10154 results about "Driving safety" patented technology

The invention provides a vehicle-mounted video monitoring system and a vehicle-mounted video monitoring method integrating 360-degree look-around perception and multi-mode intelligent analysis. According to the system, video images around a vehicle are collected through a vehicle-mounted 360-degree all-round camera, high-precision vehicle position information is obtained through a vehicle-mounted multi-sensor fusion positioning technology, videos and position time are stored in an associated mode, and a space-time video database is constructed. The system utilizes a multi-mode perception fusion technology to intelligently analyze a video, extracts semantic information, and supports a user to quickly retrieve a target video clip through natural language interaction. Meanwhile, the system can monitor abnormal driving behaviors and vehicle surrounding environment risks in real time and trigger early warning when necessary. According to the invention, advanced technologies such as 360-degree looking-around perception, multi-sensor fusion positioning, multi-modal intelligent analysis, natural language interaction and the like are innovatively and deeply fused with vehicle-mounted video monitoring, so that the limitation of a traditional vehicle-mounted monitoring system is broken through, the intelligent and humanized level is greatly improved, the development direction of a vehicle-mounted video monitoring technology is guided, and the vehicle-mounted video monitoring system has a wide application prospect. The method is of great significance in guaranteeing driving safety of intelligent automobiles and promoting development of intelligent traffic.

Disclosed in the present invention are a multi-agent vehicle-road-cloud integrated collaborative decision-making and control architecture system and method based on federated reinforcement learning. A multi-agent federated reinforcement learning decision-making and control framework having embedded vehicle dynamics characteristics is used, so as to solve the problem of in-depth integration of an intelligent traffic system and intelligent vehicles, and realize autonomous driving with vehicle-traffic in-depth decision-making and control collaboration; a semantic matrix is generated at a road side to serve as an input for vehicle-side reinforcement learning, so as to construct vehicle-side global and local trajectory planning guided by the road side; an integrated reward function for vehicle-side reinforcement learning is designed on the basis of a driving safety field constructed by the road side, so as to realize comprehensive consideration of vehicle-side safety and comfort; on the basis of road-side federated learning, vehicle-side neural network parameters are uploaded by means of V2I communication, so as to solve the problem of vehicle-road information asymmetry caused by privacy awareness; and for different environmental sample distributions, a local optimal policy for a current environment is selected by means of neural network screening, so as to synthesize a shared model benefiting from different environments, thus realizing a balance between sample efficiency and model robustness.

The invention provides an intelligent driving scene adaptive teaching method and system based on reinforcement learning, and relates to the technical field of reinforcement learning, and the method comprises the steps: training a plurality of scene recognition models based on historical data, carrying out the scene classification of the historical data, and generating a training sample set with labels; and training by using a reinforcement learning algorithm to obtain an intelligent driving decision model for different scene types. In the actual driving process, the current driving scene is recognized according to the information such as the vehicle position, the vehicle speed and the acceleration which are collected in real time, and the corresponding decision model is selected to output the standard driving behavior sequence. And comparing the actual behavior of the driver with the standard behavior, calculating a deviation value, determining a teaching feedback strategy according to the deviation value, outputting driving guidance information in real time, and using the feedback of the driver to update the decision model. Personalized teaching can be provided according to different driving scenes, the driving skill of a driver is improved, and the driving safety is enhanced.

The invention relates to the technical field of traffic safety management, in particular to a vehicle abnormal driving recognition and early warning method and system. The method comprises the following steps: firstly, acquiring vehicle position, speed, direction and driver operation data in real time, then correcting time sequence data by adopting a smooth denoising method of adaptive segmented clustering, generating an information confirmation code through standardization, and then carrying out weighted fusion on multi-source data to obtain a multi-source data fusion model; the strategy decision-making module is used for extracting vehicle speed change rate, lane deviation degree and driver behavior characteristic key indexes, identifying and grading abnormal driving behaviors by setting a threshold value, calculating a comprehensive abnormal score, and generating a grading intervention strategy for slight, medium and serious anomalies according to a comparison result of the abnormal score and the set threshold value. And finally, combining automatic intervention in the vehicle with global coordinated intervention of a road manager. According to the invention, the intervention strategy is generated based on multi-source data acquisition, processing, analysis and decision, so that the vehicle driving safety and the road management efficiency are improved.

