779 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 discloses a collision risk assessment method and system based on digital twinning and trajectory prediction, and the method comprises the steps: constructing a vehicle-road cooperation environment through a distributed digital twinning system, guaranteeing that vehicle information is always in a latest state through a prediction and updating system of a model, and providing a reliable data source for trajectory prediction; based on the multi-dimensional space-time interaction characteristics of vectorization processing, a multi-agent multi-mode parallel trajectory prediction algorithm is provided, and the motion states of multiple vehicles at the future moment are obtained at the same time; a driving risk field is dynamically generated according to multi-dimensional kinematics characteristics of a vehicle, and multi-source collision risks in a traffic scene are modeled in a three-dimensional mode by combining road characteristics and static obstacle characteristics. According to the method, the optimization method is integrated, fine-grained modeling is carried out on the traffic scene by using the digital twinborn technology, multi-agent trajectory prediction is combined with the field theory, and the timeliness and accuracy of collision risk assessment in the complex traffic scene are greatly 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 belongs to the field of road condition recognition, and discloses a road condition recognition method and device and electronic equipment, and the method comprises the steps: calculating a yaw velocity actual value according to a lateral sensor signal; calculating a yaw velocity reference value according to the vehicle information and the driving information; calculating an accumulated deviation value, and judging whether the accumulated deviation value is greater than a first threshold value or not; and if yes, setting first identification information for the current road section. According to the method, the actual value and the reference value of the yaw velocity are calculated in real time by using the lateral sensor signal and the vehicle driving data, and the road condition is accurately judged by comparing the deviation value of the two values, so that the road condition information is provided for a driver or an automatic driving system in time. When the bad road is detected, the identification information is set, so that early warning is facilitated, a driver can take proper measures, potential driving risks are avoided, and driving safety and comfort are ensured. And meanwhile, the method does not depend on a visual system which is easily influenced by the environment, so that the robustness and adaptability of the road condition recognition system are enhanced.

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 an underground tunnel driving risk evaluation method based on a dynamic Bayesian network. The method comprises the following steps: constructing an underground tunnel driving risk evaluation index system; determining Bayesian network nodes according to the underground tunnel driving risk evaluation index system, and constructing an underground tunnel driving risk dynamic Bayesian network model; obtaining a driving risk assessment result according to the dynamic Bayesian network model of the driving risk of the underground tunnel; and performing driving improvement according to the driving risk assessment result. The method can effectively identify and predict the driving risk probability of different road sections of the underground tunnel, provides risk early warning for drivers, and provides data support for a traffic management department to optimize an induction system and an accident prevention strategy, thereby improving the driving safety and traffic efficiency in the tunnel.

The invention relates to a driving risk scene construction and data acquisition method, system and device and a medium, and the method comprises the following steps: carrying out the feature calibration of real traffic accident data, and carrying out the processing to obtain an accident data comparison analysis result; screening the calibrated traffic accident data based on a preset condition, and performing clustering division processing on the screened traffic accident data to obtain a typical driving risk scene; according to the accident data comparison analysis result and the typical driving risk scene, constructing a driving risk scene based on the simulated driving environment; and according to the constructed driving risk scene, carrying out a driving simulation experiment, and collecting physiological state, psychological state and driving behavior data of a driver in each driving risk scene. The method can be widely applied to the field of driving safety evaluation.

The invention discloses a comprehensive driving risk quantification method based on a multi-angle risk variable weight, and the method comprises the steps: calculating a multi-angle driving risk based on the real-time driving information of a vehicle, and the multi-angle driving risk comprises a vehicle instability risk, a violation risk and a collision risk; vehicle driving states are judged, wherein the vehicle driving states include the face of potential conflicts and the absence of potential conflicts; determining a variable weight value of the multi-angle driving risk according to the vehicle driving state; and constructing a comprehensive driving risk quantitative model based on the variable weight, and performing model verification. According to the method, the variable scheme of the multi-angle driving risk weight is designed by analyzing the driving state of the vehicle in the dynamic environment, and the variable weight comprehensive driving risk model is constructed, so that the real-time quantification of the driving risk in the dynamic environment is realized, and the risk quantification result is closer to the actual situation.

The invention provides a vehicle control method, electronic equipment and a vehicle, and is applied to the technical field of vehicle intelligent control, and the method comprises the steps: obtaining the positions and the driving directions of all vehicles in a vehicle team, and determining a leading vehicle of the vehicle team and a following vehicle of the leading vehicle based on the positions and the driving directions; in response to determining that the vehicle is a pilot vehicle, determining a pilot road section according to driving information of the pilot vehicle, and sending the driving information and the pilot road section to a following vehicle; in response to the fact that the vehicle is determined to be the following vehicle, target driving parameters are determined according to driving information sent by the navigation vehicle, the following vehicle is controlled to pass through the navigation road section based on the target driving parameters, and the target driving parameters are the lowest required driving parameters allowing the following vehicle to pass through the navigation road section. According to the invention, the overall driving efficiency of the motorcade is improved, and the vehicle driving risk and failure rate are reduced.

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 discloses a driving behavior risk detection method, system and device and a storage medium, and relates to the field of driving safety. The method comprises the following steps: collecting vehicle internal data, external environment data, driver biological characteristic data, vehicle-mounted video data and vehicle-mounted audio data; extracting a first feature from the vehicle internal data, extracting a second feature from the external environment data, extracting a third feature from the driver biological feature data, extracting a fourth feature from the vehicle-mounted video data, and extracting a fifth feature from the vehicle-mounted audio data; fusing the first feature, the second feature, the third feature, the fourth feature and the fifth feature to obtain a feature vector, and inputting the feature vector into a preset machine learning model to obtain a risk detection score; and when the risk detection score exceeds a threshold value, displaying risk information on a vehicle-mounted display screen and giving a voice prompt. By implementing the technical scheme provided by the invention, the driving risk can be comprehensively evaluated by integrating multi-source data, the early warning is timely performed, 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.

The invention discloses a safety protection method and system based on a multi-level automobile Internet of Things, and belongs to the field of automobile safety protection, and the method specifically comprises the steps: collecting and encrypting vehicle and environment data through an encryption sensor network, and guaranteeing that the data is safely transmitted to the local through quantum encryption communication; the local processing unit processes the data by using a reinforcement learning AI decryption algorithm, and recognizes the driving risk in combination with machine learning; the risk assessment model based on multi-source information fusion systematically assesses the risk and optimizes and generates a protective measure strategy; when a risk is encountered, the Internet of Things technology instantly triggers early warning and executes protection, meanwhile, vehicle data are uploaded to the cloud in real time, a safety auditing mechanism is established, records are comprehensively monitored, and vehicle driving safety and data privacy protection are ensured.

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).