211 results about "Vehicle accident" patented technology

The invention discloses an unmanned vehicle accident judgment and responsibility judgment system and method based on roadside perception, and the system comprises a roadside perception module which is used for obtaining target scene image data, physical environment information and point cloud data in real time, the accident identification and evaluation module is used for judging whether an accident exists in the target scene and classifying the accident type, and through multi-dimensional analysis of the accident, the problems that the accident judgment response is not timely and the data dimension is not comprehensive in the unmanned vehicle accident handling process are solved. Besides, the invention provides an accident escape system and an accident judgment system based on the accident judgment system, an escape strategy is formulated by combining a steering wheel control technology, and accident liability is judged, so that the problems that an unmanned driving strategy cannot reasonably avoid obstacles and accident liability processing is complex are solved, the claim settlement time is shortened, and the safety is improved. And a whole-process solution from accident perception, intelligent decision-making to responsibility judgment is provided for the unmanned vehicle.

The invention discloses an accident analysis and intelligent intervention method and system based on an intelligent networked automobile, and particularly relates to the technical field of accident analysis, which comprises the following steps: when the intelligent networked automobile approaches an accident-prone place, extracting important features from historical traffic data in the accident-prone place of the intelligent networked automobile by using a t-SNE dimension reduction method; acquiring real-time traffic data of the intelligent networked automobile, and determining similar information of the traffic data of the intelligent networked automobile; bayesian inference is carried out based on historical traffic data in an accident-prone place of the intelligent networked automobile and real-time traffic data of the intelligent networked automobile, and probability information of the traffic data of the intelligent networked automobile is determined by analyzing statistical data and constructing a machine learning mode; through comprehensive analysis of the similar information and the probability information of the traffic data, the risk that the intelligent networked automobile enters the accident-prone place is evaluated, the road traffic safety can be improved, and the accident occurrence rate is reduced.

The embodiment of the invention relates to the technical field of vehicles, and discloses a vehicle accident early warning method and device, electronic equipment and a storage medium, the method comprises the following steps: obtaining running state data in a vehicle based on a preset duration, the running state data being determined based on vehicle data obtained by at least one bus source in the vehicle; determining target time sequence characteristic data in the operation state data, wherein the target time sequence characteristic data indicates a change trend, a periodic rule and a sudden change condition of the operation state data in a time dimension and a dependency relationship among the data; and inputting the target time sequence characteristic data into a risk assessment model to obtain an assessment result of the accident risk of the vehicle after the preset duration. By applying the technical scheme of the invention, more accurate vehicle early warning can be realized, and the safety driving requirement of the vehicle can be better met.

The invention relates to the related technical field of vehicle damage analysis, in particular to a multi-source data-based vehicle damage correlation analysis method and system, and the method comprises the steps: obtaining vehicle accident multi-source data, building a multi-dimensional feature matrix, recognizing vehicle damage leading factors and an effective correlation chain, generating a vehicle damage influence degree sequence, and constructing a vehicle damage correlation analysis network. And generating a vehicle damage diagnosis report in combination with the vehicle damage influence degree sequence. The technical problems that the identification capability of hidden linkage damage is insufficient, linkage damage caused by secondary factors is easy to ignore, and a vehicle damage diagnosis report cannot provide comprehensive and accurate decision support for accident handling are solved, and the purposes of determining an association strength value by utilizing forward propagation, identifying vehicle damage leading factors, effective association chains and generating influence degree sorting are achieved. And the incidence relation between the influence factors and the vehicle damage types is accurately quantified, a vehicle damage correlation analysis network is constructed, and a diagnosis report is generated in combination with influence degree sorting, so that the technical effects of comprehensiveness, accuracy and reliability of vehicle damage diagnosis are effectively improved.

A lockbar in a tongue of a seat belt system in a vehicle, wherein the crash locking tongue includes the lockbar, a base plate, and a cover material overmolded onto the base plate. The base plate is configured to be reversibly coupled to a buckle in the seat belt system and to have a slot through which a belt webbing extends in order to restrain the movement of a passenger in the vehicle. The lockbar includes one or more fins located on a bottom surface near a front edge, a pair of guide wings located on a back edge adjacent to opposite sides, a clamping surface that is in contact with the belt webbing, and an elastomeric spring. The lockbar undergoes a vertical translation upon a vehicle accident that causes a clamping surface to apply an inward pressure on the belt webbing, thereby, reducing movement of the belt webbing.

