86 results about "Traffic violation" patented technology

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 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 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.

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 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 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.

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 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.

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.

The application provides a kind of dynamic driving risk assessment method and system based on DNC and NGBoost-PA-WOE, comprising: database, for storing city road traffic map, historical traffic accident data and historical traffic violation data, and can automatically update data;Data acquisition module is used to collect vehicle dynamic data and environmental characteristics;Data preprocessing and cleaning module is used to preprocess and clean data;Degree centrality factor (DNC) analysis module is used to calculate the DNC value of target road node;Evidence weight module is used to calculate the WOE value corresponding to the accident rate, violation rate, vehicle dynamic data and environmental characteristics of node;Risk assessment module is used to predict the risk score of road node according to DNC value, WOE value, accident rate and violation rate.The application adopts NGBoost-PA-WOE model to predict the risk score of road node, which combines NGBoost algorithm, penalty attribute (FPA) and weight evidence (WOE), and can effectively improve the prediction accuracy of risk score.

The present application relates to the technical field of behavior recognition, in particular to a large-scale incomplete multi-view, multi-modal scene image library construction and recognition method, comprising the following steps: deploying multiple sensors and cameras in a traffic scene, and unifying multi-modal data to the same space-time coordinate system; fusing the multi-modal data after space-time alignment, predicting the target state and updating the measurement value; based on the fused data, using the adversarial generative network under space-time constraints, dynamically generating multi-view candidate views, combining the target kinematic model to complete the missing views; fusing the target kinematic model and deep learning technology to track the cross-camera target, constructing the traffic regulation ontology library, and combining the image recognition technology to automatically identify and classify the traffic violation behavior. The present application ensures the consistency of different sensor data in time and space dimensions, avoiding data confusion and errors caused by space-time differences.

A traffic violation monitoring method and apparatus, a medium, a program product, and a vehicle. The traffic violation monitoring method comprises: determining, on the basis of scenario traffic regulation information and current driving information of a vehicle, whether the vehicle has violated a traffic regulation, wherein the scenario traffic regulation information is traffic regulation information corresponding to a traffic scenario where the vehicle is currently located.

This application relates to the field of intelligent transportation technology and discloses an intelligent identification and evidence collection system for traffic violations based on multimodal fusion perception. The system includes an edge sensing terminal, which uses a synchronous control module to achieve hardware synchronous acquisition of multimodal sensors. A data processing module performs spatiotemporal alignment of the acquired data and performs cross-modal correlation and fusion tracking of the target to generate a high-precision fused motion trajectory. Finally, the trajectory is compared with a digital road traffic rule model to determine the violation. This invention solves the technical problems of low accuracy and susceptibility to false alarms and missed detections caused by single sensors in complex scenarios such as adverse weather and target occlusion. It can generate highly credible multi-dimensional structured evidence, significantly improving the automation level and credibility of traffic law enforcement.

This system provides a vehicle driving safety comparison system that allows for a compelling comparison of the driving safety of elderly drivers and others. [Solution] In the vehicle driving safety comparison system 1, the first determination unit 4 determines whether there is a violation of traffic rules on a registered road X based on driving information, road location information, and traffic rules, and the calculation unit 5 calculates the first violation frequency of traffic rule violations determined on a single registered road X during a predetermined period for multiple sample vehicles Z driven by drivers within a predetermined age range, and the second violation frequency of traffic rule violations determined on the same registered road during the same predetermined period for target vehicles Y driven by drivers older than the predetermined age range. A vehicle driving safety comparison system characterized by having the following features.

The invention discloses a traffic violation detection and insurance acceptance automation method, and relates to the technical field of intelligent traffic systems, and the method comprises the steps: synchronously collecting multi-source video stream data in a vehicle driving process through a vehicle-mounted camera and a traffic camera, the time-space consistency alignment of the video streams is realized based on the timestamp and the space coordinate mapping; transmitting the aligned multi-source video stream to a cloud server, and carrying out preprocessing, target detection and behavior analysis on the video stream by using a multi-modal analysis model based on deep learning to judge whether a traffic violation behavior exists or not; generating a violation record containing violation time, place, type and vehicle information according to the violation judgment result, and uploading the violation record to a cloud server for storage through a data compression and encryption technology; and based on the violation record, an insurance acceptance process of an insurance company system is automatically triggered through a standardized API interface, insurance auditing and compensation processing are completed, and a processing result is fed back to a user side and a traffic management department.

