815 results about "Traffic crash" patented technology

According to the method, multi-modal data such as voice, images, texts, GIS and Internet of Things sensing are fused, and deep neural network prediction, reinforcement learning scheduling optimization and rule engine compliance check are combined; the intelligent vehicle and material dispatching method and system are applied to multiple scenes such as emergency material storage depots, fire-fighting emergency command, urban disaster response, traffic accidents and medical first aid. The system is interconnected and intercommunicated with an intelligent emergency material storage cloud platform, a city brain, Beidou navigation, intelligent fire fighting and other external platforms, and supports one-key issuing, path optimization, traffic signal linkage and whole-course return closed loop. Compared with the prior art, the method has the advantages that unification of multi-modal situation awareness, data-driven optimal scheduling and expert knowledge constraints is realized, the response time is remarkably shortened, the resource utilization rate is improved, and compliance safety is ensured.

The invention discloses a road traffic accident cause analysis and responsibility judgment method and system, and relates to the technical field of traffic management data processing, and the method comprises the steps: carrying out the credible collection of multi-source data, and constructing an evidence chain; performing multi-source data preprocessing and cross-modal fusion optimization; performing multi-dimensional cause intelligent analysis; performing responsibility judgment based on a quantification rule; and the responsibility judgment result is subjected to multi-dimensional rechecking and rule iteration adaptation. According to the road traffic accident cause analysis and responsibility judgment method and system, through a full-process closed-loop design of data acquisition, preprocessing, cause analysis, responsibility judgment, re-checking iteration and report evidence storage, technologies such as multi-source perception and an AI algorithm are integrated; the method solves the problems of uncredible evidence chain, one-sided cause traceability, non-uniform judgment standard, low cooperation efficiency and the like in traditional accident processing, realizes intelligence, standardization, compliance and traceability of accident processing, remarkably improves the credibility, processing efficiency and judicial suitability of a judgment result, and provides core technical support for modernization of traffic control.

The invention provides an automatic driving test scene generation method based on real traffic data, and solves the problems of low accident data utilization rate, SIL / HIL test splitting and insufficient boundary coverage in the prior art. Comprising the following steps: acquiring multi-source heterogeneous traffic accident data; cleaning data by adopting a joint interpolation-anomaly detection mechanism; vehicle dynamic sudden change characteristics within 0.5 second before braking are extracted through LSTM and DTW algorithms; constructing a three-dimensional scene pipeline driven by a physical engine, and dynamically associating the pavement slippery coefficient with the rainfall intensity; analyzing the accident text into simulation parameters by using a semantic-physical parameter converter; performing SIL-HIL cooperative verification: performing extreme illumination perception test and narrow road planning verification in an SIL environment, and realizing 1ms step length fault injection test in an HIL environment; positioning failure parameters based on Bayesian optimization; a GAN is adopted to generate a long-tail scene, and a test boundary is expanded by coupling extreme conditions such as rainstorm / low visibility; and outputting a standard scene library containing the collision probability thermodynamic diagram. The safety verification efficiency under the extreme working condition is remarkably improved.

A method, device and system for active management and control of road traffic safety are disclosed. The method includes acquiring traffic data of a target road in real time, wherein the target road includes management and control sections; for each management and control section, judging whether there is a traffic accident according to the acquired traffic data; if so, formulating an emergency management and control strategy; if not, extracting traffic flow data from the traffic data, and generating predicted traffic flow data according to the traffic flow data by a traffic flow prediction model; generating a risk level according to the predicted traffic flow data by a risk prediction model; determining the current active management and control strategy of the management and control section according to the risk level and the predicted traffic data; and issuing the corresponding management and control strategy of each of the management and control sections.

