1519 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 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 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 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 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.

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 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 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 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.

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.

The invention discloses an intelligent parking navigation system based on image recognition and the Internet of Things, and relates to the technical field of intelligent parking navigation, the system draws a three-dimensional electronic map of a parking lot, divides the parking lot into monitoring areas, deploys sensors and cameras to collect image data, and transmits the image data to a cloud through the Internet of Things. The intelligent regulation and control model module uses a convolutional neural network CNN to carry out parking space recommendation and path guidance. And the vehicle monitoring module monitors vehicles entering the parking lot in real time, calculates a geometric environment adaptation coefficient and evaluates a traffic risk. And the vehicle guiding analysis module calculates the traffic flow and the parking space idle index, and recommends a proper parking area for the vehicle. The parking space recommendation analysis module evaluates the comfort level of the parking space, ensures the safety of backing and parking, and avoids the risk of traffic jam or traffic accident through strategy adjustment. The system improves the parking efficiency and safety, and optimizes the parking experience.

The invention relates to the technical field of traffic accident traceability, and discloses an unmanned aerial vehicle routing inspection traffic accident traceability method for a complex scene, which comprises the following steps: establishing an unmanned aerial vehicle cluster cooperative control system to carry out accident scene data acquisition; performing multi-source deep data fusion on the acquired data through a deep spatial-temporal feature network; and inputting the fusion data into a causal reasoning enhanced accident traceability analysis model constructed based on an improved Harris eagle optimization algorithm, and generating an accident traceability analysis report. According to the causal reasoning enhanced accident traceability analysis model constructed based on the Harris eagle optimization algorithm, the global search capability in a high-dimensional feature space is improved through optimization of adaptive weight adjustment and a chaos search strategy, and the accuracy and interpretability of a traceability result are improved.

The invention discloses a multi-source data fusion and intelligent analysis method for an intelligent traffic enterprise, and relates to the technical field of intelligent traffic, and the method comprises the following specific steps: S100, collecting video frame data, radar track data and voiceprint data of the traffic enterprise in real time, S200, calculating a quantized video definition score, S300, calculating a radar signal-to-noise ratio, and S500, calculating a video definition score. According to the method, the video, radar and voiceprint data quality can be accurately judged through multi-modal data quality real-time evaluation and credibility quantification, and a cross-modal attention weight matrix is dynamically adjusted according to a quality evaluation result in a complex environment, that is, when the video quality is reduced, the video, radar and voiceprint data quality can be accurately judged; the system automatically increases the radar data weight, ensures that high-quality data source key information is always focused, learns more accurate and comprehensive abnormal characteristics, effectively reduces the traffic accident rate, and guarantees the road traffic safety.

The invention relates to an emergency rescue method and device based on driver health monitoring, a vehicle and a medium. The method comprises the following steps: acquiring real-time health state data and a current driving scene type of a driver in a vehicle driving process, and performing weight distribution on each monitoring dimension of the real-time health state data based on the current driving scene type to obtain a monitoring data group corresponding to the current driving scene type; determining a current alarm level under the condition that the driver is judged to be in a preset abnormal state based on the monitoring data group, and selecting a corresponding auxiliary driving strategy and a corresponding emergency rescue strategy based on the current alarm level and the current driving scene type; the current vehicle is controlled to run based on the corresponding auxiliary driving strategy, and / or emergency rescue is carried out on the driver based on the corresponding emergency rescue strategy. Therefore, the problem that traffic accidents occur due to the fact that the abnormal state of the driver is not monitored in time in the prior art is solved, and driving safety is improved.

The invention relates to the field of biological signal processing, and discloses an EEG signal fatigue detection method based on a time sequence enhanced hybrid architecture, which designs a time sequence perception extrusion-excitation module, adaptively performs attention weighting on a time dimension, effectively extracts key time sequence features, and improves the accuracy and reliability of fatigue detection. A fixed position code is replaced by an LSTM time embedded code, so that the attention of the model on important information in the time dimension is further enhanced; a mixed framework combining a space selection module and a Transform multi-head attention module is designed, and the long context sequence sensing ability and generalization ability are enhanced; a learnable activation function is introduced to replace a fixed linear weight, and the classification capability of the model is enhanced through nonlinear representation such as a spline function. According to the method, fatigue signs of EEG signals can be effectively captured, and the method has remarkable advantages in detection of different subjects and has important practical application value in reduction of traffic accidents caused by driver fatigue.

