47 results about "Road traffic accident" patented technology

The invention discloses a road traffic accident cause analysis and responsibility judgment method and system, and relates to the technical field of traffic management data processing, and the method comprises the steps: carrying out the credible collection of multi-source data, and constructing an evidence chain; performing multi-source data preprocessing and cross-modal fusion optimization; performing multi-dimensional cause intelligent analysis; performing responsibility judgment based on a quantification rule; and the responsibility judgment result is subjected to multi-dimensional rechecking and rule iteration adaptation. According to the road traffic accident cause analysis and responsibility judgment method and system, through a full-process closed-loop design of data acquisition, preprocessing, cause analysis, responsibility judgment, re-checking iteration and report evidence storage, technologies such as multi-source perception and an AI algorithm are integrated; the method solves the problems of uncredible evidence chain, one-sided cause traceability, non-uniform judgment standard, low cooperation efficiency and the like in traditional accident processing, realizes intelligence, standardization, compliance and traceability of accident processing, remarkably improves the credibility, processing efficiency and judicial suitability of a judgment result, and provides core technical support for modernization of traffic control.

The invention discloses a road traffic accident restoration system and method suitable for an intelligent networked automobile, and relates to the technical field of data processing, and the system comprises a positioning module which is used for obtaining the current accident state of a road traffic accident and positioning an associated equipment set; the correction module is used for executing data acquisition of an associated vehicle and associated road end equipment and establishing an original sensing data stream; the segmentation module is used for executing accident semantic node slicing by utilizing a semantic time slice channel; the causal analysis module is used for outputting a causal attribution score with time resolution; and the authentication module is used for executing accident scene reconstruction under generation-discrimination confrontation and executing candidate restoration set confidence authentication. The technical problems that in the prior art, synchronous fusion of multi-source heterogeneous data is difficult, accident causal relationship implicit is difficult to mine, and the reliability of accident scene reconstruction is low are solved, and the technical effect of improving the time sequence accuracy, causal interpretability and result reliability of road traffic accident restoration is achieved.

The invention relates to the technical field of knowledge extraction, and discloses a multi-source road traffic accident knowledge extraction method and system, and the method comprises the steps: determining a joint feature vector based on an accident knowledge triple marking matrix, constructing a comprehensive function based on the joint feature vector, and distributing marks for the accident knowledge triple marking matrix through the comprehensive function, an accident knowledge triple optimization mark matrix is obtained, the accident knowledge triple optimization mark matrix is decoded, and an extraction result containing knowledge triads is obtained. The hidden error accumulation caused by semantic ambiguity in the model is solved, and knowledge extraction with higher robustness is realized. Complete and credible structured input is provided for constructing nodes according to types in a subsequent knowledge graph, and high-quality construction of the multi-source road traffic accident knowledge graph from an unstructured text to a semantic-definite and logic-consistent multi-source road traffic accident knowledge graph is truly realized.

The invention relates to the technical field of artificial intelligence and intelligent traffic, in particular to a traffic accident liability intelligent affirmation method and system based on a multi-agent cooperation mechanism and a large language model. The whole process of credible information verification, responsibility affirmation reasoning and standard document generation is simulated. Wherein the legal expert agent adopts a competing mechanism, and the fairness and the accuracy of affirmation are improved through preliminary affirmation, double-party defense and final judgment. The reasoning ability of a large language model and accurate knowledge retrieval of a retrieval enhancement generation technology are fused, the real-time performance and accuracy of legal clause quotation are ensured, and a road traffic accident identification document conforming to specifications is automatically generated. The method effectively solves the problems that traditional manual identification is low in efficiency and high in subjectivity, and an existing intelligent method lacks interpretability and legal accuracy.

The invention belongs to the technical field of model application, and particularly relates to a large-model-based intelligent wounded personnel sorting method for road traffic accident depth survey data and wound scoring, which comprises the following steps: firstly, building a multi-terminal and multi-dimensional data acquisition network, acquiring full-process multi-source data and finishing preprocessing; a multi-score fusion model is constructed according to a preset trauma scoring rule, a wounded intelligent sorting large model is embedded, a dynamic weight distribution model is called in combination with an accident scene to determine each score weight, and a score feature matrix is constructed according to a score input source, feature dimensions and weights; then, a cross-domain knowledge graph fusing hospital and traffic accident engineering knowledge and associated information is constructed, and a large sorting model is fused; and finally, through multi-modal feature extraction and fusion, a strategy optimization and incremental learning training model, a final wounded intelligent sorting large model is obtained. According to the method, the problems of low efficiency and low accuracy of wounded person evaluation of a wounded person sorting method in the prior art can be solved.

