158 results about "Traffic violation" patented technology

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 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.

Systems and methods for generating adversarial driving scenarios for autonomous vehicles. An artificial intelligence model can compute an adversarial loss function by minimizing the distance between predicted adversarial perturbed trajectories and corresponding generated neighbor future trajectories from input data. A traffic violation loss function can be computed based on observed adversarial agents adhering to driving rules from the input data. A comfort loss function can be computed based on the predicted driving characteristics of adversarial vehicles relevant to comfort of hypothetical passengers from the input data. A planner module can be trained for autonomous vehicles based on a combined loss function of the adversarial loss function, the traffic violation loss function and the comfort loss function to generate adversarial driving scenarios. An autonomous vehicle can be controlled based on trajectories generated in the adversarial driving scenarios.

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.

Disclosed herein are systems and apparatus for detecting a traffic violation. In one embodiment, a system for detecting a traffic violation comprises a context camera assembly and a license plate recognition (LPR) camera assembly. The context camera assembly can comprise a context camera housing containing a context camera, a context camera mount configured to mount the context camera housing to an interior of a carrier vehicle, and a context camera skirt coupled to and protruding outwardly from the context camera housing. The license LPR camera assembly can comprise an LPR camera housing containing one or more LPR cameras, an LPR camera mount configured to mount the LPR camera housing to the interior of the carrier vehicle, and one or more LPR camera skirts coupled to and protruding outwardly from the LPR camera housing.

The invention provides a dynamic driving risk assessment method and system based on DNC and NGBoost-PA-WOE, and the system comprises a database which is used for storing an urban road traffic map, historical traffic accident data and historical traffic violation data, and can automatically update the data; the data acquisition module is used for acquiring vehicle dynamic data and environment characteristics; the data preprocessing and cleaning module is used for preprocessing and cleaning the data; the degree centrality factor (DNC) analysis module is used for calculating a DNC value of a target road node; the evidence weight module is used for calculating WOE values corresponding to the accident rate, the violation rate, the vehicle dynamic data and the environment characteristics of the nodes; and the risk assessment module is used for predicting the risk score of the road node according to the DNC value, the WOE value, the accident rate and the violation rate. According to the method, the NGBoost-PA-WOE model is adopted to predict the risk score of the road node, the model combines the NGBoost algorithm, the FPA and the WOE, and the prediction precision of the risk score can be effectively improved.

Systems and methods for generating adversarial driving scenarios for autonomous vehicles. An artificial intelligence model can compute (110) an adversarial loss function by minimizing the distance between predicted adversarial perturbed trajectories and corresponding generated neighbor future trajectories from input data. A traffic violation loss function can be computed (120) based on observed adversarial agents adhering to driving rules from the input data. A comfort loss function can be computed (130) based on the predicted driving characteristics of adversarial vehicles relevant to comfort of hypothetical passengers from the input data. A planner module can be trained (140) for autonomous vehicles based on a combined loss function of the adversarial loss function, the traffic violation loss function and the comfort loss function to generate adversarial driving scenarios. An autonomous vehicle can be controlled (150) based on trajectories generated in the adversarial driving scenarios.

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 the technical field of traffic management, and provides a traffic safety auxiliary management method, the method is applied to persuasion robots, at least one persuasion robot is arranged on each supervision road section, traffic information is obtained by obtaining traffic conditions on the supervision road sections, and the traffic information is confirmed to be violation information or accident information; according to the violation information or the accident information, the persuasion robot executes different working modes to perform persuasion warning work; the system can be used in traffic accidents or other emergencies, meets the traffic demands under different conditions, can provide corresponding assistance and persuasion work under various conditions, and reduces the conditions of traffic jam and traffic violation.

The traffic violation behavior identification method provided by the invention comprises the following steps: for at least one to-be-processed image, performing instruction identification on a processing demand by using a pre-trained instruction identification model, and determining at least one instruction corresponding to the processing demand; aiming at each instruction in the at least one instruction, extracting a first type label and a second type label related to the instruction from the at least one to-be-processed image; the first class label is a label used for representing attribute information of a target in the at least one to-be-processed image; the second type of labels are labels used for representing traffic behaviors presented by at least one to-be-processed image; the target is related to a target traffic violation behavior indicated by the instruction; searching a target label combination matched with the target traffic violation behavior from a preset label combination library; and according to a to-be-processed label combination formed by the first type of labels and the second type of labels and the target label combination, determining whether the at least one to-be-processed image has a target traffic violation behavior.

