631 results about "Driving risk" patented technology

The invention provides a risk control big data mining method and system based on the Internet of Vehicles, and the method comprises the steps: collecting a real-time operation data flow of a target vehicle in an Internet of Vehicles environment, including vehicle state time sequence data, a driving behavior event set, external environment perception data and a vehicle-mounted communication interaction record; and then respectively extracting a combined operation feature vector, an abnormal behavior mode identifier and a dynamic environment risk factor from different data, converting to generate a structured communication feature matrix, constructing a space-time association risk analysis map based on the structured communication feature matrix, and carrying out driving risk dynamic assessment on a target vehicle through the space-time association risk analysis map. And finally, generating a risk control decision instruction set according to the result, and feeding back the risk control decision instruction set to the vehicle-mounted control terminal and the Internet of Vehicles cloud platform, thereby realizing comprehensive assessment and effective management and control of vehicle risks.

The invention discloses a container wharf hazardous chemical transport vehicle intelligent tracking and positioning system comprising a data acquisition module used for acquiring vehicle positioning, driving behaviors and environment state data; the data fusion and time synchronization module is used for performing time alignment on the multi-source data; the behavior modeling and trajectory prediction module is used for extracting driving behavior characteristics and predicting a driving trajectory; the risk assessment and anomaly detection module is used for calculating an anomaly score and predicting a driving risk; the security strategy optimization module is used for constructing a zero-sum game security control model and optimizing speed limit, path and early warning strategies; the remote control and intelligent intervention module is used for executing remote speed regulation, emergency braking, path optimization and alarm pushing in the intelligent dispatching system; and the cloud monitoring and data analysis module is used for storing a track, an environment, a driving score and a safety strategy and predicting a long-term risk trend. According to the invention, based on intelligent game and multi-modal fusion, accurate tracking and safety management and control of the hazardous chemical substance transport vehicle are 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 technical field of intelligent traffic, and discloses a road transportation key operating vehicle intelligent supervision system, which comprises a data acquisition and fusion module used for acquiring multi-source heterogeneous data of a vehicle terminal, an external environment and the like in real time and carrying out standardization processing; the cognitive digital twinning construction module is used for constructing a dynamic heterogeneous graph representation human-vehicle-road-environment system and outputting a cognitive state vector through a graph neural network; the risk prediction and evaluation module is used for predicting a risk evolution trend based on the state vector sequence and quantifying the uncertainty of prediction by using a Monte Carlo discarding method; and the self-adaptive intervention decision module is used for combining risk prediction and uncertainty, making a decision through a reinforcement learning model and executing an optimal active intervention instruction. By constructing cognitive digital twinning and introducing uncertainty quantification and closed-loop self-checking, prospective prediction and self-adaptive intervention of driving risks are realized, and the accuracy and robustness of supervision are improved.

The invention relates to the technical field of driver behavior recognition, in particular to an intelligent behavior monitoring and recognition system for coping with a vehicle driver, which comprises a multi-mode driver data information module used for acquiring first driver multi-dimensional state information in a preset historical driving period, extracting and generating a first dangerous behavior feature set and a first dangerous response feature set according to the multi-dimensional state information of the driver; the environment information acquisition module is used for acquiring first driving environment interaction risk information in a preset historical driving period; a risk assessment model construction module; a real-time data evaluation module; according to the invention, the dangerous behavior of the driver is identified by combining the multi-dimensional state information of the driver with the environmental information, and the parameters of the dangerous behavior risk assessment model are continuously updated through the real-time data, so that the accuracy of monitoring and identifying the behavior of the vehicle driver is improved; meanwhile, high-risk behaviors can be obtained in advance, and the driving risk is prevented.