The invention discloses an unbalanced discrete data generation method fusing causal constraints and potential variables, and the method comprises the steps: firstly collecting environmental factors and accident severity of a road traffic accident occurrence point, recording the environmental factors and accident severity as road traffic accident data D, building a causal graph G of the road traffic accident data D, and obtaining an adjacent matrix A; secondly, performing encoder mapping calculation on the original data feature X through a variational auto-encoder VAE to obtain a potential variable z; taking the potential variable z and the category label Y as the input of a WGAN-GP network generator to obtain a virtual potential variable, and distinguishing the potential variable z and the virtual potential variable in combination with the authenticity score Di output by a discriminator to obtain a high-fidelity potential variable; and finally, taking the high-fidelity potential variable as the input of a VAE decoder, and generating few-class sample synthesis data in combination with causal constraints. According to the invention, the urban traffic accident data monitoring quality can be improved, and the road safety management and decision-making accuracy can be improved.

The invention relates to the technical field of knowledge extraction, and discloses a reasoning method and system for a multi-source road traffic accident knowledge graph, and the method comprises the steps: constructing an accident knowledge triple reconstruction feature vector based on an accident relation type final feature vector, an accident entity final feature vector and an attention weighted fusion path feature vector; and reconstructing a feature vector according to the accident knowledge triad, generating an accident knowledge real triad, and inserting the accident knowledge real triad into a knowledge graph. The case-level closed reasoning mechanism strictly follows the principle of independence of judicial facts, knowledge confusion caused by global knowledge mixing is effectively prevented, and semantic accuracy and logic consistency of the knowledge graph in multiple case scenes are remarkably improved.

The application discloses a heavy engineering vehicle driving performance evaluation method based on natural driving data, first, vehicle motion data under a natural driving environment is collected, and data preprocessing is carried out; then, corresponding statistical features of driving behaviors under different road types are extracted; secondly, driving performance evaluation is carried out based on driving behavior statistical indexes; finally, driving scores are calculated based on driving performance evaluation results, and finally, a driving performance evaluation system is obtained. The application can serve driving performance examination and safety education management of drivers, enhance road traffic safety consciousness of the drivers, and reduce road traffic accidents.

The utility model relates to an automatic telescopic anti-splashing device which comprises an anti-splashing device body, the anti-splashing device body is composed of a first sealing plate, a second sealing plate and an air cylinder, the middle position of the first sealing plate and the middle position of the second sealing plate are both bent outwards to form a containing cavity used for containing the air cylinder, an air cylinder seat is fixed to the end of the first sealing plate, and the air cylinder seat is fixed to the end of the second sealing plate. A cylinder is fixed to the cylinder seat through bolts, a sliding mechanism is arranged on at least one of the upper side and the lower side of the cylinder, a fixing plate is fixed to the side, close to a piston rod of the cylinder, of the second sealing plate, and a mounting through hole allowing the end of the piston rod to penetrate through is formed in the middle of the fixing plate and connected with the end of the piston rod through a connecting piece. A baffle is fixed under the second sealing plate. The anti-splashing device is simple and reasonable in structure and capable of effectively blocking splashing objects and preventing the splashing objects from hurting pedestrians and passing vehicles, so that the safety performance of the sweeping operation vehicle is improved, and road traffic accidents are reduced.

The present application relates to the field of traffic safety assessment, in particular to a VRU road traffic accident injury grade evaluation system and method, the system comprises a collection module, a processing module and a traffic accident injury module; the method comprises, according to the collision form and contact site of VRU road traffic accident, the accident is classified to obtain the collision type;When traffic accident occurs, the collision information of traffic accident is collected;The collected collision information is classified and stored according to the collision type;With the minimum number set of the collision type of different VRU road traffic accidents, the human body injury mechanics parameter and the actual human body injury disability degree injury grade establish corresponding data model, analyze the optimal solution of the relationship of the corresponding injury grade of collision information;The predicted injury grade is determined according to the optimal solution.The present application can obtain the predicted injury grade in the first time of traffic accident, more timely, after the collection of relevant information, the analysis and prediction of injury grade are automatically carried out, and the evaluation time of injury grade is short.

