600 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.

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 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.

The invention relates to the technical field of intelligent automobile test and evaluation, in particular to an intelligent networked automobile automatic driving performance evaluation method based on simulation test, which comprises the following steps: S1, generating a reference steady-state traffic flow in a simulation platform; s2, injecting traffic flow disturbance parameters into the reference steady-state traffic flow, and recording whole-course operation data of the reference steady-state traffic flow; s3, extracting performance indexes of the tested vehicle; s4, calculating a deviation degree, and quantifying the deviation degree as a performance attenuation rate; s5, performing weighted fusion to obtain a comprehensive disturbance tolerance index; and S6, outputting an evaluation conclusion of the automatic driving performance of the tested vehicle according to the comprehensive disturbance tolerance index. According to the method, the reference traffic flow is constructed, the disturbance is injected, the performance attenuation rate is quantified, and then the disturbance tolerance index is generated through weighting, so that objective, comparable and interpretable comprehensive evaluation of the automatic driving performance of the intelligent networked automobile is realized.

The invention discloses an evolutionary game theory-based overtaking and lane changing behavior control method for an expressway interworking and interleaving area, and belongs to the technical field of intelligent network connection vehicle cooperative control. According to the method, firstly, a lane changing opportunity model is constructed based on vehicle motion state constraints and driving comfort conditions, and all lane changing opportunity points are obtained; through an evolutionary game model, the efficiency, the safety effectiveness and the traffic environment influence of the overtaking vehicle and the overtaken vehicle are integrated, and the optimal lane changing opportunity is screened; and finally, establishing a trajectory prediction model by utilizing model prediction control, and generating an optimal lane changing trajectory. According to the method, the integrated optimization of the lane changing opportunity and the track is realized, the speed fluctuation can be effectively inhibited, the longitudinal risk of the lane is reduced, the lane changing success rate, the passing efficiency and the driving safety of the expressway intercommunication interweaving area are remarkably improved, and the method is suitable for the lane changing control of various intelligent network connection vehicles in a complex interweaving environment, and has a relatively high engineering application value.

The invention provides an emergency call system, method and device based on AI vision and a storage medium, and relates to the technical field of active safety of intelligent network connection automobiles. The system comprises a camera module, a DMS module, an OMS module, an AI vision algorithm module, an AI vision application module and an eCall application module which work cooperatively. Aiming at the three defects that a traditional emergency call system depends on collision triggering, data dimensions are deficient and active protection is absent, DMS and OMS are innovatively and deeply integrated with the emergency call system, a multi-modal fusion decision mechanism is realized in combination with a layered AI visual architecture, and through deep cooperation of camera shooting, DMS, OMS, AI visual algorithm, AI visual application and an eCall application module, the multi-modal fusion decision mechanism of the emergency call system is realized. A full-link intelligent safety closed loop of sensing, decision making, control and rescue is constructed, early recognition, active intervention and accurate rescue of sudden diseases of drivers, serious fatigue disability and emergency physiological events of passengers are achieved, and meanwhile the rescue quality of collision accidents is improved.

The invention belongs to the technical field of Internet of Vehicles communication, and particularly relates to a joint resource allocation and relay selection optimization method of a networked vehicle cooperation system. The method comprises the following steps: firstly, constructing a double-hop cooperative relay model from a wireless access point to a vehicle and from the vehicle to the vehicle; secondly, considering constraints such as time delay of each link, and establishing an optimization model taking minimization of end-to-end time delay as a target; then, a double-layer optimization method combining resource allocation and relay selection is provided; the inner layer decouples an original problem into two sub-problems under any given relay strategy and solves a resource allocation problem by using a convex optimization method; the outer layer encodes each chromosome into a relay selection strategy through an improved genetic algorithm, and the output of the inner layer serves as a fitness value to solve the relay selection problem. According to the method, the end-to-end delay performance of the system is guaranteed, and meanwhile, the influence of the delay performance of each link on the system is considered, so that the reliability and the robustness of the network connection vehicle cooperation system in the urban dense scene are effectively improved.

A system for estimating a lane-level traffic jam is provided. The system includes one or more processors programmed to obtain information on lane changes of the vehicles in a road section including a traffic jam section, the road section including a plurality of lanes; collect driving data of the vehicles after the lane changes; estimate lane-level traffic jam distribution of the plurality of lanes based on the information on the lane changes and the driving data; and transmit the lane-level traffic jam distribution to vehicles approaching the traffic jam section.