The embodiment of the invention discloses a traffic accident data processing and analyzing method and device and electronic equipment. The method comprises the steps of performing data fusion on traffic accident data in various data formats based on a large-model heterogeneous field mapping method to obtain an accident data set, performing data cleaning on the accident data set according to a dynamic rule engine and a correction mechanism to obtain a target data set, and storing the target data set in a database. And finally, performing hierarchical analysis on the target data set based on the multi-dimensional accident analysis model to obtain a data analysis result, and performing visual display on the data analysis result. Through multi-source data intelligent fusion, data quality control and a multi-dimensional accident analysis model, the data quality of the multi-source data is improved, the purpose of efficiently and automatically processing different data formats is achieved, a large amount of human resources are saved, and meanwhile the accuracy and reliability of a data processing analysis result are improved.

The invention relates to a traffic accident intelligent detection system and method based on a YOLOv12 improved architecture. The system comprises a YOLOv12 enhanced feature extraction network, a multi-scale detection head, a time sequence information fusion module, a real-time reasoning optimization engine and an intelligent decision fusion system, and realizes collaborative optimization of local feature enhancement and global context modeling by constructing six core technology modules and adopting collaborative learning of a C2f-Attention mechanism and deformable convolution. According to the method, a composite loss function special for traffic accidents is innovatively designed, and adaptive fusion of multi-scale features and difficult sample mining are realized through a multi-objective optimization mechanism of Enhanced Focus Loss, IoU-aware Loss and Severage-aware Loss. According to the method, the problems of low detection precision and false alarm and missing alarm caused by illumination variation, shielding and motion blur in a traffic monitoring scene are effectively solved, in the test of an AccidentsDesection YOLOv8 data set, the mAP at 0.5 reaches 91.27% and is improved by 8.6% compared with that of YOLOv8, the reasoning speed reaches 67 FPS, experimental results show that the system has excellent performance in the aspects of detection precision, real-time performance and model compression, and the method is suitable for popularization and application. The method achieves a remarkable effect in traffic accident intelligent identification, and has a remarkable technical effect and industrial application value.

The invention discloses an automobile driving state blind area monitoring method and a rearview mirror, and relates to the technical field of blind area monitoring. Comprising the following steps of information collection, specifically, sensors and cameras are installed on wheels of an automobile, a steering wheel steering column, an automobile chassis and a steering system respectively, driving state data of the automobile speed, the steering wheel angle, the automobile acceleration and the steering angle and image data around the automobile are collected in real time, and real-time automobile information data are obtained; according to the system, driving state data such as the vehicle speed, the steering wheel angle, the vehicle acceleration and the steering angle are combined, the blind area around the vehicle is monitored in real time through the sensor and the camera, and when the vehicle steers, the blind area monitoring range is adjusted in real time to adapt to the driving direction change; the rearview mirror can display blind area images in real time, the blind area alertness of a driver can be greatly improved, and the traffic accident risk caused by the blind area is effectively reduced.

The invention provides a three-dimensional reconstruction method for a traffic accident scene in combination with a ground-air view angle, and relates to the technical field of three-dimensional reconstruction, and the method comprises the steps: carrying out the image collection of the traffic accident scene through employing cameras of two platforms, namely an unmanned plane and an unmanned vehicle, carrying out the semantic segmentation of the image, and generating a pixel-level mask of a key target; extracting an ROI from the image according to the pixel-level mask of the key target, and performing image enhancement and denoising processing on the ROI to obtain a processed ROI image; and optimizing internal and external parameters of cameras of the unmanned aerial vehicle and the unmanned vehicle, performing absolute scale recovery, reconstructing a three-dimensional surface model by combining an SfM method, an MVS method and a surface reconstruction method of a combined camera optimization objective function based on a pixel-level mask of a key target and a processed ROI image, and performing digital quantitative analysis of an accident scene. The method can solve the problem that the existing three-dimensional reconstruction method is insufficient in camera calibration optimization and lacks absolute scale information.

