2092 results about "Traffic accident" patented technology

A driving state monitoring and feedback method and system based on multi-modal human factors intelligent data analysis, and an edge computing terminal device. The method comprises: receiving multi-modal human factors data of a tested driver that is collected in real time (S110); pre-processing the multi-modal human factors data, wherein the pre-processing comprises denoising processing and data normalization processing (S120); sending the pre-processed multi-modal human factors data into a pre-trained first state recognition model, so as to obtain a real-time recognized driver state, wherein driver states include a normal state and abnormal states, and the types of the abnormal states include a plurality of states such as a fatigue state, a distracted state and an angry state (S130); and when it is recognized that the driver state is an abnormal state, generating, for different categories of abnormal states, driving state feedback instructions to a driving intervention system, such that the driving intervention system performs state feedback adjustments on the driver on the basis of the received driving state feedback instructions (S140). The method and system can recognize different driving states of a driver in real time and then perform processing on the basis of different driving states, thereby avoiding the occurrence of traffic accidents.

The invention relates to the technical field of water traffic risk assessment, particularly provides a traffic risk dynamic assessment model construction system, and solves the problem that the intelligent degree and dynamic performance of traffic risk assessment need to be further improved. According to the system, a first modeling assembly constructs a risk propagation model adopting meta-learning; the second modeling component constructs a multi-subject collaborative risk assessment model; an active intervention component; and generating an active intervention strategy. The traffic safety is improved, and the system can effectively reduce the occurrence rate of traffic accidents through real-time risk perception, accurate evaluation and active intervention; a traffic management dynamic risk map and an intervention strategy are optimized, a scientific decision basis is provided for a traffic management department, and the management efficiency is improved; through multi-channel early warning and path induction, safer and more convenient travel experience can be provided for a driver; the development of intelligent traffic is promoted, and technical support is provided for the realization of an intelligent traffic system.

The invention relates to the technical field of traffic accident analysis, and discloses an intelligent analysis method for vehicle and pedestrian collision accident liability, and the method comprises the steps: firstly obtaining multi-source heterogeneous accident data, fusing a cross-platform data source through employing a federal learning framework when the data is insufficient, and reconstructing an accident scene three-dimensional coordinate system; and then, based on a multi-modal data fusion result, constructing a traffic participation entity relation topology model by using a graph neural network, and generating an accident dynamic evolution graph. Then, establishing a collision dynamics digital twin model by utilizing a physical engine, extracting a key collision feature vector, and constructing a responsibility probability distribution model based on a generative adversarial network; and optimizing a responsibility judgment strategy by adopting a double-layer reinforcement learning framework, verifying a physical simulation result through a hierarchical verification mechanism, analyzing a responsibility judgment logic chain, and finally outputting a responsibility analysis report with an interpretable label. The method can accurately and intelligently analyze the accident liability, and has good interpretability.

The invention discloses a highway situation awareness and accident risk early warning method fusing multi-source data. The method comprises the following steps: S1, constructing multi-source data fusion and multi-scale traffic flow data; s2, studying and judging an accident influence factor mechanism, and excavating factors influencing the occurrence of a traffic accident; s3, early warning of accident risks is carried out, and the safety risk level of the road section is evaluated; and S4, researching the beforehand management and control system, and constructing a beforehand management and control strategy system based on deep mining analysis of traffic accidents and actual operation experience. According to the invention, on the basis of the high-speed portal data and the charging flow data, the detection data of the thunder-vision integrated roadside equipment are fused for fusion processing, so that fine traffic operation parameters such as traffic flow and vehicle speed before and after a historical accident are obtained; traffic flow characteristics causing accidents are mined, and a traffic accident risk dynamic early warning management and control model and a post-accident evaluation mechanism are established based on the traffic flow characteristics, so that a management department is helped to recognize potential risks in advance.

The invention discloses an unmanned aerial vehicle multi-modal feature fusion target tracking method and system based on natural language description, belongs to the technical field of computer vision and image processing, and solves the problem that in the prior art, when the quality of an image collected by an unmanned aerial vehicle is poor or image features are not obvious, the target tracking capability and the long-time tracking capability are poor. Natural language description is carried out on a traffic accident scene in an image of an unmanned aerial vehicle visual angle, and a language prompt is obtained; constructing a scene-context feature pyramid network to perform context information enhancement processing on the image of the view angle of the unmanned aerial vehicle to obtain a feature-enhanced image; respectively carrying out visual coding and language coding on the enhanced image and language prompt to obtain visual features and language feature vectors, and carrying out visual-language bimodal feature local alignment; and fully fusing the obtained aligned new language features with the visual features to obtain multi-modal features for target tracking. The method is used for multi-modal feature fusion target tracking of the unmanned aerial vehicle.

