133 results about "Traffic violation" patented technology

The invention relates to the technical field of behavior recognition, in particular to an image library construction and recognition algorithm under a large-scale incomplete multi-view and multi-modal scene, which comprises the following steps of: deploying a plurality of sensors and cameras in a traffic scene, and unifying multi-modal data into the same space-time coordinate system; fusing the multi-modal data subjected to space-time alignment, predicting a target state and updating a measurement value; dynamically generating multi-view candidate views based on the fused data by using an adversarial generative network under space-time constraint, and complementing missing views in combination with a target kinematics model; a target kinematic model and a deep learning technology are fused, a cross-camera target is tracked, a traffic regulation ontology library is constructed, and traffic illegal behaviors are automatically identified and classified in combination with an image identification technology. According to the method, the consistency of different sensor data in time and space dimensions is ensured, and data chaos and errors caused by space-time differences are avoided.

The invention relates to a traffic violation behavior detection system based on unmanned aerial vehicles. The traffic violation behavior detection system comprises a flight control platform and a plurality of unmanned aerial vehicles in communication connection with the flight control platform, the flight control platform is used for issuing flight instructions to the plurality of unmanned aerial vehicles and receiving image data returned by each unmanned aerial vehicle; performing data processing and trajectory tracking on the image data, extracting key features in a geographic coordinate trajectory of each vehicle, and comparing the key features with violation judgment rules corresponding to the current waypoint to obtain violation results; if the violation result is that violation is confirmed, a surrounding evidence obtaining instruction is sent out; the plurality of unmanned aerial vehicles are used for receiving and executing the flight instruction issued by the flight control platform, autonomously flying according to a preset cruise route, acquiring image data of a waypoint in charge in real time and returning the image data to the flight control platform; and after the surrounding evidence obtaining instruction is received, the surrounding evidence obtaining module is also used for starting the surrounding evidence obtaining mode to fly to the illegal vehicle and obtaining the information of the illegal vehicle. According to the invention, efficient and instant response and disposal of illegal behaviors are realized.

The invention discloses an intelligent traffic evacuation method based on dynamic path optimization and multi-objective cooperation, and relates to the technical field of intelligent traffic and emergency management, and the method comprises the steps: building an improved HD-PSO model based on a traffic violation risk space-time distribution diagram, calculating the risk index of a path node through a dynamic congestion impact factor matrix, and calculating the risk index of the path node; a four-layer dynamic early warning mechanism is constructed, and a real-time risk index and a hierarchical response instruction are output; on the basis of the optimized evacuation path weight parameters and resource allocation factors and in combination with a real-time environment state, through a DRL-LSTM hybrid model, predicting people flow density and dynamically updating the path weight parameters, and generating an optimized evacuation strategy; the improved HD-PSO model is used for outputting the Pareto optimal solution, the evacuation time is compressed through the fitness function, the accident rate is reduced, and the resource utilization rate is improved.

The invention relates to an intelligent traffic signal control method and system based on traffic flow prediction, and belongs to the technical field of intelligent traffic. Traffic vehicle video streams and traffic flow data are collected through a high-definition camera and a traffic flow sensor group, different vehicles and speeds, directions and movement tracks thereof are identified through the traffic vehicle video streams, special vehicles are judged, traffic violation behaviors and road abnormal conditions are monitored according to the traffic vehicle video streams and the traffic flow data, and the traffic violation behaviors and the road abnormal conditions are monitored. A traffic prediction model is established according to traffic vehicle video streams and traffic flow historical record data, and traffic flow, vehicle positions, vehicle categories, lane line information, vehicle movement tracks, traffic violation, road abnormal conditions, and traffic flow values and vehicle speeds in future time periods of all lanes at all intersections are obtained from a server in real time. And different signal lamp control schemes are generated according to different vehicles. According to the invention, a signal control scheme is designed in combination with vehicle types, traffic violation, road abnormity and other conditions, and accurate traffic congestion dispersion is realized.