The invention provides a driving behavior analysis method and system based on multi-modal sensor fusion, and the method comprises the steps: firstly obtaining a multi-source sensor data set of a target vehicle containing at least two different sensor source driving environment perception data, and carrying out the time-space alignment processing of the multi-source sensor data set, and generating a multi-source fusion data set; then, through a feature extraction layer of a preset behavior analysis network, a driving behavior feature set containing vehicle operation, environment interaction and behavior continuity features is extracted from the multi-source fusion data set, an anomaly recognition layer is used for carrying out anomaly recognition on the driving behavior feature set, and an anomaly recognition result is generated; and finally, generating driving strategy optimization data according to an abnormal recognition result, and feeding back the driving strategy optimization data to a vehicle control system to adjust a driving strategy. According to the method, driving behaviors can be comprehensively analyzed, abnormity can be timely found, the driving strategy is optimized, and the driving safety and efficiency are improved.

The invention provides a vehicle-mounted display terminal man-machine interface interaction experience optimization method and system, and relates to the technical field of vehicle-mounted man-machine interaction, and the method comprises the steps: obtaining eyeball watching, hand operation and head rotation data of a driver, and carrying out the fusion to generate an attention distribution thermodynamic diagram; constructing a dynamic scene recognition model in combination with the vehicle running state and the environment information; and calculating a function module optimization index based on the thermodynamic diagram and scene prediction, and dynamically adjusting the position, the response area and the display level of the function module. According to the invention, the interaction efficiency of drivers can be improved, the operation distraction risk is reduced, and the driving safety is enhanced.

The invention relates to the technical field of data processing, in particular to a data model processing method and system. The method comprises the following steps: acquiring multi-modal vehicle-mounted sensing data, and performing vehicle environment sensing to obtain dynamic vehicle environment sensing data; behavior pattern modeling is carried out through a vehicle-mounted sensing network, and a driver behavior prediction model is obtained; performing driving scene risk assessment based on the driver behavior prediction model and the dynamic vehicle environment perception data to obtain driving scene risk assessment data; performing vehicle multi-level early warning strategy analysis according to the driving scene risk assessment data to obtain a vehicle early warning instruction set; performing driver behavior prediction through a vehicle-mounted sensing network to obtain real-time early-warning driver behavior prediction data; and carrying out vehicle emergency intervention measure automatic decision making on the real-time driving early warning scene simulation data to obtain an emergency intervention decision execution instruction set. According to the invention, the driving safety and the response efficiency can be improved.

The invention discloses a wireless control method for LED illumination, a dimming device, a medium and a product, and relates to the technical field of electric illumination, and the method comprises the steps: obtaining tunnel illumination control parameters; dividing the tunnel entrance area into an entrance section, a buffer section and a basic section according to the tunnel parameters, and determining a plurality of illumination control subareas; calculating the brightness adaptation time of a vehicle driver based on the external brightness value and the vehicle passing speed, and generating an illumination gradient curve from the entrance section to the buffer section and the basic section; according to the illumination gradient curve, determining a target illumination value of each illumination control zone; converting the target illumination value into an LED driving parameter, and issuing the LED driving parameter to an LED driving circuit of each illumination control subarea; and collecting actual illumination data of each illumination control subarea, and regenerating an illumination gradient curve when the actual illumination data exceeds a preset target range or the external brightness value changes. By implementing the method, tunnel illumination can be adjusted based on illumination changes when the weather changes drastically, and driving safety is guaranteed.