The invention discloses a built environment and traffic accident causal effect analysis method based on bi-robust learning, and belongs to the field of intelligent traffic safety and traffic accident risk assessment. By integrating satellite remote sensing, interest point distribution and traffic accident data, a spatial-temporal feature enhanced environment index system is constructed; realizing cross-regional data collaborative analysis by adopting federal learning, and guaranteeing data compliance in combination with differential privacy encryption; a generative adversarial network is introduced to generate a high-fidelity anti-fact scene, and a dual robust learning framework is coupled to accurately quantify a condition average processing effect of a built environment on accident risks. According to the scheme, a technical tool with both theoretical preciseness and practical feasibility is provided for urban traffic planning and safety management, and a scientific basis is provided for optimizing and building environment design and reducing traffic accident risks through a multi-source data fusion and privacy cooperation mechanism; and the blank in the aspects of causal robustness, insufficient anti-fact generation precision, privacy protection and multi-source data fusion in the prior art is filled.

The invention discloses a smart city traffic resource optimal configuration method, a platform and a storage medium, and relates to the technical field of traffic optimization. When a road accident is detected, accident information is collected, and a traffic accident position and traffic accident characteristics are obtained; time consumption prediction is carried out according to the traffic accident characteristics, and traffic control duration is obtained; calling real-time traffic data from an intelligent traffic system to obtain associated road data; executing multi-thread traffic flow back-stepping analysis to obtain associated traffic flow prediction information; carrying out shunting performance evaluation; and dynamically updating the signal lamp configuration of the plurality of shunting candidate roads by taking the associated traffic flow prediction information as a constraint. According to the invention, the technical problems of aggravation of traffic jam and low emergency management efficiency caused by lack of scientificity and dynamic adaptability of shunting planning and lagging of signal lamp regulation in a traffic accident scene in the prior art are solved, and the technical effects of optimizing traffic resource configuration, relieving traffic jam and improving traffic emergency management efficiency are achieved.

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 an end-cloud cooperative detection method and system for traffic anomalies, and relates to the technical field of intelligent traffic control. The method comprises the following steps: collecting traffic video streams through edge equipment, and identifying abnormal behaviors and generating structured event data by using a lightweight YOLOv3-tiny model; when the confidence exceeds a dynamic threshold and the event type is a high-risk type, uploading a video clip and data to a cloud; the cloud integrates a historical road condition map, meteorological data and a real-time traffic flow state, reconstructs a three-dimensional event scene by adopting a space-time attention pyramid network, and verifies the authenticity of an event in combination with a traffic flow sudden change detection algorithm; and generating a signal lamp forced switching instruction for the risk level overrun event, and issuing the signal lamp forced switching instruction to roadside equipment within 3 seconds to execute emergency response. According to the invention, full-link closed-loop control of traffic accidents from identification to response is realized, and the false alarm probability is greatly reduced while the identification accuracy is guaranteed.

The invention provides a traffic participant behavior analysis-based edge calculation signal lamp control method and system, and the method comprises the steps: obtaining video stream data of a target intersection, carrying out the frame-by-frame behavior state analysis of the video stream data through a spatial-temporal feature coding network, extracting the behavior state features of each traffic participant, and carrying out the analysis of the behavior state features of each traffic participant; and inputting the behavior state characteristics into a preset behavior matching model, generating a behavior triggering identifier of the traffic participant in the current time window, generating a signal lamp control instruction of an edge computing node according to the time sequence relevance between the behavior triggering identifier and the phase state of the current signal lamp, and sending the signal lamp control instruction to the edge computing node. And finally, a signal lamp phase switching time sequence of the target intersection is adjusted based on the signal lamp control instruction, so that the traffic participants meeting the traffic rule conflict condition obtain the traffic priority under the signal lamp phase switching time sequence. According to the method, the traffic accident risk is reduced, and meanwhile, the overall traffic efficiency of the intersection is optimized by dynamically adjusting the minimum response period and the phase duration parameters.