The present application belongs to the technical field of highway traffic control. Disclosed in the present application are a method and device for dynamically evaluating the degree of impact of a highway traffic accident, which method and device are used for solving the existing technical problem of after a traffic accident occurs on a highway, transportation efficiency being prone to being subjected to severe negative impacts caused by it being difficult to dynamically predict the degree of impact of the highway traffic accident, which is not conducive to effectively estimating the severity of the accident. The method comprises: determining a local highway network of an accident site; performing index fusion on preset structural indexes and functional indexes, and performing continuous equalization processing on the severity of a highway traffic accident on the basis of comprehensive indexes obtained after fusion, so as to obtain an accident severity evaluation system for the highway traffic accident; and performing data fusion on information of the natural evolution patterns of traffic flows, information of the severity of the accident at the moment when the accident occurs, and information of abrupt changes in the traffic flows that are induced by the accident, so as to obtain an accident impact factor system of the highway traffic accident.

The present application provides a traffic accident recognition method and device based on a multi-modal large language model. The method comprises: acquiring multi-modal data in a driving state of a target vehicle, wherein the multi-modal data comprises video data, audio data, and sensor data; inputting the multi-modal data into an accident recognition model obtained by training a multi-modal large language model for multi-modal information mining, and determining, on the basis of the mined multi-modal information, whether a traffic accident has occurred to the target vehicle; and if the traffic accident has occurred to the target vehicle, sending alarm information. According to the method, effective capturing of global information in the driving process of a target vehicle can be realized by means of an accident recognition model obtained by training a multi-modal large language model, so that recognition of traffic accidents in different scenes is enhanced, and the recognition accuracy of traffic accidents in different scenes is improved.

The invention relates to the technical field of knowledge extraction, and discloses a multi-source road traffic accident knowledge extraction method and system, and the method comprises the steps: determining a joint feature vector based on an accident knowledge triple marking matrix, constructing a comprehensive function based on the joint feature vector, and distributing marks for the accident knowledge triple marking matrix through the comprehensive function, an accident knowledge triple optimization mark matrix is obtained, the accident knowledge triple optimization mark matrix is decoded, and an extraction result containing knowledge triads is obtained. The hidden error accumulation caused by semantic ambiguity in the model is solved, and knowledge extraction with higher robustness is realized. Complete and credible structured input is provided for constructing nodes according to types in a subsequent knowledge graph, and high-quality construction of the multi-source road traffic accident knowledge graph from an unstructured text to a semantic-definite and logic-consistent multi-source road traffic accident knowledge graph is truly realized.

The invention discloses a traffic drainage signal management method and system based on a Beidou system, and relates to the technical field of Beidou satellite navigation, and the method comprises the steps: obtaining the real-time traffic data of the position and speed of a vehicle at each intersection based on the Beidou system, and transmitting the real-time traffic data to a traffic management platform; carrying out feature matching on the accident identification model trained based on historical emergency data and a deep learning algorithm and real-time traffic data, and identifying an emergency; and constructing a real-time traffic situation map in combination with the identified emergency information and the traffic condition information of the intersections. The vehicle position and speed data are obtained in real time through the Beidou system, emergencies are rapidly recognized and signal lamp timing is dynamically adjusted in combination with a deep learning algorithm, and compared with a traditional fixed timing scheme, traffic flow changes can be responded in real time, frequent start and stop of vehicles are reduced, the road passing efficiency is improved, and the traffic safety is improved. And particularly, in a sudden traffic accident or construction scene, a signal strategy can be quickly optimized, and traffic congestion aggravation is avoided.