The invention discloses a traffic comprehensive law enforcement situation awareness, study and judgment system based on big data and cloud computing, and relates to the technical field of traffic law enforcement management. The problem that a traffic law enforcement platform based on edge calculation has calculation and storage bottlenecks and cannot timely and accurately recognize novel and rare traffic illegal behaviors when dealing with extremely complex traffic scenes and mass data is solved. According to the invention, the law enforcement scene and the vehicle abnormal condition are identified through data collected by various devices, illegal behaviors are identified and early warned in time by combining audio and video analysis, the law enforcement accuracy and efficiency are improved, the road safety and the market order are guaranteed, the traffic law enforcement situation model carries out clustering analysis on traffic data, a visual situation map is generated, and law enforcement decision is assisted. Resource configuration is optimized, illegal trend is predicted, a targeted scheme is formulated, law enforcement scientificity is improved, the system is optimized by analyzing feedback information of law enforcement officers, key information is pushed in real time, collaborative law enforcement is promoted, and comprehensive law enforcement efficiency is improved.

The invention discloses an intersection traffic violation total factor snapshot system based on multi-view collaboration, and relates to the technical field of intelligent traffic, and the system comprises a data collection and transmission module, a data preprocessing and splicing module, a data optimization and analysis module, a traffic violation judgment module, and a data storage and management module. According to the invention, through a panoramic video optimization unit in the data optimization and analysis module, the spliced panoramic video is deeply optimized, and tiny splicing traces or image local distortion generated in the splicing process is further eliminated, so that the panoramic video picture is clearer, smoother and more real, and high-quality image materials are provided for subsequent analysis; and the target in the video is accurately recognized through the image recognition and analysis unit, target feature information of vehicles, pedestrians and traffic signs and marked lines is extracted, traffic illegal behaviors can be more accurately judged based on the information, and the situations of misjudgment and missed judgment are reduced.

The invention discloses a non-motor vehicle passenger-vehicle identification method and system in a crowded scene, and belongs to the technical field of intelligent traffic management. In order to solve the problem of accurate recognition of non-motor vehicles, persons and vehicles in a crowded scene, image data of a traffic intersection is collected through a traffic camera, target detection processing is performed on the image data by using a target detection model, and a person target and a non-motor vehicle target are detected to obtain a person target frame and a non-motor vehicle target frame; judging whether the scene category to which the image data of the traffic intersection belongs is a crowded scene or not to obtain crowded scene image data; personnel key points and non-motor vehicle key points in the crowded scene image data are extracted; associating the personnel key points with the non-motor vehicle key points to determine personnel key points associated with the non-motor vehicle key points to obtain a personnel-vehicle association target; and identifying whether the person-vehicle associated target has traffic violation behaviors or not through an image tracking technology, and if so, identifying the identity information of the person target for traffic violation early warning and processing.

The invention relates to a low-altitude road inspection system with AI analysis and 5G communication, and relates to the technical field of unmanned aerial vehicle remote 5G monitoring data processing, and the system comprises a plurality of inspection unmanned aerial vehicles, a ground control center and a 5G communication base station. And the inspection unmanned aerial vehicle flies at a low altitude according to a preset route, shoots road conditions by using the image acquisition module, and transmits image data to the ground control center in real time through the 5G communication base station. The AI analysis system of the center analyzes the images, accurately recognizes abnormal conditions such as road diseases, obstacles and traffic violation behaviors, and feeds back results for workers to make decisions. The system integrates AI analysis and the 5G communication technology, achieves efficient, intelligent and real-time road inspection, improves the inspection efficiency and accuracy, reduces the labor cost, discovers road abnormity as soon as possible, and guarantees the safety and smoothness of the road.

The invention discloses a highway traffic situation intelligent early warning method, and relates to the technical field of big data analysis, and the method comprises the steps: obtaining a historical traffic violation record of a target vehicle, quantifying the basic risk coefficient of each traffic accident type of the target vehicle and performing correlation fitting on the basic risk coefficient and the prior risk probability of the historical traffic accident type in a plurality of known scenes to generate the comprehensive risk index of each traffic accident type of the target vehicle in the plurality of known scenes and the environmental factor of each scene; establishing a local contribution dynamic quantization function of the environmental factors of each scene, and endowing the real-time environmental factor contribution weight of each scene of the expressway and each traffic accident type basic risk coefficient of the target vehicle to carry out traffic accident type weighted regression analysis of each scene; and generating a real-time traffic accident risk map of each scene of the expressway. The method has the beneficial effects that the early warning accuracy and the real-time performance are remarkably improved, and full-chain decision support is provided for highway safety management.