This utility model is applicable to a device for preventing vehicle accidents when an external object enters the space under the pedal unit, more specifically the gap between the pedal lever and the floor while the vehicle is moving, thereby preventing fatal road accidents. The device comprises at least one cutting element created to pierce a large external object and is in the form of a shaped cover plate, which is a semicircle with an outer and inner edge, the outer edge of which is in the form of the said at least one cutting element, and the inner edge is made for mounting to the pedal lever of the vehicle. The device has a simple design, is effective for piercing / cutting an external object that has entered the space under the control pedal unit, is easy to install on the pedal lever and prevents accidents.

A system for recording event data of an autonomous vehicle is disclosed. The present disclosure suggests an EDR system operation scheme suitable for properly investigating the cause of a vehicle accident. Subsystems of an autonomous driving control system may determine whether an event satisfying a preconfigured condition has occurred, and send to an EDR system a trigger signal to trigger recording of data elements related to the event when the occurrence of the event is detected. The EDR system may record data elements that are useful to reconstruct a map of an external environment surrounding an autonomous vehicle at the time of the occurrence of the event or to reconstruct an internal environment of the vehicle.

The invention provides a test file generation method and apparatus, an electronic device and a medium. The test file generation method comprises the steps of obtaining an accident report of a vehicle; wherein the accident report is a process of describing a vehicle accident by using a natural language; determining structured description information based on a behavior tree based on the accident report; wherein the structured description information is used for describing the process of the automatic driving test scene by utilizing a plurality of nodes of a behavior tree; generating a test file based on the structured description information; wherein the test file is used for simulating an automatic driving test scene. According to the technical scheme, the accuracy and the performability of test file generation can be improved, and the generation efficiency of the test file is improved.

The invention belongs to the technical field of vehicle risk assessment, and discloses a vehicle accident risk prediction method and device, a storage medium and computer equipment. The method comprises the following steps: acquiring operation parameters of a plurality of vehicles, including load data, operation road type, gradient data, vehicle speed, driving behavior data, night driving time proportion, active safety configuration data, operation weather data, accident frequency and fault frequency; determining a plurality of risk assessment features; taking the risk assessment features as a training set, training a random forest model, and taking the random forest model as an accident risk prediction model when the performance of the random forest model reaches a preset requirement; and inputting the risk assessment features of the to-be-predicted vehicle into the accident risk prediction model, obtaining a survival function output by multiple trees of the accident risk prediction model, and determining the accident occurrence probability of the to-be-predicted vehicle within a preset duration according to the survival function. And the accident risk of the vehicle can be accurately predicted by using the multi-dimensional operation data of the vehicle.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes such as financial science and technology, and discloses an early warning method, device and equipment based on multi-source accident data and a medium. Training a similarity matching model based on the dimension reduction feature set to generate a similar index structure, analyzing the real-time sensor data stream, converting the real-time sensor data stream into a real-time feature vector, and mapping the real-time feature vector to the similar index structure to obtain a candidate accident record set; and performing similarity comparison on the candidate accident record set and the real-time feature vector to generate a similarity index, and outputting abnormal early warning information when the similarity index exceeds a preset threshold. According to the method, the historical accident data and the real-time sensor data are fused, rapid retrieval and comparison are achieved in a similar index structure, and the real-time performance and accuracy of early warning are improved.

A safety performance quantitative evaluation method for an autonomous driving system, comprising: establishing a vehicle accident rate decomposition model to decompose hazardous events at a vehicle level into hazardous events caused by functional deficiencies at a component level, the component level comprising a perception layer, a positioning layer, a planning layer, and a control layer; acquiring statistical probabilities of hazardous events caused by functional deficiencies in the planning layer, the control layer, and the positioning layer; performing scenario modeling on the probability of the hazardous events caused by the functional deficiency in the perception layer; obtaining confirmed objectives of functions at least on the basis of a natural driving data set, the vehicle accident rate decomposition model, the statistical probabilities of the hazardous events, and the scenario modeling of the perception layer; and using the confirmed objectives of the functions as standards for safety performance quantitative evaluation.