The invention discloses a visual constraint-based vehicle safety domain modeling and conflict risk assessment method, which comprises the following steps of: analyzing and selecting a non-signalized intersection with frequent traffic accidents by using accident black spots, and acquiring multi-dimensional traffic information of the intersection through an unmanned aerial vehicle; building three vehicle safety domain models of straight running, right turning and left turning; by predicting a vehicle trajectory equation, determining an intersection conflict position, dividing intersection conflicts into two categories of cross conflicts and confluence conflicts according to a trajectory interaction mode, and in different conflict scenes, according to a constructed vehicle safety domain, performing quantitative calculation on a conflict risk of vehicles in a non-signalized intersection to obtain traffic conflict exposure time; and based on the traffic conflict exposure time data, determining an optimal clustering number K of a K-means algorithm through an elbow rule, clustering results of the traffic conflict exposure time by using the K-means algorithm, and grading conflict risks. According to the invention, the accuracy of vehicle conflict detection at the non-signalized intersection is improved.

The invention discloses a visual angle robust traffic accident detection method based on space-time attention domain adaptation. The method comprises the following steps: constructing a double-flow network architecture; the progressive multi-granularity spatial domain adaptation module processes appearance change caused by a visual angle through global and local feature alignment, and uses SSAM to generate a semantic mask and entropy-guided mobility weight to realize accurate spatial domain adaptation; the time collaborative attention module realizes dynamic alignment of accident related time information between different visual angles through cross-domain video clip correlation calculation and a collaborative attention mechanism; a comprehensive contrast learning strategy is introduced, and noise robust motion feature learning is enhanced; a multi-view-angle joint training strategy is adopted, cross-domain knowledge migration is carried out by using monitoring view angle, vehicle-mounted view angle and unmanned aerial vehicle view angle data, and model optimization is realized through a comprehensive loss function. According to the method, the performance is remarkably improved in a cross-view migration task, and an efficient solution is provided for a unified multi-view traffic accident detection system.

The invention discloses a large transport intelligent line selection implementation method fusing a graph neural network and a large language model, and the method comprises the following steps: S1, constructing multi-source features to a unified high-dimensional vector space according to large transport historical data and real-time environment data; s2, constructing a structured prompt template through multi-modal input; s3, based on the structured prompt template, generating a candidate route set in combination with a large language model; s4, dynamically optimizing the candidate route set through a reinforcement learning framework, and outputting an optimal route. Compared with the prior art, the method has the advantages that intelligent generation and dynamic decision making of a large transportation route are realized through multi-modal feature fusion, graph structure semantic compression and large model reasoning optimization, and the transportation reliability, safety and economical efficiency can be remarkably improved; traffic accidents can be responded in real time to realize route adjustment, routes are dynamically optimized to reduce passage cost, planning quality is continuously improved through historical case learning, and safe and punctual delivery of overrun goods is guaranteed.

The invention relates to the technical field of intelligent traffic, in particular to a precise traffic accident early warning system based on multi-sensor fusion. Comprising a multi-modal data acquisition unit; the intelligent central processing unit is used for generating differentiated early warning instructions adaptive to different traffic participant types based on the three-dimensional risk decoupling result and the collision risk prediction result; a grading differentiation early warning execution unit; and an emergency linkage unit. According to the method, a meta-task sampling strategy is constructed through causal effect value grading and intersection topological feature clustering, edge lightweight adaptation is achieved in combination with gradient compression, parameter self-evolution is achieved through sliding window monitoring and knowledge distillation, and edge computing power and early warning precision are balanced. According to the invention, the multi-modal graded differentiated early warning terminals adapted to different traffic participants and a 5G edge low-delay public security and emergency linkage mechanism are matched, the early warning pertinence and the disposal practicability are quickly responded and improved, and the problem of insufficient edge adaptation and precision balance in the prior art is solved.

The invention relates to the technical field of artificial intelligence and intelligent traffic, in particular to a traffic accident liability intelligent affirmation method and system based on a multi-agent cooperation mechanism and a large language model. The whole process of credible information verification, responsibility affirmation reasoning and standard document generation is simulated. Wherein the legal expert agent adopts a competing mechanism, and the fairness and the accuracy of affirmation are improved through preliminary affirmation, double-party defense and final judgment. The reasoning ability of a large language model and accurate knowledge retrieval of a retrieval enhancement generation technology are fused, the real-time performance and accuracy of legal clause quotation are ensured, and a road traffic accident identification document conforming to specifications is automatically generated. The method effectively solves the problems that traditional manual identification is low in efficiency and high in subjectivity, and an existing intelligent method lacks interpretability and legal accuracy.