The invention discloses a driver fatigue driving state recognition method based on deep learning, and relates to the technical field of computer vision recognition methods, and the method comprises the following steps: S01, multi-modal data collection, S02, data preprocessing, S03, data enhancement, S04, multi-modal feature fusion, S05, deep learning model construction, S06, fatigue driving state recognition, and S07, real-time feedback and optimization. S08, a fatigue driving state recognition result is output, the fatigue characteristics of the driver can be captured from multiple dimensions by fusing multi-modal data including video images, vehicle driving data, environment data, voice signals and physiological signals, compared with the prior art, the scheme can more comprehensively analyze the fatigue state of the driver, and the fatigue driving state recognition result is obtained. Therefore, traffic accidents caused by fatigue driving are reduced.

The invention discloses a multi-source traffic data information extraction and intelligent responsibility judgment method, system and device, and relates to the technical field of traffic accident data processing and analysis, and the method comprises the steps: inputting traffic accident related data into a pre-constructed multi-modal large model, and generating a brief description text of traffic accident passing; retrieving legal provisions related to the traffic accident from a pre-constructed legal knowledge base according to the brief description text; and inputting the brief description text and the legal provisions into a pre-constructed Traffic-T5 model, and automatically generating a responsibility affirmation result. According to the method, unified modeling and processing are performed on the text and image data by adopting the multi-modal large model, the efficiency and accuracy of accident information extraction are improved, the multi-modal large model is optimized, the understanding and analysis ability of the multi-modal large model on incomplete traffic accident data is enhanced, and a legal knowledge base is combined, so that the method is suitable for popularization and application. Dynamic retrieval and application of traffic laws and regulations are realized, and the responsibility division result is ensured to have a basis.

The invention discloses an intelligent traffic data processing method and system based on a 5G communication system, and relates to the technical field of traffic data processing, and the method comprises the steps: collecting data based on a 5G communication network; preprocessing the collected traffic data through an edge computing node; real-time prediction and risk assessment are carried out on traffic flow based on the cloud platform; sending real-time traffic information and optimization suggestions to a traffic management center and related vehicles; when an emergency occurs, a rapid response mechanism is triggered through a 5G network, and traffic resources are scheduled. The system collects, processes and analyzes traffic data in real time through the characteristics of high bandwidth and low delay of the 5G communication network and the distributed processing capability of edge calculation, achieves the real-time prediction and management of traffic flow and accident risks, and can improve the road utilization efficiency, reduce traffic accidents and improve the traffic safety through the intelligent decision and optimization control strategy. And the emergency response capability is improved, so that a more intelligent solution is provided for urban traffic management.

The invention belongs to the technical field of intelligent traffic, and discloses a space-time fitting-based whole vehicle type traffic feature accurate recognition algorithm, which comprises the steps of obtaining driver behavior data, vehicle dynamic data and road supervision data; preprocessing the driver behavior data, the vehicle dynamic data and the road supervision data to obtain a driver behavior feature data set, a vehicle dynamic feature data set and a road supervision feature data set; according to the vehicle dynamic feature data set and the road supervision feature data set, obtaining a vehicle initial driving track; optimizing the initial driving track of the vehicle through an improved space-time interpolation algorithm to obtain a complete driving track of the vehicle; according to the complete driving track of the vehicle, the vehicle driving speed of each road section is calculated and compared with the limiting speed of the corresponding road section, and whether the vehicle is overspeed is judged; and traffic accidents caused by overspeed, fatigue driving and the like are effectively avoided.

The invention provides a traffic accident prediction method fusing multi-source features and a self-adaptive structure, and the method comprises the steps: extracting spatial features such as a geographic position, traffic flow and interest point distribution, combining the time features such as traffic flow change trend, periodicity and anomaly detection, and the external features such as weather and signal lamp density, and carrying out the prediction of a traffic accident. Node multi-dimensional feature representation is comprehensively constructed, a static adjacency matrix and a dynamic adjacency matrix are respectively constructed, geographic distance and node feature similarity information are fused, a self-adaptive adjacency matrix is generated by utilizing learnable parameters, and road network structure changes are dynamically described. Finally, traffic accidents are modeled and predicted based on a graph convolutional neural network, and accurate identification and early warning of accident risks in a complex traffic environment are realized. According to the method, the modeling capability of the prediction model for nonlinear and strong space-time correlation characteristics of traffic data is effectively improved, the accuracy and robustness of traffic accident prediction are remarkably improved, and the method has wide engineering application prospects and popularization value.