The invention discloses an automatic driving vehicle accident cause analysis method. The method comprises the steps of obtaining a data set of an automatic driving vehicle accident report; pre-processing the data set, wherein the pre-processing comprises abnormal value cleaning, error data correction and missing data repair; constructing an explanation variable system of an automatic driving vehicle accident, and carrying out statistical description on accident characteristics; an accident cause analysis model is provided by integrating existing automatic driving vehicle accident research data and conclusions based on a random parameter Logit method for solving a heterogeneity problem; based on priori knowledge and an analysis result, designing a random parametric variable, and constructing an RPL model: sampling a probability density function for multiple times by adopting a Monte Carlo method, and taking a simulated probability mean value as an integral approximate solution; and the analysis of the variable random parameter effect in the automatic driving vehicle accident is realized through the RPL model. According to the invention, the cause explanation of the heterogeneity of the important characteristics and influence factors of the accident of the automatic driving vehicle is realized.

The embodiments of the present application belong to the field of financial technology and relate to a vehicle loss estimation method, apparatus, computer equipment, and storage medium. The method comprises the following steps: identifying, classifying, and extracting features from sample data to obtain vehicle image features, vehicle description features, and vehicle information features, and calculating vehicle loss estimation pricing; combining vehicle image features, vehicle description features, and vehicle information features to obtain vehicle loss features, vehicle accident features, vehicle configuration features, and vehicle insurance policy features; inputting vehicle loss features, vehicle accident features, vehicle information features, vehicle insurance policy features, and vehicle loss estimation pricing into a preset learner for training, and performing integrated learning on the trained learner to obtain a vehicle loss estimation model; and inputting repair report data into the vehicle loss estimation model to obtain loss estimation data. The present application can effectively realize automated and efficient accurate estimation of vehicle losses.

The invention discloses an auxiliary driving automobile accident risk cause chain mining method. The method comprises the following steps: 1) acquiring multi-source time sequence data including vehicle running state data, environment perception data and road condition data; 2) data preprocessing; 3) according to the obtained multi-source time sequence data, calculating a fusion risk index value of the vehicle at the t moment; 4) by taking the high-risk time sequence evolution time section as an analysis window, analyzing accident risk process influence factor variables based on correlation analysis and causality analysis to obtain a core dependent variable set; and 5) based on the identified core cause variable set, carrying out risk cause chain analysis in combination with a target risk event. According to the method, the risk field model fusing the vehicle kinetic energy field and the surrounding potential energy field elements is constructed, so that a high-risk evolution section before a potential collision event occurs can be identified more accurately, and the method has higher real-time response capability and environmental adaptability.

The application discloses a vehicle accident detection method, device and equipment and a storage medium. The method comprises the following steps: when the distance between a moving object around a vehicle and the vehicle is less than a preset threshold, acquiring the vehicle video of the time period when the distance between the moving object and the vehicle is within the preset threshold, the time point and the distance when the distance between the moving object and the vehicle is the shortest; detecting the relative position area of the moving object and the vehicle at the target time point; determining the vehicle accident risk level according to the vibration condition of the vehicle and the target distance; acquiring the image of the moving object, performing feature extraction on the image, determining the object type of the moving object, and identifying the object features of the moving object according to the object type; acquiring the current parking site of the vehicle, generating a corresponding vehicle accident report, and sending the vehicle accident report and the target video to a corresponding client. The application can improve the event backtracking efficiency of the vehicle owner when the accident occurs.