The invention discloses a child electric motorcycle intelligent safety control system based on the Internet of Things, and relates to the technical field of electric motorcycle safety control. The problem of insufficient driving risk perception and control response of children is solved. The method comprises the following steps: firstly, fusing obstacle trajectory prediction, a road adhesion coefficient and an environment contour, constructing a dynamic risk vector field, and generating a collision probability and out-of-control coefficient fused risk assessment matrix; then, the matching degree of handle operation and vehicle body response is calculated through cross attention, and a third-level behavior label is output; according to the behavior level and the risk direction, anti-sideslip control or progressive power limitation is executed, and intervention characteristics are recorded; and finally, constructing a virtual training task by using parent operation data, extracting a personalized control threshold, dynamically optimizing system parameters, and realizing a child driving behavior-oriented closed-loop safety control system.

The present disclosure discloses an anthropomorphic lane-changing control method and system based on driving risk quantification, and a vehicle. The method includes: determining whether a main vehicle has a lane-changing intention based on motion statuses of the main vehicle and a front vehicle in a current lane; if there is a lane-changing intention, generating an anthropomorphic lane-changing trajectory that satisfies safety constraints and reachability constraints; based on a motion status of a traffic participant that causes a risk to the main vehicle, calculating an overall risk level and determining whether the anthropomorphic lane-changing trajectory satisfies risk constraints; and if the risk constraints are not satisfied, regenerating an anthropomorphic lane-changing trajectory; or if the risk constraints are satisfied, performing tracking control based on a preview-following theory. In the present disclosure, a lane-changing intention is described and identified based on actually measured data, to generate an anthropomorphic lane-changing trajectory.

The invention relates to an automatic driving risk quantification method based on a conflict risk field. Comprising the steps of 1, constructing a basic risk field; step 2, under a basic risk field framework, constructing a conflict risk field by taking an ADV as a center; step 3, regarding the conflict risk field as a repulsive force acting on the ADV, and using the repulsive force to quantify the dynamic influence of the environmental elements and various TP states on the driving safety of the ADV; the method comprises the following steps: firstly, constructing a basic risk field on the basis of traffic vehicle distribution and motion characteristics, further forming a vehicle forward, lateral, backward and traffic regulation constraint multi-dimensional conflict risk field on the basis, and converting an abstract traffic conflict relationship into a computable repulsive force model; dynamic quantitative characterization of driving risks in a state dimension, a space dimension and a time dimension is realized, and continuous optimization and evolution of an end-to-end automatic driving algorithm are supported by means of a repulsive force model constructed based on a conflict risk field.

The invention relates to the technical field of risk assessment, in particular to a safe driving assessment method and system based on Beidou navigation. The method comprises the following steps: acquiring corresponding real-time position, speed and driving track data in a vehicle driving process through a Beidou navigation module, and acquiring vehicle state data and environment sensing data to obtain a vehicle multi-dimensional driving state data set; performing vehicle space-time and driving operation feature analysis and driving road matching analysis on the vehicle multi-dimensional driving state data set to obtain vehicle driving road attribute matching data; and carrying out vehicle driving risk estimation and vehicle safety over-rate evaluation based on the vehicle driving road attribute matching data, carrying out safety comparison verification evaluation, generating vehicle personalized driving improvement suggestions, and pushing the vehicle personalized driving improvement suggestions to the vehicle-mounted terminal to execute corresponding vehicle safe driving behavior improvement operation. According to the invention, real-time monitoring, accurate evaluation and active safety intervention of vehicle driving behaviors can be realized.

The invention provides a driving behavior intelligent identification method and system based on video analysis, and the method comprises the steps: obtaining a driver face video stream and a road environment video stream collected by a vehicle-mounted camera, and reading the driving information recorded by a whole vehicle communication network; recognizing an eyelid closing state, a sight line direction and a head posture in the driver face video stream based on a posture recognition model, and performing fatigue distraction analysis to obtain driver state information; performing motion trail analysis on the road environment video stream and the driving information, and performing driving risk assessment in combination with the driver state information to obtain driving assessment information; and performing early warning construction according to the driving evaluation information, generating early warning prompt information, and synchronously writing the early warning prompt information, the driving evaluation information and the driver state information into a safety data protection memory. The fatigue and distraction states of the driver can be recognized more accurately, and the accuracy of state judgment is improved.