The invention relates to the technical field of traffic accident medical rescue intellectualization, and discloses a road traffic accident deep survey data-driven wounded person claim-first and treatment-later method, which comprises the following steps of: firstly, constructing an acquisition network covering the whole process of accident scene, first aid and hospital treatment, acquiring accident engineering, real-time medical treatment and deep medical treatment data, and cleaning and standardizing the accident engineering, real-time medical treatment and deep medical treatment data; then, a large fusion model is constructed based on the preprocessed data, image features and text features are extracted through 3D-ResNet50 and ClinicalBERT respectively, and multi-modal fusion is completed in combination with a gated cross attention mechanism; meanwhile, a three-layer full-connection dynamic weight calculation model is built, a scoring feature matrix is matched to be fused with a multi-wound scoring system, and an AdamW optimizer, a cosine annealing strategy and an early stop method are adopted to optimize model training; and finally, inputting the comprehensive feature vector into a model to output a injury condition level and confidence, and optimizing a sorting result in combination with a knowledge graph and an evaluation rule so as to provide intelligent technical support for traffic accident rescue.

The invention relates to the technical field of road traffic fire protection, and discloses an all-terrain traffic accident disposal practical training base, which comprises a simulation tunnel arranged on one side in the base and comprising an arched tunnel section and a rectangular tunnel section which are communicated with each other; the simulated road comprises an entrance intersection area, an entrance channel, a long road section and a bridge section; the system comprises a simulated light rail, a falling simulation area, a simulated underground garage and a simulated river. According to the invention, an all-terrain and multi-scene comprehensive road traffic accident handling base is designed for the actual needs of road traffic fire-fighting practical training, various traffic road terrains and traffic reality scenes are simulated and arranged in the base, the training scenes are comprehensive and sufficient, the spatial position layout is reasonable and compact, the training effect and efficiency can be improved, and the practical training effect and efficiency can be improved. On the premise of ensuring sufficient setting of the simulated terrain scene, the occupied ground space is reduced to the greatest extent, and the construction cost is saved.

This invention relates to the field of traffic accident investigation technology, and discloses a novel method and system for collecting road traffic accident investigation data, including a user terminal and a processing terminal. The user terminal includes integrated acquisition glasses, which are equipped with an image acquisition module for collecting image information of the accident scene, a lidar for scanning the accident scene and obtaining corresponding three-dimensional spatial information, a communication module, an information prompt module for importing accident scene acquisition standards and generating prompt information according to the standards, and a VR image display module for projecting the prompt information into the visible space. The processing terminal includes a data processing module, which processes the image information and three-dimensional spatial information transmitted by the integrated acquisition glasses, converts them into panoramic images, and transmits them back to the integrated acquisition glasses. This invention enables rapid accident data acquisition, processing, and real-time display, achieving high acquisition efficiency and completeness.

The invention relates to a dangerous driving remote control method and system based on vehicle-road cooperation and a medium, and the method comprises the steps: M1, when a driver drives a vehicle into a certain given area, a vehicle end actively reports speed and position information to roadside equipment, and obtains the speed data information and position data information of the vehicle in real time; and M2, based on the speed data information of the vehicle and the position data information of the vehicle, fusing the data of the vehicle by adopting a multi-modal fusion algorithm based on feature extraction to obtain data information of motion parameters of the fused vehicle. According to the method, dangerous driving behaviors of the vehicle can be rapidly inhibited, road traffic injury events can be reduced, road traffic accidents can be reduced, social hazard events can be reduced, and the method has good harmony and stabilization effects on the society and families.

The invention provides a big data analysis-based dynamic speed limit control system for a road section with high occurrence of road accidents, and belongs to the technical field of road control. According to the invention, the video camera is used for collecting the vehicle flow data, the vehicle flow data of the two sections of the road section are compared and analyzed, the emergency in the road section area can be judged at low cost, and the situation of the emergency can be quickly judged in time in combination with the tracking processing of the data acquisition module. The cloud computing control platform is overlapped with the ramp control system to dynamically adjust the speed limit in the road section area, and the vehicle flow entering the high-incidence road section is synchronously limited, so that the vehicle flow pressure of the high-incidence road section is relieved from the source, the secondary accident phenomenon is reduced in an auxiliary manner, the active defense for the road traffic accident is enhanced, and the safety of the road traffic accident is improved. The dynamic adjustment effect of the system in the using process is effectively improved, and the road safety is greatly improved.