The invention provides an electric two-wheeled vehicle collision detection and early warning method and related equipment, and the method comprises the steps: obtaining environment information in front of and behind a vehicle, the environment information comprising point cloud data collected by a radar sensor and image data collected by a camera; performing time synchronization and space coordinate conversion processing on the point cloud data and the image data, and constructing a sensing data frame under unified space-time reference; based on the sensing data frame, radar features and visual features of the target object are extracted respectively, feature fusion is carried out to form a joint feature vector, target matching and association are carried out based on the joint feature vector, and a fused obstacle sensing result is generated; and based on the obstacle sensing result, in combination with the current motion state of the vehicle, predicting and evaluating a potential collision risk, determining a collision risk level, and executing a corresponding early warning prompt according to the collision risk level. And the driving safety and the system response efficiency of the electric two-wheeled vehicle in a complex traffic environment are integrally improved.

The invention discloses a road intelligent induction and dynamic early warning method and system integrated with meteorological perception, and relates to the technical field of intelligent traffic and road safety. The method comprises the following steps: acquiring real-time weather, traffic and road data, performing multi-source data fusion by adopting an improved Kalman filtering and attention mechanism, and generating unified state estimation; dynamic risk assessment is carried out in combination with Bayesian reasoning and a Markov model, and speed-limiting adaptive adjustment is realized based on safety, traffic efficiency and energy consumption multi-objective optimization; and further calculating the length and position of the dynamic early warning area, and controlling devices such as intelligent spikes to issue induction information. The system comprises a data acquisition unit, a fusion estimation unit, a risk prediction unit, a speed adjustment unit, an early warning calculation unit and an induction unit. According to the invention, real-time monitoring, risk prediction and intelligent regulation and control of the road traffic environment in complex weather are realized, and the driving safety and the traffic efficiency are improved.

The invention discloses an intelligent cabin interaction method and system combined with digital twinning, and relates to the technical field of man-machine interaction, and the method comprises the steps: collecting the multi-dimensional perception data of a cabin, and constructing a layered cabin digital twinning model; performing scene analysis on the cockpit digital twin model, extracting driver state features, predicting driver potential interaction demands, generating a multi-modal interaction strategy set in combination with the driver potential interaction demands and a current driving scene, and performing simulation evaluation on the multi-modal interaction strategy set to obtain an interaction evaluation index set; performing multi-objective decision optimization under the driving safety constraint based on the interaction evaluation index set, and determining and adaptively executing an optimal interaction strategy; and acquiring actual execution data and driver feedback data of the optimal interaction strategy, and incrementally updating the hierarchical cabin digital twin model. According to the invention, the interaction adaptability and the safety reliability of the intelligent cabin system in a complex driving scene are obviously improved, and an effective technical support is provided for realizing intelligent man-machine cooperative driving.

The invention relates to the technical field of intelligent driving, and discloses an intelligent networked automobile multi-modal sensing and decision-making system based on deep learning, and the system comprises a multi-modal data collection module which is used for collecting image data and radar time sequence data around a vehicle through a vehicle-mounted camera and a vehicle-mounted radar sensor, the surrounding environment of the vehicle can be sensed in all directions; the image data processing module is used for processing the image data by using an improved deep learning model and extracting target object feature information in the image; the radar data processing module is used for analyzing the change trend of the radar time sequence data and making an optimal driving decision according to different traffic scenes in combination with the feature information of the target object; and the optimal decision execution module is used for executing an optimal decision action according to the optimal driving decision. According to the invention, high-precision perception and quick response to environment information in a complex traffic scene can be realized, the driving safety is improved, and the traffic efficiency is optimized.

The invention provides a highway tunnel intelligent illumination control method and system, and the method comprises the steps: collecting a real-time perception data set of a plurality of monitoring nodes in a target tunnel, covering a light source operation parameter, an illumination intensity parameter and an environment state parameter sequence, carrying out the feature extraction of the real-time perception data set, and carrying out the feature extraction of the real-time perception data set; generating a global feature set including light source operation stability, regional illumination coordination and environmental coupling influence features, and then inputting the global feature set into a preset dynamic decision model for strategy decision to obtain a multi-stage illumination regulation and control strategy set covering different regions, including target brightness, dimming time sequence and equipment cooperation parameters; and finally, generating an executable instruction set according to the multi-stage illumination regulation strategy set, issuing the executable instruction set to each partition illumination control terminal, and triggering adaptive dimming of the illumination equipment, thereby realizing intelligent management and control of tunnel illumination, improving an energy-saving effect and illumination quality, and ensuring driving safety.