The invention provides a traffic accident emergency response method and system based on vehicle state perception, and relates to the technical field of intelligent traffic, and the method comprises the steps: deploying a vehicle-mounted sensor and roadside perception equipment, and collecting and obtaining vehicle state data and traffic environment data; constructing a traffic accident analysis index set, and analyzing the vehicle state data and the traffic environment data; building a traffic accident risk assessment model, assessing the traffic accident index parameter set, and outputting a traffic accident risk coefficient; triggering a target emergency response strategy; and analyzing the traffic accident index parameter set to obtain a target emergency strategy parameter, and carrying out accident emergency response processing. According to the method and the device, the technical problem of emergency response delay caused by inaccurate risk assessment of the traffic accident in a complex traffic situation in the prior art is solved, and the processing efficiency of the traffic accident is improved by comprehensively considering the multi-dimensional data, accurately assessing the risk of the traffic accident and automatically triggering the emergency strategy.

The invention relates to a traffic accident scene live-action reconstruction method and device, equipment and a medium, and the method comprises the steps: carrying out the thinning optimization of a traffic accident scene image through employing a preset Douglas-Peucker algorithm, so as to generate a thinned and optimized traffic accident scene image; performing patch expansion and filtering on the sparse point cloud by adopting a preset PMVS algorithm to obtain dense point cloud corresponding to each traffic accident element; taking the dense point clouds corresponding to the traffic accident elements as geometric constraints, taking the thinned and optimized traffic accident scene image as a texture data source to construct a training data set, and training a preset neural radiation field model to be converged by adopting the training data set to determine a traffic accident scene three-dimensional model; and inputting a to-be-reconstructed traffic accident scene image into the traffic accident scene three-dimensional model to extract three-dimensional space information corresponding to each traffic accident element. According to the method, the accident scene investigation and modeling period can be greatly shortened, and the road blocking time is greatly shortened.

The invention discloses a traffic accident video evidence analysis system and method based on multi-modal deep learning, and relates to the technical field of deep learning, and the method comprises the steps: carrying out the target recognition and tracking through radar point cloud, extracting a target number component and a motion variance component, carrying out the calculation through normalization and linear combination, and obtaining a dynamic fusion weight of each modal; and quantization and adaptive adjustment of scene complexity are realized. Then extracting each modal feature vector in a preset time sequence window, and performing weighted fusion based on the weight to form a unified feature vector; and inputting the uniform feature vector into a pre-trained accident classification model, and outputting whether an accident occurs or not and a type result. The method has the advantages that through complementary enhancement of multi-modal data, the accident identification accuracy in complex scenes such as low illumination and rain and fog is improved; adaptive adaptation to different scenes is realized through dynamic weight distribution; the whole scheme forms a closed loop, and has high robustness and practical value.

The invention relates to the technical field of unmanned vehicles, and discloses an unmanned vehicle auxiliary lane changing decision-making method based on multi-modal fusion, and the method comprises the steps: synthesizing a visual image of a densely overlapped vehicle contour through a generative adversarial network, shielding missing laser point cloud, radar signals of clutter interference, and other analog data; samples are expanded in combination with oversampling and transfer learning, and the problem of insufficient sample size is solved. A dynamic attention mechanism is utilized, correlation modes of multi-modal features such as vision, laser and radar in the scene are mainly learned, the scene is quickly recognized through a long-tail scene adaptation module of a meta-learning framework, and decision parameters are dynamically adjusted. In training, a mixed loss function is adopted to reinforce learning, sudden plug triggering is re-evaluated during real-time decision making, and meanwhile, the model is continuously optimized by relying on a feedback iteration mechanism. Finally, the model can accurately identify a plugging scene, the safety space is judged by fusing multi-modal features, dynamic changes are dealt with, and the traffic accident risk caused by plugging is reduced.