The invention discloses an intelligent intervention system and method based on multi-mode driving behavior analysis, and belongs to the field of intelligent traffic. The system comprises a multi-dimensional perception module, a behavior analysis engine, a risk assessment matrix and a self-adaptive intervention module. The multi-dimensional sensing module is integrated with a multi-source heterogeneous sensor and is used for acquiring physiological characteristics of a driver, driving operation data and environment information; the behavior analysis engine analyzes multi-modal data based on spatial-temporal feature fusion, and digs a driving behavior mode; the risk assessment matrix judges danger levels according to the multi-dimensional driving risk quantification result level by level; and the self-adaptive intervention module implements a grading progressive correction strategy. The system realizes active prevention and control of driving risks through cross-modal data association, behavior chain prediction and environment-behavior coupling analysis. The system solves the problems that a traditional system is single in sensing dimension, coarse in risk assessment, rigid in intervention strategy and the like, and effectively improves the driving safety.

The invention provides a driving risk early warning method based on long-term and short-term driving style features. The method comprises the following steps: constructing long-term and short-term driving feature vectors according to driving behavior data; based on the standardized long-term and short-term driving feature vectors, a clustering method is adopted to determine long-term style feature vectors and long-term and short-term driving risk score labels; constructing a multilayer driving risk assessment model considering long and short term driving styles, and performing fitting training on the assessment model based on the long and short term driving style sample set to obtain an optimal multilayer driving risk assessment model; determining a long-term driving style category according to a long-term driving feature vector collected in real time, inputting the long-term driving style category and a short-term driving feature vector collected in real time into an optimal multilayer driving risk assessment model, and outputting a moving average trend of a scoring time sequence and a fluctuation trend of a driving behavior in a time window according to a predicted driving risk score output in real time. And formulating a two-stage early warning strategy. According to the invention, highly personalized and adaptive risk early warning can be provided.

The invention discloses a foggy day driving augmented reality auxiliary method and system based on multi-mode perception, and relates to the technical field of intelligent driving. The method comprises the following steps: constructing a three-dimensional environment map to carry out real-time driving risk prediction, and carrying out preliminary adjustment on data source weight distribution in the three-dimensional environment map according to a set rule; the PPO reinforcement learning algorithm is adopted to readjust data source weight distribution, a new three-dimensional environment map is reconstructed based on the readjusted data source weight, and the risk probability, predicted in real time, of sudden deceleration of the front vehicle and the reconstructed three-dimensional environment map are projected to a windshield through a layered projection strategy to prompt a driver. According to the method, accurate and comprehensive multi-mode sensing data can be obtained in a foggy day environment, the accuracy of decision making and risk prediction is ensured, quick response can be made according to environment changes, the safety of a driver under complex weather conditions is improved, and the method can adapt to more changing environments in practical application.

The invention relates to a vehicle-cloud integrated wet and slippery road vehicle driving risk early warning method and system, and the method comprises the following steps: obtaining the forward video data of a vehicle, recognizing the type of a current driving road according to the forward video data through a road segmentation and road classification multi-task model, and carrying out the early warning of the driving risk of the vehicle through a pre-established road adhesion coefficient reference table. A road adhesion coefficient is obtained and uploaded to the cloud; continuously counting global road adhesion coefficients, establishing a road adhesion coefficient fusion model, and fusing the global road adhesion coefficients through road section matching and introduction of time difference factors to generate a wet and slippery road map; determining an early warning area corresponding to any wet and slippery road section on the wet and slippery road surface map through a dynamic calculation method; and for a wet and slippery road section, phase plane stability boundaries of different types of vehicles are calculated, typical driving control behaviors are simulated, instability risks are evaluated, and early warning information is sent out. Compared with the prior art, the method achieves the observation of the wet and slippery road surface, and has the advantages of high early warning precision, high pertinence and the like.