The invention relates to the technical field of electric vehicle control, and provides an intelligent new energy locomotive power distribution and energy recovery control system and method. The system comprises a multi-source information sensing layer, an intelligent decision control layer and an execution feedback layer. The multi-source information sensing layer collects the state of a vehicle and external environment data. The intelligent decision control layer generates a power distribution and energy recovery instruction through data fusion and preprocessing, fuzzy logic assistance, reinforcement learning decision and cooperative work of a multi-objective optimization module; and the execution feedback layer executes the instruction, monitors feedback in real time, and optimizes related actions. According to the method, advanced technologies such as reinforcement learning and fuzzy logic are fused, complex and changeable driving scenes are accurately dealt with, self-adaptive optimization of power distribution and energy recovery strategies is achieved, the energy recovery efficiency is improved, power distribution is accurate and efficient, dynamic adjustment can be achieved according to driving intentions and working conditions, the driving safety and comfort are guaranteed, and meanwhile the driving efficiency is improved. And efficient utilization of energy is realized.

The invention discloses a driving behavior risk assessment method and system, and relates to the technical field of intelligent traffic and driving safety, and the method comprises the steps: collecting the operation parameter data of a vehicle, carrying out the feature extraction of the operation parameter data, extracting feature parameters to form a feature vector, and calculating the Mahalanobis distance score of the feature vector; according to the Mahalanobis distance score of the feature vector, carrying out risk grade division by adopting an improved hierarchical fuzzy inference system to obtain a risk grade score; and establishing a driver portrait model according to the risk level score, and when the risk level score exceeds a personalized risk threshold, outputting a driving risk behavior improvement suggestion to complete driving behavior risk assessment. According to the invention, a refined grading basis is provided for individual risk monitoring of the driver, dynamic monitoring and real-time intervention of driver behaviors are realized, traffic accidents can be reduced, and driving safety is improved.

The invention relates to the field of monitoring and early warning, in particular to a dynamic beam radar monitoring and linkage early warning method and system for a layered side slope of an expressway, and the method comprises the steps: carrying out the layered scanning of a layered geologic structure of the side slope, synchronously obtaining a phase coherent echo signal, carrying out the multi-threshold scattering point extraction, and carrying out the multi-threshold scattering point extraction; through analysis of a prior constraint model and a geological vegetation recognition network, interference signals caused by vehicle passing are eliminated to obtain a space-time coherent scattering point set, the space-time coherent scattering point set is input to a deformation calculation assembly line, differential interference measurement, adaptive atmospheric disturbance correction and slope structure parameter inversion are executed, and a displacement data stream is generated. And carrying out space-time correlation analysis on the layered inclination angle time sequence data and the meteorological and hydrological data, updating a slope stability evaluation index through a dynamic baseline, and generating an early warning instruction. According to the invention, a technical system from interference suppression and precise calculation to decision closed loop is formed, high reliability of road slope monitoring results is ensured, and intelligent support is provided for traffic safety and emergency management and control.

The invention discloses a safety analysis method and system applied to a complex driving scene of a storage robot, and the method comprises the steps: obtaining driving monitoring information, sensing the driving environment of a target storage robot according to the driving monitoring information, and obtaining environment sensing information; carrying out dynamic obstacle detection based on the environment perception information, detecting a dynamic obstacle in a real-time driving environment of the target storage robot, and carrying out motion track prediction on the detected dynamic obstacle to obtain track prediction information; scene risk modeling is carried out according to the environment perception information and the action track information, and the driving risk of the target storage robot according to the preset driving track is analyzed; when it is detected that a driving risk exists in the preset driving track, the driving track is adjusted in combination with the environment perception information and the action track prediction information, and a driving strategy is generated for driving assistance. And the accuracy and intelligence of driving safety analysis of the AGV storage robot in a complex dynamic environment are improved.