The invention provides a traffic violation event identification method and system based on fusion data, and relates to the technical field of computers. The method comprises the following steps: firstly, mining a traffic scene semantic vector corresponding to a traffic scene image and a radar point cloud semantic vector corresponding to radar point cloud data; secondly, performing cross attention mapping on the traffic scene semantic vector and the radar point cloud semantic vector to form a first attention vector, a second attention vector and a third attention vector; then, performing cross attention fusion on the first attention vector, the second attention vector and the third attention vector based on the image shielding parameter and the point cloud shielding parameter to form a traffic scene fusion vector; and finally, illegal event identification is carried out based on the traffic scene fusion vector, and an illegal event identification result is formed. Based on the method, the problem that the reliability of traffic violation event recognition is relatively low in the prior art can be improved.

Disclosed are systems and methods for detecting traffic violations using one or more mobile detection devices. Videos captured by one or more mobile detection devices can be processed on the mobile detection devices to extract data and information concerning a potential traffic violation involving a vehicle and a restricted road area. The mobile detection devices can transmit such data and information to a server configured to make a determination as to whether a traffic violation has occurred by comparing the data and information received from the mobile detection devices.

The invention relates to an automatic driving test scene extraction method based on roadside multimode perception, and belongs to the field of automatic driving test. The method comprises the following steps: firstly, acquiring an image frame, millimeter wave radar data, laser radar data and local map data of a target roadside area; inputting the continuous image frames into a YOLOv8s model of a double-branch detection head, and synchronously identifying traffic participant information and traffic accident event information; matching and fusing the multi-sensor data in combination with local map data to generate traffic participant track point data; performing tracking management on the trajectory point data through a Kalman filtering algorithm to obtain initial trajectory information; through multi-dimensional abnormal point detection and layered trajectory reconstruction, final trajectory information is generated; based on the final trajectory, traffic violation detection and GGNN network driven high-interaction risk identification are completed; and finally extracting tracks before and after the event to form an automatic driving test scene. According to the method, the problems of poor multi-source data fusion, detection separation and the like in the prior art are solved, scene integrity and accuracy are improved, and high-order automatic driving test requirements can be met.

The invention discloses a traffic violation detection method, device and equipment based on a UV-KGNN network and a medium, and relates to the field of intelligent traffic, and the method comprises the steps: collecting multi-mode traffic related data based on a plurality of sensors carried by an unmanned aerial vehicle, and determining target physical quantities related to the operation states of the unmanned aerial vehicle and a vehicle; target detection and feature extraction are carried out based on a preset initial network and the target physical quantity, a UV-KGNN network is constructed by using a corresponding vehicle detection frame and the depth visual features, and uncertainty trajectory prediction of the vehicle is carried out by using the UV-KGNN network; based on a trajectory prediction result, a preset classifier classifies and judges potential traffic violation behaviors; and based on the magnitude of the target confidence corresponding to each target potential traffic violation behavior in the behavior determination result, analyzing the processing strategy to determine a target traffic violation detection result. According to the invention, traffic violation detection with high precision, physical consistency, uncertainty quantification and strong environmental adaptability is realized.

The invention discloses a traffic illegal behavior identification method and device. The method comprises the following steps: acquiring a plurality of to-be-recognized images; performing target detection on the plurality of to-be-recognized images to obtain a detection result; adopting the target object and the plurality of traffic elements to fill the preset text cue word to obtain a cue instruction set; and processing the prompt instruction set and the plurality of to-be-identified images by adopting the target generative model to obtain an illegal judgment text. According to the method and the device, the technical problem that the false alarm rate is high due to the fact that a recognition method for illegal behaviors such as red light running depends on single visual detection or a rule engine and lacks semantic understanding and multi-mode cooperative reasoning ability for traffic elements in the prior art is solved.