The invention, which relates to the technical field of highway traffic safety monitoring, discloses an expressway pavement ice condensation monitoring system comprising a data acquisition module for acquiring environmental data and pavement icing state information on an expressway pavement; and the self-calibration module is connected with the data acquisition module, performs self-calibration on the data acquisition module according to the environment data acquired in real time and the road surface icing state information, and corrects sensor errors caused by external environment factors. According to the highway pavement ice condensation monitoring system, a self-calibration function is added in monitoring equipment and an intelligent algorithm is introduced, so that the monitoring system can automatically correct measurement errors caused by external environment changes in real time, and a machine learning model is combined to predict pavement conditions more accurately; the working stability of the system in severe weather can be improved, and the risk of icing of the road surface can be found earlier, so that more accurate early warning information is provided for a traffic management department, and the probability of traffic accidents is reduced.

The invention relates to the technical field of intelligent traffic systems, in particular to a curve early warning method, system and terminal based on target detection, and the method comprises the steps: generating a visual twin image of a target curve; the visual twin images are displayed on display devices on the two sides of a target curve; if the movement track of the moving target is overlapped with the position of the obstacle or the movement track of the moving target is overlapped with the estimated movement track of the obstacle, early warning information is sent, and the early warning information comprises sound prompt information and image prompt information. The graphic prompt information comprises that a visual twin image corresponding to the moving target is rendered on a display device on one side of the road consistent with the moving direction of the moving target. A visual twin image of a target curve is generated through a multi-sensor fusion technology, the motion states of a moving target and an obstacle are reflected in real time, and early warning information is sent out when a potential collision risk is detected, so that the traffic accident rate of a curve area is effectively reduced.

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 traffic accident risk prediction method and system based on space-time hypergraph comparative learning, and belongs to the field of traffic accident prediction, and the method comprises the steps: firstly obtaining historical traffic accident risk values and corresponding space-time features through data preprocessing; then, traffic accident area embedding based on local geographical perception and global semantic perception is generated through a multi-channel attention convolutional network and a hypergraph network, and dynamic time characteristic evolution is captured by adopting a GRU model and an Attention mechanism; in order to alleviate the problem of unbalanced spatial distribution of accident data, a comparative learning normal form is adopted to carry out cooperative supervision training on local and global region embedding so as to obtain robust accident characterization; and finally, integrating the ZINB model into traffic accident representation to better fit the probability distribution of sparse discrete data, thereby improving the accuracy of a model prediction result.

The invention provides a traffic flow spatio-temporal feature adaptive extraction method based on a dynamic Kolmogorov-Arnold network, and aims to solve the defects of a traditional model in the aspects of dynamic spatio-temporal modeling, structural adaptability and feature expression efficiency. According to the method, a network structure is dynamically adjusted through differential topology search and a parameterized primary function library, a double-flow coupling architecture is designed to extract space and time dependent features respectively, and adaptive fusion of spatial and temporal features is realized by using a gating mechanism. The method comprises the following specific steps: performing space-time normalization and graph structure coding to generate a node feature matrix; extracting multi-scale spatial-temporal features in parallel by the dynamic graph convolution KANs and the time sequence convolution KANs; feature alignment and cooperative enhancement are realized based on a local correlation matrix and a bidirectional interactive attention mechanism; and the lightweight KAN decoder is combined with the dynamic basis function library to output a prediction result. Experiments show that the MAE is reduced to 1.78 vehicles per minute and the reasoning speed is improved by 1.8 times in a traffic accident emergency scene on a PeMS08 data set, and the method is suitable for an intelligent traffic management and vehicle-road cooperation system.

The invention relates to the technical field of intelligent traffic, in particular to an intelligent traffic accident prediction and emergency response system and method based on vehicle-mounted data. The method comprises the steps of collecting vehicle-mounted data, and then performing denoising processing on the vehicle-mounted data by using an adaptive convolutional neural network; constructing an intelligent traffic accident prediction model based on the de-noised data, capturing key features in time sequence data by using a bidirectional LSTM, an attention mechanism, residual connection and a Dropout layer, and predicting an accident occurrence probability; according to a prediction result, an early warning threshold value is dynamically adjusted, alarms of different levels are triggered, and corresponding measures are taken through automatic emergency response or driver reminding; through collecting data before an accident, a prediction model and an emergency response strategy are optimized, prediction precision is continuously improved, and traffic management efficiency is enhanced. According to the invention, vehicle-mounted data and an intelligent algorithm are comprehensively utilized, accurate accident prediction and efficient emergency response are provided, and traffic safety and traffic efficiency are improved.