The utility model discloses a road traffic accident rescue device which comprises a battery bin and a driving handle, the driving handle is fixedly installed in the center of the top face of the battery bin, an anti-slip strip is arranged on the outer surface of the driving handle, a driving rotating shaft is installed in the center of the top face of the driving handle, and a connecting square block is arranged at the top end of the driving rotating shaft. A connecting square groove is formed in the center of the connecting square block, and a combination hole and a positioning hole are formed in a frame of the connecting square block. According to the road traffic accident rescue device, handheld operation is performed on a driving handle according to an anti-slip strip, so that a first expansion insertion plate, a second expansion insertion plate, a third expansion insertion plate and a fourth expansion insertion plate are inserted into gaps, needing to be expanded, of a vehicle in a road traffic accident; the first expansion insertion plate, the second expansion insertion plate, the third expansion insertion plate and the fourth expansion insertion plate are different in length and width, and rotary expansion work is carried out through a driving motor in a driving handle in sequence, so that rescue auxiliary work is achieved.

This invention relates to the field of intelligent transportation technology, specifically to a method and system for tracking hit-and-run accidents based on unmanned vehicles and air-ground integration. The method includes: installing road traffic monitoring equipment at various road nodes to monitor road traffic conditions and the characteristics of fleeing vehicles in real time, and transmitting this information to an unmanned enforcement vehicle and a regional traffic data processing center; upon receiving the fleeing vehicle's characteristics, the unmanned enforcement vehicle triggers an automatic takeoff procedure for a drone; after takeoff, the drone locks onto the fleeing vehicle based on the provided characteristics and transmits real-time information about the fleeing vehicle to the regional traffic data processing center; the regional traffic data processing center receives all information and generates a real-time dynamic trajectory map of the fleeing vehicle, and formulates a deployment plan. This invention integrates information from monitoring equipment, unmanned enforcement vehicles, and drones to achieve the discovery, tracking, and deployment of vehicles fleeing road traffic accidents, improving the detection rate and processing efficiency of hit-and-run cases.

The invention belongs to the technical field of machine vision, and relates to an air-ground cooperative road congestion quick response method, device and equipment and a medium. The method comprises the steps of obtaining a road condition and a road image; according to the road condition of the road, the road is divided into blocks, and different block areas are obtained; acquiring actual coordinates of the unmanned aerial vehicle and actual coordinates of the different block areas, and establishing coordinate mapping of the road image and the block areas according to the road image to obtain image coordinates of the different block areas; according to the road image, in combination with the image coordinates of the different block areas, obtaining the vehicle density of each block area; when the vehicle density meets a preset condition, obtaining a central point of the traffic accident; and performing path planning according to the central point of the traffic accident and the road image to obtain a response path. According to the invention, rapid positioning and rapid processing of road traffic accidents can be conveniently realized through rapid detection of the unmanned aerial vehicle.

The invention provides a road traffic accident site unmanned aerial vehicle self-adaptive exploration method, and the method comprises the steps: calculating the accident site diameter D and the accident center position according to the accident site image data collected by an unmanned aerial vehicle, and further calculating the shooting height H of an unmanned aerial vehicle exploration image, and does not need to set a reference object on the site ground. The survey image acquisition height is obtained through dynamic calculation according to the actual site condition; when a traffic accident scene live-action aerial view record graph is drawn, a laser range finder carried by the unmanned aerial vehicle is utilized to obtain the accurate height from the camera to the ground, and the actual size of any target in the aerial view record graph is directly calculated by combining the focal length of the camera and the size parameter of the sensor without depending on a ground scale or carrying out additional camera calibration. The whole process is low in calculation amount, high in calculation efficiency and suitable for various scenes.