The present invention relates to a vehicle accident notification system which detects whether a vehicle accident has occurred with respect to a first vehicle, generates information thereon on the basis of state information of the first vehicle, and notifies other nearby vehicles of accident information through communication with an external server. More specifically, the present invention relates to a vehicle accident notification system, comprising: a first communication unit installed in the first vehicle and communicating with an external server; a first detection unit installed in the first vehicle and acquiring first state information of the first vehicle; a safety triangle installed in a trunk of the first vehicle; and a first control unit installed in the first vehicle and configured to generate vehicle accident information if it is determined that an accident of the first vehicle has occurred using the first state information, wherein if the vehicle accident information is generated, the first control unit opens the trunk so that the safety triangle is deployed, and transmits the generated vehicle accident information to the outside through the first communication unit.

The invention provides a vehicle accident data processing method and device and a storage medium, and the method comprises the steps: S1, obtaining the vehicle state information of a vehicle within a preset time range before and after the accident time according to the accident time after a triggering event of an accident is received; s2, inputting the vehicle state information into a pre-trained vehicle accident analysis model, and judging whether the vehicle has an accident or not; s3, when it is judged that the vehicle has the accident, determining the latest effective parking time point; s4, according to the effective parking time point, judging whether at least one piece of alarm data of a predetermined type exists or not; if yes, determining the time corresponding to the first piece of alarm data of the pre-type as the accident occurrence time; otherwise, determining the time corresponding to the vehicle positioning record which is closest to the effective parking time point and has the vehicle speed greater than 0 as the accident occurrence time. By means of the technical scheme, the accident occurrence time can be determined more accurately.

The utility model discloses a traffic tool safety warning device, and relates to the field of automobile accident safety warning. The vehicle safety warning device comprises a triggering unit which can trigger a smoke warning unit to release smoke to the air; the smoke warning unit can be triggered by the triggering unit to release smoke for warning to the air, the smoke warning unit comprises a tank body, a smoke outlet guide pipe and a valve, the tank body is used for storing the warning smoke, the smoke outlet guide pipe is a warning smoke spraying channel, and the valve is used for controlling spraying of the warning smoke; the fixing unit is connected with the vehicle, the smoke warning unit and the triggering unit are fixed to the vehicle, and the fixing mode of the fixing unit is nut fixing and magnetic strip fixing; the vehicle safety warning device solves the problem that when a vehicle is difficult to move to a safe position after a fault or an accident occurs, a vehicle coming behind cannot be warned.

This invention discloses a method for accurate identification of the moment of an accident in a new energy vehicle based on multimodal data fusion. The method acquires vehicle motion sensor data, dynamic parameters, and visual image data, and then performs the following parallel processing: The physical layer processes the data to obtain motion features and compares them with preset trigger conditions; when a suspected collision is detected, it outputs the physical layer trigger time and acceleration amplitude; the dynamic layer processes the data based on the deviation between the vehicle's dynamic model and its actual motion state, outputting a vehicle trajectory status label; the visual layer processes the data to detect targets and analyze their motion state; when visual evidence of a collision is detected, it outputs the visual layer trigger time and the type of collision object; the fusion layer performs spatiotemporal alignment of the three layers' outputs and makes a comprehensive judgment, outputting the fused accurate collision trigger time and accident scene classification information. This invention effectively filters false alarms and improves the detection rate of low-speed accidents through multimodal fusion, achieving accurate identification of the moment of an accident and scene reconstruction.

This invention provides a pneumatically controlled fence control group for tunnel boring machines, relating to the field of control group technology, including road passages. After the centralized control system is implemented, personnel control the opening and closing of the fence from a fixed, safe position, eliminating the need for frequent movement along vehicle auxiliary transport routes, thus ensuring the safety of personnel operating the fence and achieving "safety without human intervention." With the centralized control system, shift leaders can closely monitor the on-site safety production situation, ensuring smooth and safe production. This solves the problem of personnel being easily collided with vehicles during operation, which increases the risk of vehicle accidents. Furthermore, manually opening the fence is inefficient, and automatically opening the fence using sensors is prone to errors, leading to lower safety. During use, collisions between vehicles and the fence can cause damage, increasing maintenance costs.

