining whether the in-vehicle application is a malicious application based on the temporal anomaly information and the rule matching information. The technical solution provided by this application can effectively ensure the security of intelligent connected vehicle systems.

This invention belongs to the field of intelligent transportation technology and discloses a vehicle task offloading method assisted by drone swarms in intelligent transportation environments. This method targets a traffic light intersection scenario consisting of multiple intelligent connected vehicles and drone swarms. The drone with the most battery power in the swarm is designated as the cluster leader drone, and the remaining drones are designated as edge service drones. A DQN algorithm is deployed on the cluster leader drone. The DQN algorithm design can monitor the battery level of the edge service drones in real time and dynamically adjust the reward function based on battery consumption, fully utilizing the computing resources of the edge service drones to complete more tasks with limited battery power. This invention can also determine the task type based on the amount of computation generated by the vehicles and apply rewards and penalties accordingly, enabling the agent to adopt an offloading strategy corresponding to the task type, fully utilizing computing resources, reducing communication resource waste, and improving task completion rate.

The invention introduces an Internet of Vehicles vehicle reputation dynamic evaluation method based on an evaluation graph. The method comprises the following specific steps: S1, constructing a global vehicle evaluation graph; s2, carrying out local evaluation and set division; s3, evaluation set selection; s4, performing weighted aggregation calculation; s5, performing iterative updating and amplitude limiting processing; s6, vehicle-trust center closed-loop interaction is carried out; s7, performing long-term reputation evolution; according to the method, evaluation control and collusion attacks are prevented, long-term and smooth evolution of the vehicle reputation is also realized, the robustness and credibility of an Internet of Vehicles system are remarkably improved, meanwhile, forward excitation on low-reputation vehicles is considered, and a dynamic and credible vehicle reputation management system is formed.

This invention belongs to the field of intelligent connected vehicles and vehicle-road-cloud integration technology, and discloses a method for traffic emergence recognition and new function discovery for IVCPS. This invention collects multi-dimensional traffic states, constructs univariate and joint probability densities, and achieves early and accurate recognition of traffic emergence based on distribution distance; then, it constructs a novelty index through mutual information to quantify the novelty of system functions; finally, it uses novelty as a reward-guided parameter to automatically discover new functions. This invention can effectively capture early emergence, quantify new functions, and form an integrated closed loop of recognition and discovery, improving the autonomous cognition and proactive optimization capabilities of IVCPS, and is applicable to intelligent road networks, vehicle-road cooperation, and proactive traffic management.

The application belongs to the technical field of industrial process control and vehicle networking cooperative control, and discloses a vehicle networking cooperative vehicle fleet self-triggered formation control method under denial of service attack conditions, which comprises the following steps: constructing a state space equation of cooperative vehicles, and obtaining expected formation states and actual states of a leading vehicle and a following vehicle; step 2, modeling possible denial of service attacks in vehicle networking; step 3, designing a self-triggered control update strategy for each following vehicle in a time interval in which communication is normal and the communication topology of the fleet remains connected; step 4, based on steps 1, 2 and 3, constructing a fleet consistency controller, and under the condition that the denial of service attack satisfies the frequency and duration constraint conditions, the fleet system meets the control requirements. The application reduces the communication and calculation burden of the fleet, improves the safety and robustness of the system under the conditions of communication attack, limited bandwidth and unstable network, and is suitable for cooperative fleet formation control scenes under various vehicle networking topological structures.

This invention relates to a method for calculating the actual road emissions of heavy-duty vehicles under remote monitoring. Based on the three-zone moving average window method, a three-zone moving average window model is established using a fixed duration of 300 seconds. The corresponding load ratio is calculated based on the carbon dioxide emissions of each window. The window, moving second by second, is divided into idling, low-load, and medium-high load zones according to the load ratio. Emission values ​​for the three zones are calculated, and it is verified whether the emissions in each zone exceed the limits. The beneficial effects are: This invention solves the current limitation of existing calculation methods for verifying the actual driving emission levels of vehicles after receiving emission data from networked vehicles on a remote platform. It can fully utilize networked vehicle emission data, taking into account emission data under idling and low-load conditions. It has no strict requirements on vehicle load and data continuity, does not distinguish between vehicle types, simplifies the monitoring procedure, and has strong adaptability and wide application range.

This invention discloses an electronic parking sign display method and system, belonging to the field of intelligent connected vehicle technology and smart transportation service technology. The method includes: when the parking sign is activated, randomly generating temporary vehicle relocation information; controlling the parking sign to display the temporary vehicle relocation information; when the parking sign is deactivated, controlling the parking sign to stop displaying the temporary vehicle relocation information and controlling the temporary vehicle relocation information to become invalid. When the vehicle is parked and leaving the parking lot, displaying the temporary vehicle relocation information serves as an information reminder function. Afterwards, the vehicle owner cannot be contacted through this temporary vehicle relocation information, preventing the long-term leakage of the vehicle owner's information.