A road traffic event analysis method and system, and a device and a medium. The traffic event analysis method comprises: decoding and coding the current monitoring video, so as to obtain a target video picture (S102); calibrating the target video picture, so as to obtain a calibrated video picture (S103); performing preliminary analysis on the calibrated video picture, so as to obtain a preliminary analysis result (S104); determining state change data on the basis of the preliminary analysis result (S105); compiling statistics on a traffic flow, so as to determine peak time periods and plateau time periods of a road traffic site (S106); performing preliminary detection on frame scenes on the basis of pre-determined detection parameters corresponding to the peak time periods and the plateau time periods, so as to obtain a preliminary detection result (S107); excluding congestion events from the preliminary detection result (S108); and tracking targets, and determining motion state features, so as to obtain a final detection result and report same (S109). The scale of a traffic accident event triggered in the detection result and the statistical result are used for adjusting the detection parameters corresponding to the peak time periods and the plateau time periods. The traffic event analysis method can improve the detection accuracy of road traffic events.

The invention relates to an automatic driving simulation test scene generation method based on a text traffic accident report, and aims to apply a generative language model technology in the field of natural language processing, a code generation technology in the field of program synthesis and a fuzzy test theory in the field of software test. The problem that diversified and standardized high-quality simulation test scenes are lacked in the current automatic driving test field is solved, and the method can generate diversified scene files in the OpenSCENARIO format on the basis of natural languages, and mutates seed scenes through multiple transformation operators so as to efficiently discover defects existing in an automatic driving system.

The invention relates to the field of vehicle intelligent control, in particular to a dynamic target prompting method and device, electronic equipment and a storage medium. The method comprises the steps of obtaining a dynamic target and motion data thereof in a vehicle driving environment; then, the driving direction of the vehicle is detected, the motion data is analyzed, a traversing dynamic target with a crossing trend is identified, and potential dangers are locked in advance. And then, detecting whether a collision risk target object exists between the transverse moving target and the transverse moving target based on the reverse driving direction, and if the collision risk target object exists, executing a safety prompt operation on the transverse moving target. Therefore, a set of complete active safety warning mechanism is constructed, other vehicles and pedestrians can timely perceive the danger, compared with the prior art, the system is not in the state of passively waiting for the danger, the safety of the vehicles and the pedestrians in the road environment is greatly improved, and the occurrence probability of traffic accidents is reduced.

The invention discloses a road traffic accident monitoring and alarming method and system, and relates to the technical field of data processing. The method comprises the following steps: acquiring vehicle speed values, vehicle distance values and driving tracks of a plurality of vehicles on a road to be monitored; calculating a speed change rate according to the vehicle speed value of each vehicle, calculating a distance change rate according to the vehicle distance value of each vehicle, and calculating a vehicle transverse offset according to each driving track; when the speed change rate, the distance change rate or the vehicle lateral offset exceeds the corresponding standard threshold value, determining that a traffic accident occurs on the road to be monitored; receiving the lane occupancy rate, the traffic flow density and the average passing speed of the road to be monitored; and determining an accident grade corresponding to the traffic accident based on the lane occupancy rate, the traffic flow density and the average passing speed, and generating alarm information of the accident grade corresponding to the traffic accident. By implementing the technical scheme provided by the invention, the effect of improving the road traffic accident monitoring and alarming efficiency is achieved.

The invention discloses a traffic state monitoring method and system fusing video image recognition and charging data, and the method comprises the steps: obtaining multi-source traffic data, carrying out the preprocessing of the multi-source traffic data, and generating preprocessing data; identifying the pre-processed data to generate vehicle related information; fusing and matching the preprocessed data and the vehicle related information to generate fused traffic data; analyzing the fused traffic data to generate traffic state information; a traffic management and decision support result is generated, and real-time congestion early warning and traffic accident early warning are provided; and mining and analyzing the historical traffic data to generate a traffic flow rule and accident rule analysis result. According to the invention, comprehensive, accurate and real-time monitoring and analysis of the traffic state are realized, powerful support is provided for traffic management and decision making, and the problems of difficult integration of multi-source data, insufficient real-time performance and coverage of state monitoring and imbalance of decision support and cost efficiency are solved.