The invention discloses a vehicle-mounted driving assistance method, which relates to the technical field of driving assistance and comprises the following steps: acquiring road image data acquired by a vehicle-mounted camera and vehicle state data acquired by a vehicle-mounted sensor; performing image recognition on the road image data, extracting traffic signs, lane lines and surrounding vehicle position information, and generating road environment data; and judging a current driving risk level according to the road environment data and the vehicle state data, and feeding back early warning information to a driver according to the driving risk level. According to the invention, a comprehensive utilization and real-time feedback mechanism of multi-source data is provided, and the perception and reaction capability of a driver to potential risks is enhanced. Therefore, the system not only can deal with conventional driving situations, but also can deal with complex and dynamically changing traffic situations, so that the driving safety is greatly improved, and the possibility of accidents is reduced.

The invention relates to a traffic engineering BIM (Building Information Modeling) and VR (Virtual Reality) technology combined interactive design and simulation system, which relates to the field of traffic engineering and comprises an interactive configuration module used for selecting simulation configuration to obtain user feedback information; the route mining module is used for performing networking frequent mining to obtain a mobile route case set; the virtual driving module is used for starting simulated driving and obtaining a three-dimensional dynamic time sequence diagram of a vehicle and heart rate monitoring time sequence information of a user; the safety scoring module is used for receiving a user driving safety score through the user side; the risk evaluation module is used for obtaining a driving risk evaluation value set; and the safety identification module is used for performing safety identification and then sending the information to the traffic engineering design end. Through combination of BIM and VR technologies, virtual simulation acquisition and analysis of user driving behaviors are realized, so that the driving risk of a design scheme is evaluated in a targeted manner, traffic engineering optimization design is guided, and the safety of traffic engineering design is improved.

The invention provides a driving risk assessment method and device based on human-vehicle-road environment multi-modal data, and the method comprises the steps: obtaining a plurality of driver risk factors of a driver of a target vehicle, and building a driving risk assessment model according to the plurality of driver risk factors; obtaining a weight coefficient corresponding to each driver risk factor in the driving risk assessment model; determining a risk value when the driver drives the target vehicle according to the plurality of driver risk factors and the weight coefficient corresponding to each driver risk factor; and determining a driving risk level when the driver drives the target vehicle according to the risk value.

The invention discloses a vehicle driving right transfer method based on abnormal driving behaviors and electronic equipment, and the method comprises the steps: collecting driver state data, vehicle driving state data and road environment data, and carrying out the multi-source data fusion; abnormal driving behavior detection is performed according to the multi-source data, and the driving risk is evaluated; a rule engine decision-making model based on the situation is used for deciding countermeasures to be taken, and the countermeasures comprise early warning type actions, auxiliary control type actions, forced take-over type actions and mutual switching among the actions based on an advanced auxiliary driving system or an automatic driving system; after the vehicle is forced to take over and runs for n minutes, whether the current vehicle is suitable for the driver to take over or not is judged according to driver state data collected in real time and the current comprehensive driving risk score, and whether driving right transfer is conducted or not is determined according to the judgment result. The accuracy of abnormal driving behavior detection is improved, the vehicle driving right is intelligently transferred, and the driving safety is improved.

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

The invention belongs to the technical field of traffic control systems, and particularly relates to an expressway interleaving area automatic driving vehicle driving risk assessment method, which comprises the steps of 1, obstacle space-time risk field modeling, 2, road boundary and road line risk field modeling, 3, expressway upper and lower ramp risk field modeling, 4, total risk field calculation, 5, expressway interleaving area automatic driving vehicle driving risk assessment, 6, expressway interleaving area automatic driving vehicle driving risk assessment, 7, expressway interleaving area automatic driving vehicle driving risk assessment and 7, expressway interleaving area automatic driving vehicle driving risk assessment. The method has the advantages that the physical quantity of the space-time distance is innovatively introduced, the dimension of a two-dimensional space risk field is raised to a three-dimensional time-space risk field, the real-world expressway vehicle track is extracted, and the real-world expressway vehicle track is extracted. And the influence of the prediction trajectory and the time effect on the risk is effectively quantified. A special geometric configuration field of the upper ramp and the lower ramp of the interlacing area is also constructed, and accurate limitation is applied to operation of a specific vehicle. And on the basis of a risk dynamic balance theory, YOLO and other machine vision technologies, model parameters are calibrated by using real aerial photography data.