The invention discloses an intelligent network connection vehicle-mounted terminal remote vehicle locking method, belongs to the technical field of remote vehicle locking, and solves the technical problem that a vehicle enters passive vehicle locking due to storage writing or reading abnormity. The method comprises the steps that a first control unit and a second control unit are arranged on the vehicle-mounted terminal, the first control unit is in communication connection with the second control unit, and the second control unit is responsible for receiving a vehicle locking instruction issued by the vehicle networking platform and transmitting the vehicle locking instruction to the first control unit; the first control unit is responsible for performing protocol interaction with the whole vehicle ECU according to the vehicle locking instruction to achieve the purpose of activating vehicle locking; the first control unit and the second control unit store vehicle locking instructions respectively, when the vehicle-mounted terminal is started, the second control unit reads the vehicle locking instructions stored locally and transmits the vehicle locking instructions to the first control unit, and the first control unit reads the vehicle locking instructions stored locally; and when the received vehicle locking instruction or the local vehicle locking instruction is established, the first control unit and the whole vehicle ECU perform protocol interaction to request the vehicle locking state.

The present application belongs to the technical field of intelligent transportation system, and relates to a signal-vehicle cooperative optimization method based on MPC and trust region Bayesian optimization. Firstly, traffic flow data collection and road network simulation modeling are performed, and initial path allocation of vehicles is performed, then real-time speed control is performed on intelligent connected vehicles entering the road network, and the best driving speed is calculated by using a Bayesian optimization algorithm based on a trust region according to signal light information, distance from the intersection and driving conditions of surrounding vehicles. Then, the road impedance is updated, and the best driving path of the intelligent connected vehicle is recalculated. Then, the future traffic state is predicted by using model predictive control, and the best green light time of each intersection in different phases is calculated by using trust region Bayesian optimization. The best green light time is imported into the simulation platform, and vehicle path optimization and speed control are performed again, and the cycle feedback is performed. Finally, it is judged whether the vehicle leaves the road network or reaches the simulation end time, and the vehicle control and signal optimization are ended.

This application relates to the field of intelligent connected vehicle technology, and particularly to a method and apparatus for remote configuration of scenario modes. The method includes: acquiring multiple atomic conditions and multiple atomic actions corresponding to various scenario modes of a target vehicle, to construct a target scenario mode based on at least one atomic condition and at least one atomic action; configuring the target scenario mode to the target vehicle, and obtaining the execution result of the target scenario mode if the atomic conditions and atomic actions in the target scenario mode meet the target safety conditions; adjusting the atomic conditions and / or atomic actions in the target scenario mode according to the execution result until the adjusted target scenario mode meets the target requirements, and configuring the adjusted target scenario mode to the target vehicle. This solves the problems of inflexible configuration logic, long response cycles, and difficulty in quickly matching dynamic market demands in the construction methods of scenario mode configuration technologies in related technologies, which affect the optimization and iteration of the driving experience.

A method for determining a precipitation event from network vehicles, includes receiving at a backend portion information relating to a precipitation event, associating the precipitation event with a location, comparing the information relating to the precipitation event to one or more thresholds, and sending a notification from the backend portion to multiple vehicles when one or more thresholds are met.

The invention relates to a method and device for processing data from vehicles connected to a wireless communication network. For this purpose, a group (11) of connected vehicles is selected from a set of connected vehicles and driving data is collected, with an item of seasonal information representative of the season in which the driving data was obtained being associated with the driving data. A sample (111 to 114) is selected for each season from the group (11) of connected vehicles as a function of a score calculated for the connected vehicles that were driven at least once during that season.

Presented are vehicle systems and control logic for provisioning self-detection and violation-notification capabilities for connected vehicles, methods for making / using such systems, and vehicles equipped with such systems. A method of controlling operation of a host vehicle includes a resident or remote vehicle controller or network of controllers determining if a wireless communications device of the host vehicle has an active wireless connection with a remote computing node. If so, the vehicle controller(s) receive, from the remote computing node via the wireless communications device, a wireless message containing an emergency alert and culprit data indicative of a target vehicle. The vehicle controller(s) then parse the culprit data from the wireless message and analyze the parsed culprit data to determine if the host vehicle is the target vehicle. If so, the vehicle controller(s) responsively command one or more resident vehicle subsystems to automate one or more remediating actions for the emergency alert.