The application provides a traffic violation snapshot control method and device, electronic equipment and a storage medium, and belongs to the technical field of traffic violation snapshot. The method obtains historical violation data and normal traffic data in multiple types of traffic scenes, and forms a standardized scene data set through format specification and interference filtering processing. Then, the violation features and traffic features are converted to a preset feature space through a feature extraction conversion module to generate a scene feature vector. A violation trigger judgment model is constructed based on the scene feature vector, the difference features of different scene feature vectors are matched, and a cross-scene unified feature identifier is formed. A violation probability prediction model is further trained, and the cross-scene unified feature identifier and the real-time data of the target scene are used to predict the violation probability. Finally, the scene-level violation recognition threshold is calculated according to the probability distribution of the historical violation data of the target scene, and the model parameters are updated regularly. The method realizes accurate identification and adaptive snapshot control of violation behaviors in multiple scenes.

The application discloses a kind of man-machine collaborative traffic violation intelligent detection identification method, system.The method includes: through vehicle terminal continuous caching with space-time information video stream;Through voice recognition, driver instruction is received and event reference point T0 is marked;Based on T0, extract the historical video segment and metadata of preset time length and upload to cloud end by backtracking;Cloud end carries out analysis to video segment by multi-task deep neural network model, executes vehicle tracking, lane line and sign identification based on vision, violation behavior determination and license plate recognition, generates structured report;Report is returned to vehicle terminal, after driver confirmation or correction, in local, generate and store with legal force violation event archives with digital signature and trusted time stamp in combination.This application realizes the efficient cooperation of active trigger of driver and intelligent analysis of machine, evidence solidification, improves the convenience, accuracy and evidence credibility of traffic violation evidence.

The invention discloses a traffic violation judgment method and device based on a distributed system, and relates to the technical field of intelligent traffic management and information technology fusion, and the method comprises the steps: collecting multi-source heterogeneous traffic image and video data of an external camera, a vehicle-mounted camera and a mobile terminal; time-space synchronization is completed through GPS timestamp calibration and a space coordinate alignment algorithm; image enhancement, feature extraction and quality evaluation preprocessing are executed through an edge calculation module, and a standardized data stream is generated and transmitted to a distributed processing cluster; analyzing violation behaviors by using an intelligent identification model dynamically updated in the cluster, and dividing results into violation, fuzziness and no violation according to a preset threshold value; and carrying out multi-level auditing of secondary verification of an external system, interactive confirmation of a driver and manual rechecking of an auditor on the violation information to form a final judgment result and trigger a corresponding processing notification. According to the invention, the accuracy, the response speed and the system expansibility of traffic violation judgment are obviously improved.

The present invention discloses a method for identifying urban traffic risk hotspots, which belongs to the field of traffic management. The method includes the following steps: S1: obtaining a three-dimensional model of a target city, vehicle trajectory data, and weather data to build a city simulator; S2: identifying traffic violations based on target detection and relationship detection on the basis of the city simulator built in step S1; S3: extracting parking hotspots based on the city simulator built in step S1, and modeling environmental features to obtain traffic accident hotspots; S4: calculating traffic risk hotspots based on a weighted clustering algorithm for the traffic violations identified in step S2 and the traffic accident hotspots obtained in step S3. The city simulator is used to comprehensively simulate the traffic conditions in the city, and methods such as deep learning are used to timely and accurately discover urban traffic risk hotspot areas.

The invention relates to the technical field of car rental, and discloses a big data-based car rental credit information analysis and management method, which comprises the steps of 1, collecting operation data of online car-hailing drivers, including daily order quantity, revenue composition, platform punishment, customer service complaint and traffic violation records; acquiring asset registration information of the rental vehicle, wherein the asset registration information comprises vehicle unique identification information, an owner, a rental contract record, a mortgage or pledge record and insurance information; and step 2, on the basis of the acquired operation data and asset registration information, constructing an online car-hailing driver, a rental car and a rental contract. The technical scheme of dual-time-axis credit index extraction and dynamic credit curve calculation is adopted, the technical effect of tracking driver operation and asset dynamic changes in real time is achieved, continuous and accurate prediction of the performance ability of the online car-hailing driver is achieved, and the defects that in the prior art, static information is relied on, and the real repayment ability cannot be effectively predicted are overcome.