The invention discloses a dual-stage resource reservation and scheduling system for vehicle networking sudden accident emergency calculation, and relates to the technical field of edge calculation and intelligent traffic. The system comprises a traffic situation sensing module, a risk prediction decision module, a resource pre-freezing execution module, a task grading analysis module and a hybrid scheduling optimization module. The system innovatively divides a traffic accident handling process into an early warning stage and a response stage: in the early warning stage, the system adjusts a resource reservation ratio of edge nodes based on traffic flow density and historical task load conditions; in a response stage, the system performs hierarchical scheduling according to task types and dependency relationships by using a mixed integer programming model and a priority perception strategy. Through an end-side cloud collaborative architecture and a resource reservation mechanism, the system can significantly improve the task processing efficiency under emergencies and reduce the response time delay of high-priority tasks, has good expansibility and practicability, and is suitable for various vehicle networking application scenes such as intelligent expressways and urban traffic.

The invention provides an elastic traffic control method considering road accumulated water distribution. The method comprises the steps of collecting road surface point cloud data and obtaining a road three-dimensional model; utilizing computational fluid mechanics simulation software to construct a road ponding simulation model under the rainfall condition; according to the simulation result, calculating the critical water skiing speed corresponding to the accumulated water depth, and comparing the critical water skiing speed with the lane speed limit to judge the water skiing risk; the sections with the water skiing risk are divided into risk sections; reducing the maximum limit driving speed of the risk section; dividing a high-risk section by comparing the maximum speed limit with a preset threshold value, and performing bypassing adjustment; by comparing the proportion of the risk section with a preset safety threshold value, a channel sealing measure is taken; and real-time weather and road conditions are continuously monitored and timely adjusted. By means of the method, the lane speed limit and the traffic flow direction are dynamically adjusted, tackle and traffic accidents caused by accumulated water on the road surface are effectively prevented, the water skiing risk of vehicles under the rainfall condition is reduced, and the road traffic safety is ensured.

The invention discloses a road traffic information monitoring system and method, and the method comprises the steps: deploying a video monitor, a sensor, an unmanned plane and a satellite device to collect real-time road traffic data, and protecting the data safety through encryption transmission and multilayer identity authentication; the flow is monitored in real time by combining image recognition and sensor data, congestion is analyzed, traffic violation is recognized, and an alarm is given; future flow is predicted through machine learning and big data analysis, and the signal lamp period is intelligently adjusted; through combination of real-time and historical data, accident risk points are identified and early warning is carried out, and early warning is automatically issued and adjustment and control are carried out for severe weather. The AI technology automatically identifies accidents and notifies a management center, dispatches emergency resources, optimizes signal lamps and lane traffic, and dynamically adjusts surrounding flow; according to the road traffic information monitoring system and method, an automatic emergency processing system is established, accidents are automatically detected and alarms are triggered under the conditions of traffic accidents and emergencies, and emergency resources are quickly dispatched.

The invention discloses a traffic accident detection method based on FFC and GCSA models. The method is innovatively improved based on a YOLOv8 network model. Firstly, a multi-scale feature fusion module FFC is designed in Backbone to replace an original C2f module, and the capability of fusing the multi-scale features of the network can be further improved on the premise of not increasing the network calculation amount, so that the network can fuse the multi-scale features on the level of finer granularity; then, a GCSA attention mechanism is introduced between a C2f module and a Deect module of a Neck layer, and the recognition capability of the model for key features in a complex environment is remarkably enhanced; and finally, establishing a loss function Focal-EIoU Loss for the improved YOLOv8 network structure, so that the network classification detection capability is improved, and the generalization capability of the model is improved. In a specific implementation process, a traffic accident image data set covering multiple scenes is constructed, and specialized data preprocessing is performed; then, end-to-end training and parameter optimization are carried out on the Traffic-YOLO network model obtained after improvement; and finally, integrating the optimized model to a traffic accident detection system for real-time target detection. Compared with the prior art, the method effectively improves the accuracy and robustness of traffic accident detection in a complex scene, and has important practical significance.