The invention belongs to the field of secure communication in the Internet of Vehicles, and particularly relates to a lightweight certificateless signcryption method capable of proving security in the Internet of Vehicles, which comprises the following steps: constructing an Internet of Vehicles system consisting of a key generation center KGC, a roadside unit, a vehicle and a block chain, and initializing system parameters; the vehicle-mounted unit in the vehicle and the roadside unit outside the vehicle respectively register in the key generation center, and system parameters and pseudonyms are returned after KGC verification is passed; the KGC generates a part of keys for the pseudonym and returns the keys to the vehicle, and the vehicle calculates and generates a key and a public key according to the returned part of keys and the pseudonym, and uploads the public key and the pseudonym to the block chain; the vehicle signcrypts the message according to the key, obtains the public key of the receiver from the block chain according to the pseudonym of the opposite side, and then sends the signcryption message to the receiver; and the target vehicle or the roadside unit receives the message and performs message de-signcryption, and uploads a transaction record to the block chain for storage. According to the invention, while anonymous and secure communication of each entity in the Internet of Vehicles system is ensured, the communication efficiency is improved by reducing the vehicle calculation pressure and the communication overhead.

The invention relates to the technical field of intelligent network connection, in particular to a driving task arrangement generation method and device, a vehicle, a medium and a program product, and the method comprises the steps: obtaining task information and resource information of at least two tasks in driving task arrangement; determining at least one optimization target of task arrangement based on the task information and the resource information; and inputting the task information and the resource information into a pre-trained large language model to output initial task scheduling data meeting at least one optimization target, and verifying the initial task scheduling data until task scheduling data meeting a preset verification condition is obtained. Therefore, the problems that in the related technology, manual analysis is prone to errors, the management efficiency is low, and the actual use scene cannot be adapted are solved; model processing depends on a fixed rule, is lack of dynamic scheduling capability, increases response delay of a system, causes task timeout, and cannot guarantee reliability and safety of a driving task.

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.

Methods, systems, apparatus, and non-transitory computer readable media are described for using a vehicle as a payment device. Various aspects may include receiving a selection of a stored financial card or financial account at a vehicle head unit. The selected financial card or financial account may be transmitted to a point-of-sale (POS) terminal for making a payment by transmitting a tokenized card number to the POS terminal. The tokenized card number may be transmitted over a very short-range communication link to ensure that the transmission is secure. For example, electronic circuitry may be attached to the exterior of the vehicle, where the electronic circuitry may be within a threshold distance (e.g. one inch, three inches, six inches, one foot, three feet, etc.) of the POS terminal. The tokenized card number may be transmitted from the vehicle head unit to the electronic circuitry and then to the POS terminal.

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.

Systems and methods for externally communicating vehicular gaming status are disclosed herein. One embodiment of a gaming-status communication system detects automatically, in a connected vehicle, activation of a gaming mode of the connected vehicle in which a gaming system of the connected vehicle supports at least one of (1) electronic game playing by one or more occupants of the connected vehicle and (2) multi-vehicle networked electronic gaming involving the connected vehicle and one or more other connected vehicles. In response to detecting the activation of the gaming mode, the system activates automatically one or more external human-machine interface (HMI) indicators on the exterior of the connected vehicle to communicate, to other road users, information pertaining to the gaming mode of the connected vehicle.

The invention provides an intelligent network connection vehicle confluence area cooperative control system and method, and relates to the technical field of intelligent traffic systems. The method comprises the following steps: collecting vehicle trajectory data in a confluence area, and carrying out conflict risk quantitative evaluation and risk grade division based on TTC and PET composite indexes; a maximum value selection operator of a traditional game enhanced DQN is replaced by mixed strategy Nash equilibrium distribution, and multi-agent collaborative decision optimization is realized; predicting a vehicle trajectory based on a space-time attention mechanism, and generating a control scheme, a vehicle control command and an execution time sequence in combination with a risk level and a game strategy; and carrying out real-time interaction on information of the road side unit and the CAV vehicle, and pushing a control scheme, a vehicle control command and an execution time sequence to complete cooperative control of the intelligent network connection vehicle confluence area. The method overcomes the defects that in the prior art, vehicle control is conducted only according to a traditional reinforcement learning algorithm, learning is unstable, and strategy conflicts exist in the multi-agent environment.

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.

1. Name of the product of this design: Graphical user interface for monitoring vehicle network control systems for electronic equipment. 2. Purpose of this design product: This design product is used for an electronic device. 3. The key design point of this design product lies in the content of the graphical user interface. 4. The picture or photo that best illustrates the key points of the design: main view. 5. Purpose of the graphical user interface: Monitoring of the T-BOX control system in the connected vehicle network. 6. Human-computer interaction mode of the graphical user interface: The main view is the initial interface. Double-click the sample voltage SV: the interface change state diagram pops up in the box on the right to modify the parameters.