The invention discloses a vehicle-mounted light source automatic adjustment control method and system based on environment perception, and relates to the technical field of vehicle control, and the method comprises the steps: collecting vehicle data and surrounding environment data, and extracting perception features; constructing embedded features according to the content of the sensing features, respectively calculating classification loss and correlation loss of the embedded features, generating an initial light source adjustment item based on the sensing features, and optimizing the initial light source adjustment item according to the embedded features to generate a light source adjustment scheme; acquiring driving data of a driver, judging a driving state and adjusting an in-vehicle light source according to the driving state; and collecting feedback data of a driver for the light source adjustment scheme to optimize the light source adjustment scheme, and generating and storing an optimization record. The adaptive capacity and response capacity of the vehicle-mounted light source to the environment are effectively improved, the adjusting flexibility of the vehicle-mounted light source is improved, the vehicle-mounted light source is synchronously adjusted by recognizing the state of a driver, and the driving safety is effectively improved.

The invention discloses a fatigue driving monitoring and early warning system based on adaptive learning, and the system comprises a data processing module which is used for collecting and preprocessing driving data; the facial feature module is used for constructing a facial key point dynamic trajectory graph; the physiological feature module is used for extracting a heart rate multi-order modal component and a skin electric energy disturbance factor; the behavior characteristic module is used for extracting a periodic disturbance degree, a lane offset curvature fluctuation range and a control rhythm index; the feature fusion module is used for integrating multi-source information and carrying out time domain modeling; the recognition updating module is used for constructing an individualized recognition model and dynamically updating model parameters; the fatigue evaluation module is used for evaluating a fatigue state and generating a corresponding grade output signal; and the early warning intervention module is used for triggering voice prompt, seat vibration or visual prompt according to the output signal. According to the invention, real-time identification and intelligent intervention of the driving fatigue state are realized, and driving safety and response efficiency are improved.

The invention relates to the technical field of vehicle control, in particular to an unmanned vehicle control method based on deep learning, and the method comprises the following steps: collecting the multi-modal data of weak participants in a road environment in real time, and recognizing the weak participants; the behaviors of the weak participants at the next moment are predicted through multi-dimensional information fusion, and behavior intention prediction of the weak participants is carried out; the path planning and control strategy of the vehicle is adjusted in real time through the self-adaptive decision control module; and when the predicted behavior intention of the weak participant has high uncertainty or potential risk, an emergency avoidance mechanism is automatically triggered, the risk level of the current traffic environment is dynamically evaluated, an optimal avoidance path is generated, and emergency stop or detour operation is executed. According to the method, double guarantees of high-dimensional analysis of behavior intentions of participants and real-time control of vehicles are combined, and the driving safety in a complex traffic scene is ensured.

The invention discloses a vehicle fault diagnosis method and device and a vehicle, and aims to detect common cause failure of a wheel speed signal and ensure the safety of the vehicle in a fault state. Comprising the following steps: acquiring a sensing signal of a vehicle dynamic sensor assembly, generating dynamic data of a vehicle, and performing consistency processing on the dynamic data to calculate an acceleration deviation value; whether the acceleration deviation value exceeds a preset fault threshold value or not is judged, and if yes, the exceeding frequency is recorded; and in a preset diagnosis time period, if the number of times exceeding the fault threshold value is larger than a preset number-of-times threshold value, a power system of the vehicle is triggered to degrade, and an alarm is given out. According to the invention, the potential fault of the vehicle power system can be timely and accurately detected, the potential safety hazard caused by the fault is reduced, and the reliability and safety of vehicle operation are improved. According to the invention, the abnormity of the wheel speed signal under the common cause failure condition can be effectively detected, degradation and alarm measures are taken in time, and the driving safety of the vehicle is guaranteed.

The invention discloses a high-performance low-cost multi-modal data fusion model and equipment for complete driving environment state perception, and the model comprises a feature extraction module which is used for extracting initial potential features of a camera image and a laser radar point cloud; a probability cross attention mechanism constructs a feature fusion model of uncertainty perception; a query initialization model capable of reducing uncertainty is adopted, and the initialization process of query is enhanced by utilizing two-dimensional target detection priori; and based on a robustness optimization training strategy of query denoising, stable optimization is realized under uncertainty. A nuScenes data set is selected for training, verifying and testing the comprehensive performance of the model, and the TensorRT realizes CUDA acceleration and is quantitatively deployed on a vehicle-mounted calculation experiment platform. According to the multi-modal data fusion system, a perception state uncertainty explicit measurement calculation theory based on data and a model is innovated, a perception model for coping with environment uncertainty is established, the completeness, reliability and reconfigurability requirements of a driving environment are met, and driving safety and decision accuracy are ensured.