The invention provides a traffic law violation reminding method and system based on a multi-modal large model, and belongs to the technical field of automobile intelligent driving, the method comprises the following steps: obtaining multi-modal data of a current traffic scene, the multi-modal data of the current traffic scene comprising image data, audio data and text data; inputting the multi-modal data of the current traffic scene into a pre-trained multi-modal large model, and performing real-time reasoning and outputting a reasoning result by the pre-trained multi-modal large model; and generating traffic regulation violation reminding information according to the reasoning result. The comprehensive road scene understanding ability is constructed by fusing multi-source data such as vehicle-mounted vision, hearing, positioning and vehicle state. Compared with a traditional single-mode identification scheme, the traffic violation detection accuracy can be greatly improved in complex driving environments (such as severe weather and dense building groups), and the problems of view limitation and insufficient environmental adaptability of a single sensor are effectively solved.

The invention discloses a traffic violation education management method, computer equipment and a readable storage medium, and the method comprises the steps: inputting a monitoring image or video into a traffic violation detection model, so as to recognize traffic violation behavior data or a data set; performing face and license plate recognition processing on the violation image or the violation frame image to obtain a face recognition result and a license plate recognition result; whether the face recognition result is invalid face data or not and whether the license plate recognition result is invalid license plate data or not are judged, and if not, identity recognition calibration processing is carried out according to the face recognition result and / or the license plate recognition result, and illegal people are educated; and if yes, carrying out identity checking processing according to the license plate RFID information group and carrying out education processing on violation personnel. According to the invention, information of violation personnel can be determined by adopting a plurality of identity recognition modes and educated and processed, and the detection efficiency and accuracy are high, so that the manual patrol workload of traffic police is reduced.

This invention relates to the field of machine learning technology, and in particular to a method for identifying highway traffic violations based on an improved SlowFast network. The improved SlowFast network includes an input layer, a dual-branch feature extraction layer, a feature fusion layer, and a classification output layer. The dual-branch feature extraction layer includes a Slow branch and a Fast branch, and introduces an attention mechanism: the Slow branch uses ResNet-18 as the backbone network, introduces a CBAM spatial attention module after the third residual layer group of ResNet-18, and removes two residual blocks from the fourth residual layer group of ResNet-18; the Fast branch uses 3D MobileNetV2 as the backbone network, uses Depthwise Conv3D to replace standard convolution in MobileNetV2, and introduces an SE attention module after the output layer of the last 3D inverse residual stage of MobileNetV2. It balances lightweight, real-time performance, and high accuracy, perfectly meeting the needs of real-time behavior recognition scenarios deployed on edge devices.

The invention discloses a vehicle speed detection method, belongs to the technical field of expressway low-speed vehicle detection, and particularly relates to an expressway fast lane vehicle speed detection method, which comprises a first monitoring point and a second monitoring point, the speed of a vehicle is monitored through the first monitoring point and the second monitoring point, so that whether the vehicle has slow speed or violation behaviors is judged; the method can accurately recognize and judge the traffic violation behavior of long-time low-speed driving on the expressway, can warn and remind the driving behavior which does not touch the traffic violation behavior of low-speed driving but is lower than the lower limit of the speed of the expressway for a long time, and aims to correct the bad habit of a vehicle driver on the expressway and improve the driving safety. The highway traffic order is ensured, the road smoothness is improved, and the accident rate is reduced.

This invention discloses a personalized federated learning method for detecting traffic violations, comprising: training a basic feature extraction model using a violation training sample library; deploying a global feature extraction layer on the server; distributing the basic feature extraction layer to each client and generating a dynamic task description; training the basic feature extraction layer of each client using local data and performing knowledge distillation learning through a teacher model distributed by the server; extracting the mean features of local data to form a local prototype vector for each client, and uploading the local prototype vector and its global parameters of each client to the server; dynamically clustering the local prototype vectors and their global parameters of each client into several client groups to obtain cluster centers, clusterer parameters, and personalized models; and continuously optimizing the feature space distribution and model parameters of each client by distributing the updated cluster centers, clusterer parameters, and personalized models to the corresponding clients.