The invention discloses a networked vehicle accident battery cloud assessment method based on internal and external field coupled twinbodies, which comprises the following steps: firstly, defining a transient accident data window, extracting sensor data in the window by an Internet of Vehicles cloud platform, and constructing a multi-field incomplete time sequence data set of external impact and internal monomer voltage; secondly, constructing an internal and external field coupled twinborn body model, deducing and repairing an accident data blind area by using a damping and RC polarization attenuation algorithm, and obtaining an impact-voltage twinborn enhancement vector; and then, through a multi-source moment collaborative dynamics unit and a voltage entropy time-space outlier mining unit, calculating a mechanical clamping force decline value and a battery cell thermodynamic anomaly deviation degree, constructing a full-dimensional failure degree matrix, carrying out normalized weighted calculation to generate a quantitative evaluation vector, and outputting a risk positioning report. According to the invention, accurate quantification and graded early warning of the failure state of the battery pack accident can be realized under the extreme accident condition, and important technical support is provided for battery safety emergency disposal and management strategy optimization.

The application relates to a communication method and device. The method comprises the following steps: in response to an accident of a vehicle, selecting a target communication device from preset terminals and a public safety answering point (PSAP) according to a communication priority of the preset terminals and the PSAP; wherein the preset terminals are communication terminals of accident contacts associated with the vehicle; in response to the target communication device only comprising the PSAP, sending communication data to the PSAP to establish a communication connection with the PSAP, and recording a communication process with the PSAP to obtain communication audio data; in response to the communication with the PSAP being ended, sending a target communication request to the preset terminals to establish a communication connection with the preset terminals, and playing the communication audio data to the preset terminals. The method can enable the accident contacts associated with the vehicle to learn relevant information of the vehicle accident in time after the accident of the vehicle.

It provides a smart IoT-enabled vehicle accident detection and emergency response system that enables real-time detection of vehicle accidents and automatic transmission of location and alert data to emergency services, significantly reducing response times, improving rescue efficiency, minimizing human intervention, and enhancing overall road safety. A smart IoT-enabled vehicle accident detection and emergency response system 100 includes an accelerometer 102, a microcontroller 104, a GSM module 106, a GPS module 108, a Wi-Fi module 110, an alarm unit 112, a rectifier 114, and a voltage regulator 116. Upon detecting a collision, the system 100 automatically sends an emergency alert and precise location data to relevant parties or services, enabling a rapid response and improving road safety.

The application discloses a digital twin-enabled unmanned vehicle post-accident maintenance method, and mainly solves the low vehicle accident processing rate of the prior art.The implementation scheme is as follows: a task model and a network model under a digital twin network are constructed; maintenance information of each maintenance provider is published; a maintenance response strategy of the unmanned vehicle after an accident at time t is established; a maintenance strategy of each unmanned vehicle at each network edge device is determined; a bid of each network edge device for the unmanned vehicle is determined; matching between the network edge device and the unmanned vehicle is completed; a maintenance price to be paid by the unmanned vehicle is determined according to a VCG pricing mechanism; and the price is paid after the maintenance is completed.The application effectively solves the two-way selection problem between the accident vehicle and the maintenance provider, improves the vehicle accident processing rate, the average income of the maintenance provider and the average social welfare, and can be used for maintenance processing of the unmanned vehicle after an accident in a city traffic system.

The present application relates to the stress simulation method and storage medium of vehicle-mounted controller, first build vehicle-mounted controller simulation model, use finite element simulation analysis to carry out modal analysis, random vibration, impact load strength analysis and check to the equipment structure, determine whether resonance occurs; Whether the load working condition meets the checking standard; Can ensure that the given structure, material and other properties meet the safety factor; If the safety factor is not met, the structure, material and other properties of the vehicle-mounted controller need to be improved and adjusted, and correct guidance is given to the design to avoid deformation, rupture and other risks caused by unreasonable design, leading to vehicle accidents, and protecting the safety of life and property.