The embodiment of the invention relates to the technical field of vehicle control, in particular to a vehicle control method, a control device and a storage medium, and the method comprises the steps: carrying out the preprocessing, feature alignment and preliminary fusion of first data obtained by all sensors, obtaining third data, and processing the third data according to a communication bandwidth demand, according to the invention, the system performance and the data transmission efficiency are improved, and it is ensured that the fourth data of each sensor are efficiently and accurately fused to generate the environmental perception data, so that the vehicle driving is controlled, the transmission delay of the vehicle control information is reduced, and the occurrence of traffic accidents is reduced.

The invention provides a smart city traffic abnormity monitoring method and system based on an Internet of Things large model, and relates to the field of Internet of Things and smart city traffic management. The system comprises an abnormity judgment module and a diversion module. The abnormity judgment module is configured to perform abnormity judgment on the target area according to the multi-source data and determine a plurality of hidden danger hot areas; the diversion module is configured to determine a plurality of standby routes according to the judgment result and the regional road network topological map; determining a plurality of main routes according to the plurality of standby routes and the position information of the plurality of variable information boards, generating a diversion instruction, and sending the diversion instruction to the emergency supervision object platform; and based on the diversion instruction, controlling a plurality of variable information boards to display a sketch of a corresponding main route, and controlling traffic lights on a plurality of standby routes to perform green light signal display according to a passing period. According to the method, the main pushing route of the variable information boards can be reasonably determined and controlled, potential traffic accident hidden dangers are prevented, and traffic safety is guaranteed.

The invention discloses an automatic driving traffic accident intelligent analysis system based on sand table simulation, and relates to the technical field of vehicle automatic driving. The system comprises a sand table simulation scene construction module which is used for simulating a high-risk scene of an automatic driving traffic accident and providing a physical and digital twinborn combined experiment environment; the automatic driving data acquisition and perception module is used for acquiring vehicle sensor data in real time and realizing environment perception and behavior decision simulation; the traffic accident intelligent analysis core module is used for realizing accident cause analysis and responsibility determination based on multi-source data and a knowledge base; and the cloud control platform and data visualization module is used for monitoring sand table simulation data in real time and providing a visual interface and an interaction function. The system can simulate and analyze the performance of automatic driving in various complex traffic environments and perform analysis and prediction, the analysis and prediction accuracy is high, and the analysis effect is better.

The invention relates to an expressway road condition monitoring system and method, and belongs to the field of expressway road condition monitoring, and the system comprises an ultrasonic sensor which is used for detecting whether branches grow into a guardrail of an expressway or not; the pressure sensor is arranged on the guardrail of the expressway and is used for detecting whether the guardrail of the expressway is bent or not; the image recognition module is used for detecting whether a traffic accident occurs in the target road section; the ESP32 single-chip microcomputer is connected with the temperature and humidity sensor, the ultrasonic sensor and the pressure sensor and gives an alarm when the branches grow into the guardrail of the expressway, whether the guardrail of the expressway is bent or not and a traffic accident happens to a target road section. According to the invention, through the strong operation processing capability and low power consumption characteristic of the ESP32, in combination with various sensors such as ultrasonic, pressure, temperature and humidity sensors of the image recognition module, complex road condition scenes can be accurately analyzed, and abnormal conditions such as traffic accidents can be timely captured, so that road condition monitoring is more comprehensive and meticulous.