The application relates to the technical field of knowledge extraction, and discloses a multi-source road traffic accident knowledge extraction method and system, which comprises the following steps: determining a joint feature vector based on an accident knowledge triple tuple mark matrix, constructing a comprehensive function based on the joint feature vector, assigning marks to the accident knowledge triple tuple mark matrix by using the comprehensive function, obtaining an accident knowledge triple tuple optimization mark matrix, decoding the accident knowledge triple tuple optimization mark matrix, and obtaining an extraction result containing knowledge triple tuples. The application solves the problem of implicit error accumulation caused by semantic ambiguity in the model, realizes higher robustness of knowledge extraction, provides complete and reliable structured input for subsequent construction of nodes in a knowledge graph according to types, and truly realizes high-quality construction of a multi-source road traffic accident knowledge graph from unstructured text to semantic explicitness and logic consistency.

The application discloses a road traffic accident warning device laying system and method based on air-ground cooperation, which comprises a warning device laying vehicle, an unmanned carrying box and an interactive decision system, the interactive decision system is connected with the warning device laying vehicle and the unmanned carrying box, the unmanned carrying box comprises an unmanned aerial vehicle and a carrying box body which is hung below the unmanned aerial vehicle and located between landing gears of the unmanned aerial vehicle, and the warning device laying vehicle is located in the carrying box body, the system and the method can lay warning devices around an accident vehicle in time after a vehicle has an accident, so as to remind following vehicles and provide guarantee for safe road passing.

The invention relates to a traffic accident severity prediction method and a related device, and belongs to the technical field of traffic safety, and the traffic accident severity prediction method comprises the steps: obtaining a road traffic accident data set, and determining influence factors of the traffic accident severity based on the road traffic accident data set; determining an association rule of each influence factor and the accident severity by adopting an association rule mining algorithm; and optimizing hyper-parameters of a preset traffic accident severity prediction model by adopting a preset optimization algorithm, and predicting the road traffic accident severity by adopting the optimized traffic accident severity prediction model in combination with the association rule. According to the invention, the accuracy of traffic accident severity prediction can be improved.

The invention relates to the technical field of traffic safety, and discloses a high-altitude region road traffic accident influence assessment method. The evaluation method comprises the following steps: acquiring a current injury condition index and a current altitude stress index of a person in a current traffic accident and a current duration index of a rescue unit arriving at a traffic accident site; obtaining the hypoxia medical model after parameter updating; using the updated hypoxia medical model as a calculation framework, and determining a current hypoxia deterioration result index corresponding to the current injury condition index according to the current altitude stress index and the current duration index; and determining the severity of the current traffic accident according to the current hypoxia deterioration result index. By adopting the assessment method, the severity of the traffic accident can be accurately assessed. The invention further discloses a system for evaluating the influence of the highway traffic accidents in the high-altitude area.

The application discloses an information assisted driving method based on a vehicle-road cooperation technology, a car-following driving assistance module considers the problem that a traditional intelligent vehicle can only refer to driving information of a single vehicle in front in car-following driving, detects information of a plurality of vehicles in front through subscription of a publisher, and performs an assisted car-following behavior according to the information. A lane-changing decision information assistance system considers the problem that a traditional vehicle is prone to inducing traffic accidents due to a detection blind area, performs real-time detection of a roadbed traffic unit, combines vehicle collision time, a vehicle head time interval, road conditions and other information, and performs decision of a lane-changing behavior based on a fuzzy rule. When a vehicle is faulty or performs a lane-changing behavior, the roadbed traffic unit and other traffic participants can be informed through message queue telemetry transmission (MQTT). Through the road condition information assistance based on MQTT, the safety of the car-following and lane-changing behaviors of the intelligent vehicle is effectively improved, and the probability of road traffic accidents induced by the detection blind area of the intelligent vehicle is reduced.

The invention provides a traffic accident detection method based on deep learning, and the method comprises the following steps: S1, obtaining an accessed real-time video, and verifying whether a scene is normally shot, and does not have short-range and long-range stretching and angle deviation or not according to the scene id of the real-time video, so as to prevent a detection region under normal shooting from changing; s2, judging the vehicle parking information of the accessed real-time video, and detecting whether a static vehicle exists or not; s3, filtering out vehicles outside the detection area by loading a detection area configuration file; and S4, predicting whether a road traffic accident occurs through a region accident identification algorithm according to the vehicle stop time, the vehicle stop number and whether the frame regions of the adjacent vehicles are overlapped. According to the traffic accident detection method based on deep learning, vehicle collision accidents in two accident high-incidence scenes of urban crossroads and high-speed key road sections can be detected and analyzed, and an alarm is given in time.