The invention relates to the technical field of intelligent traffic monitoring, and discloses a traffic video monitoring image intelligent analysis method based on deep learning. Comprising the following steps: constructing a double-flow heterogeneous graph network by using traffic video streams and geographic information data, and generating multi-dimensional context information containing a driving style and a group mode; calculating the structural tension of the real track in the network, and generating an anti-factual reasoning trigger instruction when the condition is met; in response to the instruction, instantiating the virtual obstacle and generating a personalized anti-fact trajectory; and obtaining a driving intention judgment result based on the trajectory similarity and the multi-dimensional context information, and updating a static rule. According to the method, the double-flow heterogeneous graph network is constructed, the personalized anti-fact trajectory is generated when the structural tension meets the condition, the correlation degree of the deviation behavior and the assumed risk avoiding behavior is quantified according to the trajectory similarity, and the accuracy of traffic behavior analysis is improved on the basis of distinguishing substantive traffic violation and compliance hidden risk avoidance.

The invention provides an off-line road violation photographing early warning method and system, electronic equipment and a readable medium, and belongs to the field of intelligent driving. Alarm data containing longitude and latitude are pre-stored locally; acquiring longitude and latitude information of the current position; converting the acquired longitude and latitude into hash values; locally matching a hash matrix corresponding to the alarm data according to the hash value; according to the method, an algorithm for retrieving a large amount of data is optimized, the alarm data are processed in advance, the alarm data are converted into two-dimensional data through the Hash algorithm to be stored locally, the index address of each data block is stored, and therefore the data retrieval efficiency is improved. In the data retrieval process, whether the current data block meets the requirement or not can be judged only by retrieving the index value of each data block, and compared with a common binary search method, the efficiency is greatly improved.

The application belongs to the technical field of intelligent traffic, vehicle-road cooperation and traffic monitoring, and discloses a monitoring and cooperative management system based on intelligent identification of traffic violations, which comprises a multi-modal heterogeneous perception layer, an edge and cloud cooperative transmission layer, a dynamic game decision layer, a cross-domain cooperative disposal layer connected in sequence in communication, and an intelligent traceability and optimization layer in bidirectional communication with the dynamic game decision layer. By constructing the four-modal heterogeneous perception layer of space base, road base, vehicle-mounted and human base, multi-dimensional data cross verification is realized, solving the problem of single or binary perception blind area and high misjudgment rate in the prior art. By designing a dynamic game decision algorithm linked in real time with multi-modal fusion data, introducing a dynamic weight adjustment, scene correction and illegal hazard level coefficient calibration mechanism, the illegal level can be accurately distinguished, and different traffic scenes can be adapted, solving the problem that the existing algorithm cannot adapt to complex scenes and has low accuracy.

The invention relates to a method performed by means of a processor unit (20) in a monitoring system (10) comprising said processor unit (20) enabling the detection of status of traffic violations according to at least one traffic data received from a plurality of data collection units (50) disposed on highways that enable the collection of at least one traffic data related to said highways.

The invention provides an intelligent traffic violation warning method and apparatus, and an electronic device. The method comprises the steps of obtaining a target image; generating a prompt word corresponding to the target image based on a feature extraction rule base; inputting the target image and the cue word into a text generation model for violation cue text generation processing to obtain a target cue text; and based on the target prompt text, sending prompt information to the traffic participant to prompt the traffic participant to stop the traffic violation behavior. According to the embodiment of the invention, the traffic participant can be warned based on the target prompt text, and the effects of accurate prompt and accurate correction are achieved.

The present disclosure provides an AI-powered traffic control system (102) mounted on a vehicle (301) for autonomous monitoring and management of traffic violations and conditions. The system includes at least one AI-powered camera (201) to capture images or video, detect traffic violations, conditions, and unsafe driving behaviours using image recognition and machine learning algorithms; a speed detection device (202) to measure vehicle speed; and an adaptive light system (203) to deliver real-time visual feedback to surrounding drivers. A processor (501) analyses data from the camera (201) and speed detection device (202), controlling the adaptive light system (203) and communicating with external systems. Memory (502) stores data for reporting and post-incident analysis. A method for implementing the system includes initializing components, capturing data, analysing violations, providing feedback, and ensuring privacy protection. This system (102) enables decentralized traffic management, enhancing road safety and regulatory compliance in varied traffic environments.

The invention discloses a method, device and equipment for identifying traffic accident behaviors and a computer readable medium, and relates to the technical field of computers. A specific embodiment of the method comprises the following steps: inputting a traffic accident image into a target detection model, and identifying traffic prohibition signs and the number of the traffic prohibition signs; intercepting a sub-graph of a traffic prohibition sign in the traffic accident image, identifying a traffic prohibition corresponding to the traffic prohibition sign, analyzing a traffic accident text corresponding to the traffic accident image according to the traffic prohibition, and determining a traffic accident behavior; and screening out a traffic accident retrieval behavior from the traffic accident behavior and the traffic accident similar behavior similar to the traffic accident behavior, querying the knowledge base according to the traffic accident retrieval behavior, and obtaining and pushing a traffic illegal behavior corresponding to the traffic accident query behavior, a traffic law article corresponding to the traffic accident query behavior and a corresponding investigation operation corresponding to the traffic accident query behavior. According to the embodiment, the time consumed for identifying traffic accident behaviors can be shortened, and the identification accuracy is improved.