The invention discloses a serious vehicle accident reminding method and system, and the method comprises the steps that a vehicle-mounted monitoring device obtains the current state information of a current vehicle, and when it is judged that a serious vehicle accident happens to the current vehicle according to the current state information, serious accident information is generated and uploaded to a cloud monitoring platform; when the cloud monitoring platform judges that a serious vehicle accident actually occurs according to the serious accident information, serious accident reminding information is generated according to the serious accident information; and the cloud monitoring platform obtains related position information of nearby vehicles in the accident signal receiving range according to the serious accident information, and sends serious accident reminding information to the nearby vehicles according to the related position information. Whether a serious vehicle accident happens to the vehicle or not is detected by monitoring the state data of the vehicle during running in real time, and when the serious vehicle accident happens, accident information is uploaded to the cloud monitoring platform, so that emergency rescue of the accident vehicle and warning prompt of nearby vehicles are achieved.

The invention relates to the technical field of artificial intelligence and insurance claim settlement, can be applied to the financial field, and discloses a claim settlement method and device for a vehicle accident, computer equipment and a storage medium. The driving parameters are sent by the vehicle-mounted terminal when the vehicle has an accident; identifying field damage information of the target part in the damaged image; calculating damage verification information corresponding to the driving parameters based on a mechanics principle mathematical model; the three-dimensional physical simulation engine based on the accident scene simulates the collision process of the scene damage information, and outputs driving verification parameters; and determining a claim settlement scheme of the vehicle based on a comparison result between the driving parameter and the driving verification parameter and a comparison result between the field damage information and the damage verification information. Therefore, the problems of high difficulty and low efficiency of vehicle insurance loss assessment and claim settlement are solved.

The utility model discloses a mechanical structure for automatically forcibly opening a vehicle door, which relates to the technical field of vehicle-mounted machinery and comprises a vehicle door plate, one side of the vehicle door plate is connected to the side wall of an A-pillar mounting plate, and a mechanical mechanism for forcibly opening the vehicle door plate is fixedly arranged on one side of the A-pillar mounting plate and is used for forcibly damaging an electromagnetic lock in the vehicle door plate. The forced door opening mechanical mechanism comprises a servo motor and a transmission shaft, an output shaft of the servo motor is fixedly connected with the transmission shaft through a coupler, one end of the transmission shaft is fixedly connected with a worm, and the servo motor is fixedly connected to the A column mounting plate through a motor fixing base. A bearing seat is fixedly installed on one side of the A column installation plate, one end of the worm penetrates through and is rotationally installed in the bearing seat, the device recognizes a vehicle accident through a mechanical system circuit or a detection sensor, a transmission mechanism is driven by an automatic motor, the connecting position of the electromagnetic lock is forcibly damaged, and safety is improved.

The invention relates to a multifunctional inspection robot adapted to an urban elevated anti-collision wall, which comprises a frame for supporting the whole equipment, the frame is provided with a solar power generation device used for supplying power to the multifunctional inspection robot, an LED lighting device used for warning and lighting at night, a monitoring device used for inspection and monitoring of a dangerous construction site, and a clamp type power wheel set used for walking of the multifunctional inspection robot on a completed engineering anti-collision wall. The warning device is arranged on one side of the frame, the LED lighting device and the monitoring device are installed on the other side of the frame, and the clamp type power wheel set is installed on the inner side of the frame. According to the application, the surrounding environment along the anti-collision wall and vehicle accidents can be comprehensively monitored, 24-hour dead-angle-free fire early warning and anti-theft and anti-robbery early warning can be realized for the construction site of updating operation along the urban viaduct, and meanwhile, the construction monitoring is performed on the site in the whole process and in multiple directions, so that the management personnel can conveniently know the production working condition, and the working efficiency is improved. And manual inspection is reduced.

The application provides a driver fatigue prediction method and device, electronic equipment and storage medium, which obtains a monitoring video; obtains facial state features and a fatigue score according to the monitoring video; trains a fatigue prediction regression model by taking the facial state features as an input vector and the fatigue score as a target result; extracts facial state features of a to-be-detected driver from a to-be-detected image or video, inputs the facial state features of the to-be-detected driver into the trained fatigue prediction regression model, obtains a fatigue score corresponding to each time in a future period of time, and then can infer the time required to reach different fatigue levels according to the current time and the time corresponding to different fatigue levels, so as to achieve the purpose of predicting the fatigue condition of the driver in advance, help early warning, avoid vehicle accidents, and the same model can obtain corresponding prediction time according to different fatigue level requirements, and has high expansibility.