The invention discloses a traffic accident risk management method, system and device and a storage medium. The method comprises the following steps: acquiring road traffic accident associated information source data, extracting road traffic accident associated features from the preprocessed source data, and classifying and storing the road traffic accident associated features; establishing a traffic flow prediction model and a traffic accident risk prediction model based on the association features, and performing training; performing operation by using the trained traffic flow prediction model to obtain the traffic flow of the first target road in the first target time period; inputting the road structure characteristic historical data of the first target road, the road environment characteristic data of the first target road in the first target time period and the traffic flow data into a trained traffic accident risk prediction model for operation to obtain a traffic accident risk index of the first target road in the first target time period; and carrying out visual display on the statistical data of the traffic accident risk index. According to the scheme, an integrated management tool can be provided for road traffic accident risk management.

The invention discloses an accident liability determination method fusing retrieval enhancement generation and a thinking chain, and the method comprises the steps: obtaining a traffic accident monitoring video, carrying out the key frame extraction and preprocessing of the traffic accident monitoring video, and obtaining a time sequence image feature sequence; constructing a traffic regulation knowledge base, generating a retrieval query based on the time sequence image feature sequence, and recalling a legal clause set related to an accident scene from the traffic regulation knowledge base; inputting the time sequence image feature sequence, the legal clause set and prompt information containing thinking chain prompt words into a multi-modal large language model, driving the model to perform multi-step responsibility reasoning, and finally generating a structured road traffic accident identification book; according to the method, dynamic elements and illegal behaviors in a complex accident scene are accurately identified, the problem of judgment deviation caused by the fact that multi-modal data cannot be understood in a traditional rule is fundamentally solved, and accurate responsibility determination of the complex accident scene is achieved.

This invention discloses a road hazard warning system and method based on vehicle-road-cloud integration. The method includes the following steps: S1: Acquire data information affecting vehicle driving safety and vehicle information through edge cloud and regional cloud respectively, and upload them to the central cloud; S2: Perform perception fusion on the uploaded information through the central cloud and generate events; S3: Generate warning instructions based on the events and send them to the connected vehicle. The connected vehicle provides warnings to the driver through the HMI interface and HUD interface. This invention can improve the accuracy of hazard warnings and system reliability, reduce R&D, maintenance, and traffic operation costs, reduce road traffic accidents, and improve traffic management.

The utility model discloses an unmanned aerial vehicle assisted road traffic accident exploration device, and belongs to the technical field of unmanned aerial vehicles, the unmanned aerial vehicle assisted road traffic accident exploration device comprises an unmanned aerial vehicle main body, the bottom of the unmanned aerial vehicle main body is provided with a camera mechanism, a mounting rod pushes a limiting block to slide in a limiting groove, and the mounting rod is inserted into a fixing hole. A servo motor drives a reciprocating lead screw to rotate in an open groove of the protective shell, and the reciprocating lead screw is in threaded connection with a moving block, so that the moving block is stressed to slide in the protective shell, the moving block drives a connecting block to move, and the camera is conveniently mounted; the camera is wiped through the cleaning wiper arranged on the side face of the connecting block, the camera can be conveniently mounted and dismounted, the camera can be protected, it is guaranteed that the camera stably investigates road traffic accidents in rainy and snowy severe weather, operation is easy and fast, and use is convenient.

This application discloses a method, system, electronic device, and storage medium for generating teaching case scripts for road traffic accidents. The method includes: acquiring road traffic accident investigation report text, parsing the text, and extracting structured accident element information; based on the structured accident element information, using a preset typicality assessment model to evaluate the teaching value of accident cases, obtaining a typicality score, and selecting cases with scores higher than a preset threshold; based on an accident causal chain theory model, classifying the causal elements in the accident element information into multi-level causal chains, conducting in-depth causal analysis, and generating a visualized accident causal map; extracting related driving knowledge points and skills to generate corresponding accident prevention measure texts; and automatically generating structured teaching case scripts using a natural language generation engine. This application helps students fully understand the mechanism of accident occurrence, establish risk awareness, and improve safe driving awareness.