The invention belongs to the field of vehicle early warning, and particularly relates to a vehicle hidden danger recognition and safety early warning method and device, and the method comprises the following steps: collecting vehicle speed, tire pressure and road condition data, obtaining the states of key parts of an engine and a braking system through combining an OBD system, and achieving the dynamic perception of a whole vehicle; based on a deep learning algorithm, accurately identifying the too short distance of the front vehicle, fatigue driving, overspeed, area deviation, long-term left-occupying driving of a lane, vehicle retrograde driving and abnormal line pressing tracks; through vehicle-mounted OBD data and cloud large model analysis, potential mechanical faults of tire wear and brake pad aging are predicted, and maintenance suggestions are pushed in advance, so that a driver can be reminded of safety, the driving safety is improved, the hidden danger recognition capability is improved, safety preventive warning is performed on hidden dangers, the driving safety is ensured, and the driving safety is improved. Meanwhile, the vehicle can be guided to avoid high-risk or forbidden road sections, and the probability that the vehicle enters a dangerous or forbidden area is reduced.

The invention provides a vehicle seat self-adaptive adjusting method and device, equipment and a medium, and the method comprises the steps: obtaining current driving scene information, inputting the current driving scene information into a seat adjusting self-adaptive model, so as to obtain a seat adjusting parameter corresponding to the current driving scene information, the seat adjustment adaptive model obtaining method comprises the steps of constructing an initial neural network model, training the initial neural network model based on historical driving scene information to obtain the seat adjustment adaptive model, and finally adjusting a vehicle seat based on seat parameters corresponding to current driving scene information. The self-adaptive adjustment of the vehicle seat is realized; according to the method, the association relationship between the driving scene information and the seat adjustment parameters is established, so that the seat adjustment parameters strongly related to the current scene information are obtained based on the current scene information, seat adjustment is more suitable for the driving environment, and the driving safety and comfort are effectively improved.

The invention discloses a road safety early warning method and system based on a mixed precision quantification visual large model, and the method comprises the steps: collecting road traffic safety videos and pictures, and carrying out the preprocessing, data enhancement and marking, thereby forming a diversified data set; a pre-trained visual large model is selected as a teacher model, after fine tuning, output layer and middle layer knowledge is extracted, key features are weighted, and meanwhile, a lightweight neural network is taken as a student model, same input is received, and prediction and middle feature maps are output. And inputting data into the two models and the student model to carry out mixing precision quantification forward propagation, constructing a total loss function containing tasks, knowledge distillation and quantification learning loss, and updating parameters through back propagation. And after training is completed, exporting a quantitative model, and deploying the quantitative model to an edge computing platform to realize safety early warning. The lightweight model can realize rapid reasoning on edge equipment such as a vehicle-mounted road side, and the problem that performance and efficiency are difficult to consider in a traditional model compression method is solved.

The invention relates to a multi-scene self-adaptive high-speed obstacle early warning method and device, equipment and a medium. The method comprises the following steps: firstly, acquiring multi-source data of traffic sensing equipment, and carrying out space-time calibration and semantic analysis on the data to obtain a vehicle track feature vector set; thirdly, calculating real-time traffic flow parameters of the gridding area according to the feature vector set, and further generating a traffic feature database; secondly, modeling is carried out on the traffic characteristic database according to time periods and weather, and theoretical speed baseline parameters of all scenes are obtained; and finally, constructing a multi-dimensional feature matrix according to the theoretical speed baseline parameters, inputting the multi-dimensional feature matrix into the classification model to calculate an abnormal probability value, and if it is detected that a continuous abnormal probability value exceeds a dynamic threshold value in a preset time window, generating an early warning signal. By adopting the method, real-time monitoring and early warning of obstacles in multiple scenes of the expressway can be realized, and more effective guarantee is provided for driving safety of the expressway.