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 invention belongs to the field of traffic safety management, and discloses a traffic accident severity influence factor analysis method based on local cascade integration, which comprises the following steps: acquiring an accident data set D1; processing the accident data set D1, removing part of redundant attributes, dividing the accident data set D1 into a training set and a test set according to a proportion, and balancing the number of accidents with different severity degrees in the training set through an SMOTENC algorithm to obtain an accident data set D2; importing the accident data set D2 into a local cascade integration model, adjusting hyper-parameters of a local model through a Hyperpt method, and selecting an optimal hyper-parameter combination by using k-fold cross validation; drawing a confusion matrix according to a training result, and selecting indexes to evaluate model performance; and visualizing the model by applying a machine learning output explanation tool SHAP, and analyzing accident severity influence factors according to the visualized model. By adopting the SMOTENC resampling technology, the number of various accidents in the training set is balanced and the model training effect and the classification performance are improved on the premise of considering discrete and continuous variable differences.

The invention provides a real-time warning condition data processing method and system based on edge calculation, and relates to the technical field of traffic management, which comprises the steps of collecting traffic warning condition original data, transmitting the data to a nearby edge server in real time, constructing a lightweight deep learning model on an edge side, generating a traffic situation prediction result, and simultaneously, combining police force information to obtain a real-time warning condition prediction result. And constructing a police deployment optimization matrix, uploading the matrix to the cloud platform, generating a traffic optimization strategy, and issuing the traffic optimization strategy to the edge computing node of the accident area. The method can improve the integrity and timeliness of accident information perception, optimizes the police resource distribution and traffic dispersion decision, and remarkably enhances the intelligence and cooperation capability of traffic accident emergency management.

The invention discloses a violation snapshot method based on image recognition, which relates to the related technical field of traffic video monitoring and comprises the steps of environment perception, scene analysis, scene feature library establishment, multi-modal data acquisition, server model aggregation, model issuing and fine adjustment and violation behavior recognition. The invention further discloses a violation snapshot system based on image recognition. The violation snapshot system comprises an environment sensing module, a data acquisition module, a data processing center, a violation behavior recognition module and a snapshot and recording module. Different from a fixed snapshot strategy of a traditional method, the system can dynamically adjust the snapshot strategy according to real-time understanding of a traffic scene, the system can automatically improve the snapshot frame rate when detecting a high-risk scene in which a traffic accident is about to occur, and the snapshot frame rate can be automatically increased in a road section in which the traffic flow is small and the scene is simple. Snapshot resource consumption can be properly reduced; therefore, a complex and changeable traffic environment can be better dealt with, and the snapshot effectiveness and the resource utilization efficiency are improved.

The invention provides a vehicle control method and system and computer equipment, and the method comprises the steps: obtaining multi-modal data of a first target vehicle, determining lamp language features based on the multi-modal data, mapping the lamp language features into lamp language semantic tags through employing a pre-obtained vehicle lamp language coding table, and storing the lamp language semantic tags in a server; and the second target vehicle is controlled according to the lamp language semantic tag, so that the first target vehicle executes or stops a driving intention corresponding to the lamp language semantic tag of the first target vehicle based on a control result of the second target vehicle. According to the method, the multi-modal data is acquired to identify the lamp signal, the problem that a single sensor is easily interfered by the environment to cause misjudgment can be solved, and meanwhile, the driving behavior or driving intention of the vehicle is predicted according to the lamp characteristics, so that a coping strategy is formed in advance, sudden or high-risk situations are effectively avoided, the traffic accident probability is reduced, and the driving safety is improved. And multi-vehicle bidirectional cooperative interaction can be realized in combination with the information of the traffic participants.

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.