The invention relates to an intelligent acquisition, analysis, prediction and automatic violation correction method and system based on public traffic violation big data, and belongs to the field of traffic informatization and intelligent management. The objective of the invention is to identify complex and diverse traffic illegal behaviors and analyze massive illegal data. The system comprises data acquisition, processing and storage, analysis and prediction, automatic violation correction and data security. Through multi-source data access, edge calculation and distributed storage, real-time acquisition and efficient processing of illegal behaviors are realized; deep analysis is carried out on the traffic violation data by using deep learning and big data technologies; and through real-time video stream analysis, tracking, recording and evidence obtaining are performed on suspected illegal behaviors. The system adopts a multi-level security protection mechanism, data privacy and security are ensured, and flexible expansion and multi-system collaboration are realized through modular design. Compared with a traditional system, the system has remarkable advantages in the aspects of data acquisition comprehensiveness, processing efficiency, prediction accuracy, violation correction automation, data security and the like.

The present invention discloses a vehicle speed monitoring and prompting method in a vehicle networking scenario, which relates to the technical field of vehicle speed monitoring, and comprises the following steps: integrating multi-dimensional data collected by sensors or cameras on traffic roads through multi-source data fusion technology to form a comprehensive traffic status view; uploading the integrated multi-dimensional data to a cloud storage system; using a machine learning algorithm in artificial intelligence to conduct an in-depth analysis of traffic flow and vehicle speed; using virtual simulation technology to establish a dynamic simulation model of urban traffic; generating real-time traffic information based on multi-source data fusion, artificial intelligence and dynamic simulation models, and a traffic management center performing real-time signal control based on the real-time traffic information; designing a smart contract for traffic violation detection based on real-time traffic information and blockchain technology; the present invention realizes efficient monitoring of vehicle speed and timely prompting.

The invention discloses a safety early warning signal sending control method applied to intelligent monitoring equipment, and belongs to the technical field of artificial intelligence. According to the invention, when a traffic accident occurs, monitoring data, meteorological data and geographic data of a place where the traffic accident occurs are acquired to form traffic accident data; extracting visual features, weather features and road environment features; identifying whether the vehicle violates a rule specified by the traffic sign, judging whether the driving track of the vehicle accords with a lane rule, and forming a traffic violation risk index; combining weather characteristics and road environment characteristics to form an environment interference intensity index; checking the damage condition of traffic signs around the accident site and the shielding condition of buildings to pedestrians to obtain a road facility association index; training the multi-layer perceptron model, inputting index data, and outputting actual proportions of human factors and natural factors of traffic accidents; and when the proportion of the natural factors exceeds a threshold value, providing a reconstruction suggestion of a traffic accident occurrence place.

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 relates to a hierarchical scanning, automatic identification and processing method based on traffic violation snapshot pictures, and belongs to the field of image processing and traffic management. The technical method aims at improving the scanning efficiency, accurately recognizing traffic violation information and achieving automatic processing and uploading so as to improve the processing efficiency and accuracy of traffic violation snapshot data. Comprising the steps of hierarchical scanning, automatic identification, data verification and uploading, and exception handling and feedback. According to the method, the stored snapshot pictures are quickly positioned and extracted by presetting the folder hierarchy depth and being compatible with various folder hierarchy structures, and each picture is marked, so that repeated scanning and missing detection are avoided, and the scanning efficiency is remarkably improved. The OCR technology is combined with the deep learning algorithm, so that the recognition accuracy is remarkably improved, and particularly, error recognition can be effectively reduced in a complex environment. After identification is completed, the system verifies the accuracy of an identification result through an automatic verification mechanism, seamless connection and processing of data are completed, manual intervention is reduced, and law enforcement efficiency is improved.

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 invention discloses a traffic flow directional dredging device which comprises a walking base and signal indicator lamp panels, a square supporting pile is fixedly connected to the top of the walking base, positioning vertical plates are connected to the four side walls of the periphery of the square supporting pile in an up-down sliding mode, and the signal indicator lamp panels are fixedly connected to the tops of the four positioning vertical plates. According to the invention, the four surfaces of the square pillar are respectively provided with a positioning vertical plate which slides up and down, the top of each positioning vertical plate is provided with a signal indicating lamp plate, and then a control combination for controlling two opposite positioning vertical plates to move up and down synchronously and controlling the other two opposite positioning vertical plates to move up and down is arranged. Therefore, the lifting height of the signal indicating lamp panel corresponding to the direction can be conveniently controlled when passing in one direction of a road begins, and a vehicle driver can observe the signal passing state more visually. Therefore, the passing rate can be improved, and the traffic violation occurrence rate can be reduced.

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.