The invention belongs to the technical field of vehicle state detection and early warning, and particularly relates to a vehicle state monitoring and early warning method and system.The monitoring and early warning method comprises the steps that a sensor obtains real-time data, an anomaly detection algorithm is applied, and an abnormal event is marked; calculating an information priority according to the abnormal severity and the driving scene, and distributing the information priority to a high-priority queue; multi-mode early warning is generated, and high-frequency sound and vibration are used during high-speed driving; extracting a voice prompt, and generating voice waveform data through a voice synthesis module; voice input of a driver is recognized, intention is analyzed, and abnormal information feedback is provided; according to the scene and the abnormal state, interactive output is optimized, and detailed information is displayed during low-speed congestion; dynamically adjusting the interface of the instrument panel, and amplifying the key area in case of abnormal severity; and integrating the image data and the voice data, generating a multi-mode signal, and transmitting the multi-mode signal after rendering processing. The vehicle abnormal information can be timely and accurately transmitted to a driver, and the driving safety is effectively improved.

The invention relates to the technical field of remote monitoring, in particular to a wireless vehicle state monitoring system which comprises a state change evaluation module, a data transmission adjustment module, a power anomaly detection module, a health state evaluation module and a risk early warning optimization module. According to the method, the vehicle operation parameters are obtained, the change amplitude and the rate of the parameters are analyzed in multiple time windows, the change amplitude and the rate are compared with the preset threshold values, the transmission frequency is adjusted according to the stability of the state, important data are updated in time, and the abnormality of the power system is detected by analyzing the data transmitted in real time. By matching specific data of engine torque and gear shifting modes, prediction of potential faults of the vehicle is more accurate, data of main components of the vehicle are integrated to be compared with health standards, accurate evaluation of the overall health state of the vehicle is achieved, risk early warning can be given out in advance, driving safety is optimized, and the driving safety is improved. And the service life and the operation efficiency of the vehicle are improved.

The invention discloses a multi-vehicle cooperative driving control method and system for unmanned vehicles, and relates to the technical field of intelligent driving control, and the method comprises the steps: carrying out the multi-dimensional sensing of a plurality of unmanned vehicles, and obtaining a multi-dimensional sensing data set; environment modeling is carried out, and a dynamic traffic scene model is constructed; multi-vehicle path conflict analysis is carried out through the multi-vehicle cooperation platform, and a first path combination is generated; optimizing the first path combination according to the plurality of single-trajectory tracking results to generate a second path combination; and performing multi-vehicle intelligent cooperative driving control on the unmanned vehicle based on the multi-vehicle cooperative strategy. The technical problems that in the prior art, the environment sensing range of a single vehicle is limited, communication between vehicles is insufficient, complex and changeable traffic scenes are difficult to deal with, and consequently the multi-vehicle cooperative driving efficiency and safety are poor are solved, intelligent control over the unmanned vehicle in the dynamic traffic scene is achieved, and the driving safety is improved. The technical effect of improving the multi-vehicle cooperative driving efficiency and the driving safety is achieved.

The invention relates to an automatic driving forced lane changing decision-making method based on a dynamic continuous game, which belongs to the technical field of intelligent connected automobiles and comprises the following steps: acquiring motion state information of a self-vehicle and surrounding vehicles in a forced lane changing scene through a vehicle-mounted sensor and vehicle-to-vehicle communication; calculating a collision boundary; generating an initial decision based on non-cooperative non-zero and dynamic games; optimizing the initial strategy by adopting a game strategy based on information continuity; and outputting a final forced lane change decision under the condition that the strategy after game decision optimization based on information continuity is continuously kept for three periods. According to the automatic driving forced lane changing dynamic game decision-making method considering decision-making continuity, the decision-making model is constructed by adopting non-cooperative non-zero and dynamic games, so that the driving safety, comfort and efficiency are improved; through a dynamic game algorithm based on decision continuity, decision adaptability and predictability are enhanced, stability and consistency of decision output are ensured, and uncertainty and risk are reduced.