The invention discloses a simulation method for urban flood emergency rescue path planning, and the method comprises the steps: carrying out the modeling of an urban road network based on an urban topographic map, rainfall data, existing water distribution and key position information; mIKE simulation software is used for establishing a hydrodynamic model to simulate the rainfall and flood spreading process of the city, and flood dynamic information, including the depth and flow velocity of the flood, at each position point in the flood disaster scene model is obtained; respectively constructing objective functions according to the shortest maximum time of arrival of the disaster relief point, the lowest driving risk of the rescue vehicle and the shortest total weighted rescue time; setting constraint conditions to ensure that all disaster relief points are covered, and preventing vehicles from being excessively distributed; based on the flood dynamic information, the objective function and the constraint condition, adopting an NSGA-II non-dominated sorting genetic algorithm to solve a Pareto solution set; and performing weighted analysis on a result in the Pareto solution set to obtain an optimal solution, and outputting an urban flood emergency rescue path plan.

An intelligent driving risk early-warning method and apparatus, an electronic device, and a storage medium. The intelligent driving risk early-warning method is applied to a first vehicle, and comprises the following steps: when it is determined that the first vehicle is in an intelligent driving state, acquiring vehicle traveling information (S10); when it is determined that the vehicle traveling information satisfies a preset potential risk condition, performing risk data detection (S20); and when risk evaluation is performed by means of a preset risk evaluation model on the basis of the risk data to determine that the first vehicle has an intelligent driving risk, outputting intelligent driving risk early-warning information (S30).

The invention relates to the field of automatic driving and traffic safety, in particular to a fine-grained multi-task driving risk prediction method fused with a trajectory prediction auxiliary task. Comprising the following steps: step 1, automatically labeling risk labels based on trajectory data; 2, designing a main task of the driving risk prediction model; 3, auxiliary task design of the driving risk prediction model; and 4, constructing and training a driving risk prediction model. An experiment result based on a disclosed NGSIM data set shows that the precision and robustness of a driving risk prediction model can be remarkably improved by introducing trajectory prediction as an auxiliary task.

The embodiment of the invention discloses a driver emotion recognition method and device, equipment and a medium. The method comprises the steps of obtaining working condition information of a vehicle, and determining driving characteristics of a driver based on the working condition information; acquiring real-time road information, and determining the driving behavior of the driver based on the real-time road information and the driving characteristics; acquiring a face video of the driver, and determining expression features of the driver based on the face video; and determining an emotional state of the driver based on the driving behavior and / or the expression feature, and determining an emotional pacifying strategy corresponding to the driver based on the emotional state. According to the embodiment of the invention, the emotion state of the driver can be accurately determined, and the driving risk caused by the negative emotion of the driver is avoided, so that the driver is helped to keep a better driving state.

The invention relates to an automatic driving safety behavior decision-making method with interactive risk consciousness, which belongs to the technical field of automatic driving behavior decision-making, and comprises the following steps: S1, extracting the state of a vehicle and driving environment information based on sensor data; s2, defining a state space and an action space of deep reinforcement learning; s3, adopting a D3QN reinforcement learning algorithm to construct a decision framework and integrating a self-attention mechanism to capture multi-vehicle interaction features; s4, establishing a driving risk potential field model considering multi-vehicle interaction according to the multi-vehicle interaction characteristics and the motion state and the relative position of the environment vehicle; s5, constructing a comprehensive reward function based on the driving risk potential field; s6, performing network training by adopting an experience playback mechanism; and S7, deploying a risk action correction module at the output end of the value network.

The invention relates to the technical field of driving, in particular to a driving risk interaction prompting method, intelligent equipment and a readable storage medium, and aims to solve the technical problem of how to ensure that a driver can timely, accurately and effectively receive a risk prompt of a driving risk scene. In order to achieve the purpose, a driving risk scene of the intelligent equipment is obtained based on environment perception information of the intelligent equipment; obtaining a visual focus prediction result of the driver based on the driver state of the driver of the intelligent equipment; obtaining a channel availability evaluation result of at least one prompt channel of the intelligent equipment based on the prompt channel state of the intelligent equipment; according to a visual focus prediction result and a channel availability evaluation result, risk prompting is performed on a driving risk scene, so that the situation that the risk prompting is ignored in an unconcerned area of a driver is effectively avoided, and the risk prompting can be adaptively, timely, effectively and accurately perceived and transmitted by the driver; therefore, the driving safety of the intelligent equipment is effectively improved.