The application discloses a traffic flow directional guiding device, which comprises a walking base and a signal indicator light plate. The top of the walking base is fixedly connected with a square support post. Each of the four side walls around the square support post is slidably connected with a positioning vertical plate. The top of each of the four positioning vertical plates is fixedly connected with a signal indicator light plate. In the application, a positioning vertical plate is arranged on each of the four sides of the square support post. The top of each positioning vertical plate is provided with a signal indicator light plate. Then, a control combination is arranged to control the synchronous up-and-down movement of two opposite positioning vertical plates and the up-and-down movement of the other two opposite positioning vertical plates. When the traffic in one direction starts, the height of the corresponding signal indicator light plate in the direction can be controlled to be lifted, so that the driver can more intuitively observe the signal traffic state. Therefore, the passing rate can be improved and the traffic violation rate can be reduced.

The invention provides a traffic violation behavior identification method based on multi-source data fusion. The method comprises the following steps: acquiring multi-source data in a vehicle driving process through a plurality of sensors; fusing the multi-source data according to a preset rule, and outputting the fused multi-source data; and based on the fused multi-source data, identifying the traffic violation behavior of the vehicle. According to the invention, the data of the recorder, the traffic camera and the V2X communication equipment are fused, so that the problems of limited identification precision, especially high false alarm rate in a complex traffic environment and the like due to the fact that most of the prior art depends on a single data source are solved.

This invention provides a method and system for traffic violation identification based on fused data, relating to the field of computer technology. In this invention, firstly, traffic scene semantic vectors corresponding to traffic scene images and radar point cloud semantic vectors corresponding to radar point cloud data are extracted; secondly, the traffic scene semantic vectors and radar point cloud semantic vectors are cross-attention mapped to form a first attention vector, a second attention vector, and a third attention vector; then, based on image occlusion parameters and point cloud occlusion parameters, the first attention vector, the second attention vector, and the third attention vector are cross-attention fused to form a traffic scene fusion vector; finally, traffic violation identification is performed based on the traffic scene fusion vector to generate a traffic violation identification result. Based on the above method, the relatively low reliability of traffic violation identification in existing technologies can be improved.

The application provides a voice-based turning prompt method, system, medium, electronic device and vehicle, the distance between the current vehicle and the red traffic light intersection is obtained, when the distance between the current vehicle and the red traffic light intersection is less than a first distance threshold, the next step is executed; signal light recognition is performed to obtain the turnable direction of the signal light and the countdown time of the signal light; the lane where the current vehicle is located is obtained, and the current turnable direction is obtained according to the lane where the current vehicle is located; when the turnable direction of the signal light is the same as the current turnable direction, and the countdown time of the signal light is greater than a first time threshold, the next step is executed; when the turnable direction of the signal light, the current turnable direction and the direction of the vehicle turn signal are the same, a first voice prompt is generated, otherwise, a second voice prompt is generated; the application can help the driver to make a turning judgment and give a voice prompt, and traffic violations or traffic accidents that may be caused are avoided.

The application provides a traffic information prompting method and device, electronic equipment and a readable storage medium, which can be used in the field of traffic management. The method comprises the following steps: acquiring a monitoring image of a target vehicle shot by a first road monitoring device; sending a face image of a driver extracted from the monitoring image to an operator big data system for face recognition; receiving identity information and communication information of the driver returned by the operator big data system; determining a driving route of the target vehicle according to a driving direction of the target vehicle and a position of the first road monitoring device; determining historical traffic violation data of the driver on the driving route according to the identity information and the driving route; generating traffic prompting information according to the historical traffic violation data during the driving of the target vehicle, and sending the traffic prompting information to the driver based on the communication information. The method of the application effectively improves the risk warning ability of the driver and reduces the traffic accident rate through the pre-prompting mode.