66 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 road traffic, and particularly relates to a road traffic accident intelligent reproduction method based on automatic Pc-Crash simulation and an MLP algorithm. Comprising the following steps: 1, establishing a simulation model template in Pc-Crash simulation software according to road traffic accident scene information; 2, a simulation test is designed for unconfirmed parameters on the road accident site, and a simulation data set is obtained; 3, constructing, optimizing and training a multi-layer perceptron neural network model; 4, taking the stopping position of an accident participant of the road traffic accident or the tire friction trace deviation minimization as a core, constructing a target function, predicting an accident result by adopting an MLP neural network model, and obtaining an optimal solution of an unconfirmed parameter by using a multi-starting-point search global optimization algorithm; a simulation result is compared with actual exploration data, and the accuracy of the optimal solution is verified; intelligent reproduction of the whole process of a road traffic accident is realized, and important technical support is provided for responsibility determination and compensation processing during traffic accident processing.

The invention discloses a data analysis method for road traffic accident cause risk coupling, which comprises the following steps: collecting road traffic accident data, checking variable predictive force, constructing a road traffic accident severity cause analysis model by adopting a cumulative logistic model to mine risk factors, and constructing a coupling degree model to carry out risk coupling calculation. And the coupling strength among the risk factors is analyzed, the risk coupling effect of multiple factors is quantified, and risk decoupling suggestions are provided for road traffic safety. According to the method, key risk factors of different types of road traffic accidents can be effectively mined, the coupling effect of different forms of risk factors is quantified, and a decision basis is provided for a traffic management department to make accident prevention countermeasures.

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 relates to the technical field of auxiliary processing of road traffic accidents, in particular to a three-dimensional reconstruction system and method for road traffic accidents, and the system comprises a data collection module, a scene modeling module and an accident reconstruction module. The data acquisition module is used for acquiring environment data and evidence data of a target road traffic accident scene; the scene modeling module is used for performing three-dimensional scene modeling based on the environmental data to obtain a three-dimensional scene model of the target road traffic accident scene; and the accident reconstruction module is used for performing accident process reconstruction based on the evidence data and the three-dimensional scene model, and obtaining traffic participant operation data and three-dimensional animation of the target road traffic accident scene. According to the invention, rapid and accurate data acquisition can be realized, an accident scene can be constructed and reconstructed, and an accident process can be scientifically and visually restored and displayed.

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 embodiment of the invention discloses a method and device for predicting the disability level of passengers in a road traffic accident. The method comprises the following steps: acquiring multiple groups of historical collision records, constructing a historical data set based on each group of historical collision records, then collecting real-time collision data of a target passenger, and determining predicted disability data corresponding to the real-time collision data based on a passenger collision prediction model constructed by the historical data set; and finally, determining a target predicted disability grade of the target passenger based on the predicted disability data. Through the passenger collision prediction model, the first-time disability grade prediction of the passenger in the road traffic accident is realized, the long evaluation period is shortened, the accident situation in which the disability situation is urgently known is satisfied, and the processing efficiency of the collision accident is further improved.

The present invention discloses a method and system for identifying road traffic accident blackspot areas, relating to the field of data processing technology. The method comprises: collecting all historical road traffic accident data; performing first-order division of the target traffic area based on the nine-square correlation positioning method, and outputting a first-order spatial grid; encoding and mapping the full historical road traffic accident data according to the first-order spatial grid, and generating a road traffic accident coding mapping library; constructing an accident blackspot determiner, which calls the road traffic accident coding mapping library to identify the number of accidents and outputs a blackspot grid; performing a nine-square grid correlation merging analysis using the blackspot grid as the central grid, outputting a merged blackspot grid, and marking and visually displaying the merged blackspot grid. The method solves the technical problems of inaccurate and low efficiency in identifying traffic accident blackspot areas in the prior art, and achieves the technical effect of improving the accuracy and efficiency of identifying traffic accident blackspot areas.

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.

Various systems and methods are presented regarding utilizing technology onboard a vehicle to minimize road traffic accidents between cyclists and vehicles. A vehicle can be operating in any of an autonomous, partially autonomous, or non-autonomous manner. By utilizing onboard technology / artificial intelligence, the vehicle can detect a cyclist navigating a street proximate to the vehicle, and further determine, if a door of the vehicle was opened, whether a car dooring incident would result involving the cyclist. In response to a determination that a car dooring incident is likely, an onboard system can prevent an occupant from opening a door likely to cause the dooring incident. Accordingly, the vehicle can preemptively adjust available operations to prevent a car dooring incident.

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 present invention provides a method and a system for regulating traffic accidents, including: obtaining accident information and an estimated duration of a traffic accident, as well as traffic flow information within a preset road range at the accident point of the traffic accident; determining an accident scenario category of the traffic accident according to the accident information; the accident scenario category includes general road traffic accidents, traffic accidents near ramps, tunnel traffic accidents, and hub traffic accidents; determining an influence range of the traffic accident according to the traffic flow information; determining a regulation level of the traffic accident according to the estimated duration and the influence range; regulating prompt information on a highway prompt device according to the accident scenario category and the regulation level, so as to divert traffic flow through the prompt information. In this way, the method of automatically adjusting the regulation level of a traffic accident through accident information reduces the traffic flow affected by the accident, and further reduces the probability of secondary accidents.

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 present invention provides a warning method, device, equipment and readable storage medium for vehicle fatigue driving, which relates to the technical field of automotive safety warning. The method includes determining the facial image information of the driver based on the first video information; acquiring the lane line image information and the steering wheel rotation angle information; determining the risk coefficient of the moving vehicle based on the facial image information of the driver, the lane line image information and the steering wheel rotation angle information; and generating a warning signal when it is determined that the risk coefficient reaches a first preset threshold. The beneficial effect of the present invention is to judge the safety coefficient according to the facial image information of the driver, the lane line image information and the steering wheel rotation angle information, so as to give a speed warning to the driver, effectively avoid the occurrence of speeding behavior caused by driver fatigue or inattention, thereby improving the safety level and driving experience of vehicle driving, avoiding or reducing the occurrence of road traffic accidents, and reducing the accident rate.