The invention discloses a vehicle driving risk identification method and device fused with vehicle body attitude monitoring, and relates to the technical field of data processing, and the method comprises the steps: deploying a sensing network used for vehicle body attitude monitoring, and collecting the real-time attitude information of a target vehicle; constructing a visual evaluation channel and a regression analysis channel, and fusing to generate a vehicle dynamic evaluation model; inputting the real-time attitude information to the evaluation model to obtain a dynamic evaluation value; defining a neighborhood control limit, and extracting a preorder dynamic sequence of the evaluation value; and combining the real-time dynamic evaluation value with the preorder dynamic evaluation sequence to identify the vehicle driving risk. Therefore, the technical effects of expanding the recognition mode, fusing subjective behavior evaluation and improving the risk recognition precision are achieved.

The invention relates to the technical field of automatic driving risk analysis, in particular to a traffic risk analysis method and analysis device based on a graph neural network, and the method comprises the steps: sequentially carrying out the coding of a graph encoder and the coding of an attention mechanism based on the obtained historical track information of an intelligent agent and the lane line information of a map, and obtaining an attention mechanism; obtaining an attention mechanism coding result; decoding by using a cross attention mechanism based on an attention mechanism coding result; and performing prediction trajectory decoding by using a multi-layer perceptron based on a cross attention mechanism decoding result, performing decoding by using a deconvolution network based on a decoding result, calculating risk matrix loss by using a probability loss function, and performing model training and verification to obtain a risk analysis model based on a graph neural network so as to predict traffic risks. Therefore, the problems that the risk analysis depth of traffic participants is limited and future intentions and potential risks of the traffic participants are difficult to accurately describe by traffic risk analysis methods in related technologies are solved.

The invention discloses an auxiliary driving method. The method comprises the following steps: acquiring a driving strategy association factor; performing feature extraction on the driving strategy correlation factors to obtain driving strategy correlation features; dynamically distributing the weights of the plurality of driving strategy associated features; performing weighted fusion on the plurality of driving strategy associated features to obtain a driving strategy fusion feature; determining a risk level evaluation index based on the driving strategy fusion feature, and obtaining a risk evaluation result based on the risk level evaluation index and an index threshold; the index threshold value is generated based on the personalized data of the driver and the traffic environment data; and generating an auxiliary driving strategy according to a risk assessment result. According to the method, the multi-dimensional driver data is fused, the feature weight is dynamically distributed, and the risk assessment result is generated in combination with the personalized index threshold, so that the problem of misjudgment caused by single data dimension and fixed weight of a traditional system is solved, and the method has the advantages of improving the driving risk assessment accuracy, enhancing the system adaptability and improving the driving safety.

The invention discloses an auxiliary driving risk prediction method based on driver state monitoring and a related device, and relates to the technical field of auxiliary driving risk prediction. In the scheme of the invention, unsupervised clustering is carried out on historical driver physiological data and historical vehicle driving data by using a clustering algorithm; preliminarily dividing collision risk grades of automobile operation; constructing a training data set and a test data set according to a plurality of groups of historical driver physiological data, historical vehicle driving data and corresponding collision risk levels, and obtaining a trained collision risk prediction model; and finally, based on the actually measured vehicle driving data and the actually measured driver physiological data, risk prediction is carried out by using the collision risk prediction model so as to obtain an auxiliary driving risk prediction result. In addition, the auxiliary driving risk prediction result can also be used for updating division of collision risk levels in combination with actual conditions. The problem of insufficient prediction accuracy caused by the fact that a traditional risk prediction method is only based on vehicle operation data is solved, and the prediction accuracy of the driving risk is improved.