The invention discloses a traffic abnormal event detection method based on multi-source data fusion, and the method comprises the steps: obtaining target detection data through a single millimeter wave radar and a plurality of industrial cameras according to the characteristics of a sensor; obtaining fusion information by associating the multi-source information; tracking the fusion target to form a target trajectory, and performing smooth filtering on the trajectory to form a filtered target trajectory; violation event detection is realized by analyzing a target track, wherein violation events comprise invasion, overspeed, underspeed, lane change, illegal parking, reverse driving and line pressing; traffic state event detection is achieved by analyzing the correlation between the lane-level detection area and target data, traffic state events include congestion, event time is aligned for event evidence leaving, and event videos and event photos are included; and for a specific event, event recheck is carried out through target track anti-shake verification, and event qualification and reporting are carried out. The traffic safety risk can be effectively and intelligently prevented and controlled, the traffic efficiency is optimized, and the traffic accident occurrence probability is reduced.

The invention relates to the technical field of traffic internet, and discloses a highway traffic situation intelligent sensing system based on vehicle-road cooperation, and the system comprises a data collection module which comprises a plurality of detection piles arranged at the exit of a high-speed ramp, and the detection piles are used for obtaining various data of a highway; the data comprises vehicle driving data, traffic accident data and weather environment data; and the situation analysis module is used for performing situation analysis on the highway ramp exit after the traffic accident based on the data acquired by the detection pile, and the situation analysis comprises the steps of calculating accident processing estimation time according to the data and further acquiring an accident influence value by combining the data. A plurality of detection piles are arranged at the exit of the high-speed ramp, so that warning can be provided for the running vehicle when a traffic accident occurs, and the situation that the vehicle occupies the emergency lane when the traffic jam occurs is further prevented.

The embodiment of the invention discloses a causal inference method for traffic accident data processing and prediction model optimization, and the method comprises the steps: constructing a multi-source heterogeneous accident database, and constructing a standardized feature matrix; based on the standardized feature matrix, extracting a feature constraint rule through a decision tree, and generating a minority class anti-fact sample conforming to domain knowledge in combination with a heuristic algorithm to form an equalized data set; inputting the equalized data set into a double-path model system for training, expanding a feature space by a data path to improve the characterization capability of a minority class, and embedding an interpretability constraint module into an interpretation path to obtain a trained model; based on the trained model, constructing a visual interpretation system comprising a feature importance map and a causal association network; and based on a visual interpretation system and the standardized feature matrix, constructing a multi-level causal network identification core influence factor, and quantifying the causal effect of intervention measures through anti-fact simulation to form a priority intervention list.

This application discloses a highway traffic diversion alarm broadcasting system and method based on AI recognition, relating to the field of highway diversion technology. This application analyzes the load of each vehicle and the driving experience, historical highway violations, and historical highway accidents of each driver to divert vehicles, diverting high-risk vehicles to safer highways, thereby ensuring vehicle safety. Different safe following distances and speeds are set for different highways to avoid traffic accidents caused by excessively close following distances or excessive speeds. Furthermore, the system monitors all vehicles traveling on each highway, broadcasting warnings when accidents or violations are detected, which enhances deterrence against drivers and prevents repeated violations from leading to traffic accidents.

The invention relates to the technical field of intelligent traffic and artificial intelligence crossing, and particularly discloses a traffic accident intelligent responsibility affirmation AI auxiliary method and system based on a large model, and the method comprises the steps: obtaining a responsibility judgment knowledge base, and processing the knowledge in the responsibility judgment knowledge base; key information of the traffic accident case is acquired, and occurrence passing information of the traffic accident case is generated according to the key information of the traffic accident case; through a large model, according to a responsibility determination knowledge base, the key information of the traffic accident case and the occurrence information of the traffic accident case, determining the wrong behavior and responsibility of the traffic accident party; and outputting error-passing behaviors and responsibilities of the traffic accident parties, and collecting actual responsibility affirmation results of the traffic accident parties to form complete feedback data of traffic accident cases. According to the traffic accident intelligent responsibility determination AI auxiliary method based on the large model provided by the invention, standardization, intelligence and high efficiency of a responsibility determination process can be realized.