The invention discloses a local data security storage method for a non-networked vehicle, which comprises the following steps of: setting a vehicle-mounted MP5 (Mobile Pentium 5), and storing local data through the vehicle-mounted MP5; setting a three-level storage architecture for the vehicle-mounted MP5, and storing vehicle static information into a configuration data area; storing the operation parameters into a runtime data area according to a time sequence; storing the fault event into an event data area; implementing a hierarchical encryption strategy on data, performing full disk encryption on storage equipment, performing independent file encryption on sensitive data files, and implementing additional character encryption on key fields; an encryption key is derived based on a vehicle VIN code and a user PIN, a circular buffer management mechanism is established, and a secure data export interface is provided. According to the invention, the confidentiality, integrity and availability of vehicle local data can be improved, and the safety and reliability of vehicle data storage are improved.

A factor authentication system for a vehicle having a vehicle display, a telematics device, and a vehicle controller includes a backend server configured to communicate with the vehicle and a user electronic device via a network. The backend server is configured to receive a request from the user electronic device to link the vehicle to a use case, generate one or more random questions about vehicle signals that are visible on the vehicle display, send the one or more random questions to the user electronic device, receive, from the user electronic device, user answers to the one or more random vehicle signal questions, send the user answers to the vehicle controller; and receive, from the vehicle controller, confirmation that the user answers are correct based on a comparison to local vehicle data, to thereby authenticate the vehicle and authorize the link to the use case.

The invention discloses a compliance supervision platform design method based on a full life cycle of spatio-temporal data of an intelligent networked automobile, and belongs to the field of intelligent traffic, Internet of Vehicles and data governance, and the method comprises the steps: according to behavior characteristics of data activities, determining related data life cycle links, and obtaining meta-information of each data life cycle link; based on meta-information of each data life cycle link, a risk perception module is introduced, and the risk perception module comprises a rule engine and a behavior analysis model and is used for recognizing and judging high-risk behaviors in the process in real time; fusing the preliminary risk score and the identification score to obtain a comprehensive risk score; and when the comprehensive risk score exceeds a preset alarm threshold value, automatically triggering an early warning process, and pushing the early warning process to an industry supervision department. According to the method, roles can be configured, review can be traced, and behaviors can be traced back, a landing and extensible technical path is provided for intelligent network connection automobile spatio-temporal data compliance management, and the industry compliance management level is improved.

The invention belongs to the technical field of vehicle control, and discloses a networked automobile CAN bus control method and system, and the method comprises the steps: analyzing multi-source data of a networked automobile, constructing a multi-level control architecture based on an analysis result, setting a condition threshold value between control levels, and employing a genetic algorithm to dynamically optimize the condition threshold value as a control threshold value; constructing a threshold prediction model by using a time sequence prediction algorithm, predicting the state and operation scene change of an electronic control unit in the networked automobile by combining the obtained historical multi-source data, and generating a control prediction threshold; and in combination with the control threshold value and the control prediction threshold value, generating a cooperative control value through a cooperative recommendation strategy, and formulating a corresponding control strategy. According to the invention, a multi-level control architecture is constructed through multi-source data analysis, a communication threshold is dynamically optimized in combination with a genetic algorithm, the CAN bus load is reduced, the communication efficiency and real-time performance are improved, key message delay is avoided, and the stability and resource utilization capability of a vehicle-mounted network are enhanced.

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.

The invention provides a multi-modal interaction method based on an intelligent networked automobile vehicle-mounted interaction practical training platform and a practical training platform, and the method comprises the steps: responding to an interaction instruction of an automobile vehicle-mounted human-computer interaction interface, and obtaining multi-modal data based on the interaction instruction; constructing user interaction space coordinates based on the voice interaction data and the gesture interaction data; performing interference analysis and screening on the voice interaction data, the gesture interaction data and the touch interaction data based on the vehicle driving state to obtain target interaction data; fusing the target interaction data based on the user interaction space coordinates to obtain fused feature data; and classifying and identifying the fused feature data to obtain a target interaction instruction, and triggering the controlled object to execute an instruction operation based on the target interaction instruction. According to the method, the interference among interaction modes such as voice, gestures and touch in a complex real scene is reduced, the condition of signal confusion is reduced, and the problem of high identification difficulty caused by different user characteristics during simultaneous practical training of multiple persons is solved.

The present invention discloses a three-dimensional roadside sensor deployment method for collaborative perception in the Internet of Vehicles (IoV). The method comprises: modeling a three-dimensional road environment using an RSNs model; constructing a perception detection function for a single sensor based on the RSNs model; and constructing a global sensor detection function for each deployable area based on the perception detection function of the single sensor when multiple sensors are present. The method then optimizes the first objective, maximizing the proportion of deployable areas that meet or exceed a minimum detection probability according to the global sensor detection function, and minimizing the total number of sensors in each deployable area as a second objective. The method then constructs a deployment optimization reward function composed of the first and second objective optimization problem functions. The method then constructs a decision sequence and solves the decision sequence using a decision transformer to obtain a deployment strategy. The method further implements sensor deployment in a three-dimensional road environment.

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.