393 results about "Vehicle accident" patented technology

The invention provides a vehicle mutual-aid claim settlement method and system based on multi-modal data fusion. Wherein vision and depth sensing data and maintenance records in a vehicle accident are integrated, part types and collision mechanical parameters are extracted, and surface microscopic deformation and internal hidden cracks are captured through dual-band scanning; and carrying out time-space association on the two to construct a comprehensive damage map. And generating a standard damage sequence by using the generative adversarial network based on a normal damage rule of the component, comparing the standard damage sequence with accident damage data layer by layer, extracting an abnormal region deviating from an evolution rule, and calculating a fraud risk index according to the spatial density of the abnormal region. And when the index exceeds a dynamic threshold value, triggering multi-source data to cooperatively verify feature consistency, and outputting a claim settlement result with a fraud mark. According to the technical scheme provided by the invention, accurate identification of maintenance fraud and collaborative optimization of claim settlement risk control efficiency are realized.

Methods and systems described herein are directed to automatedly generating entity-specific notifications of a vehicle accident occurrence for entities. In response to a notification system receiving accident data, the system can determine characteristics for the accident occurrence. Using these characteristics, the system can then determine parameters for the notifications defining types of entities which ought to be recipients in accordance with the characteristics. The system can use these parameters to locate specific entities that match the parameters. Having determined the entities, the system can issue notifications to the entities, using templates specific to the entities and with accident characteristics added.

Techniques described herein relate to training and executing machine learning models configured to determine injury probabilities based on vehicle accident data. In some cases, a decision tree model may be constructed including various branching criteria based on particular vehicle accident data and injury ground truth data, including leaf nodes storing corresponding injury probabilities. A model execution component may execute the trained models to determine the probability of potential injuries associated with vehicle accidents. Based on the potential injury probabilities, a vehicle accident analysis system may identify target computer systems and / or target processes to be initiated on the target systems, based on the injury probabilities. In some example, the model execution architecture may be implemented using an event-driven system and / or cloud-based data storage and services to receive, store, and process data events associated with individual vehicle accidents.

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.

A method of vehicle accident response includes generating, by a mobile device located inside a vehicle, telematics data associated with operation of the vehicle, and transmitting, from the mobile device to one or more processors via wireless communication, the telematics data. The method also includes analyzing the telematics data to determine that an accident involving the vehicle has occurred, analyzing the telematics data to determine a likelihood of injury due to the accident, and generating a communication (i) based upon the telematics data and the likelihood of injury and (ii) in response to determining that the accident has occurred. The method further includes transmitting, to the mobile device, the communication.

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 invention discloses a CVaR-based highway hazardous chemical vehicle accident rescue and evacuation path planning method, and belongs to the technical field of traffic safety and emergency. The path planning method comprises the following steps: acquiring dangerous chemical vehicle transportation accident occurrence points, rescue vehicle rescue points, accident vehicle placement points (evacuation destinations) and road network related parameters; the rescue stage is a path from a rescue exit point to an accident occurrence point of a rescue vehicle, comprehensively considers that the response time of the rescue vehicle is shortest, the efficiency is highest and an upper-layer rescue vehicle travel time shortest model is established, considers that a secondary accident is extremely easy to occur in a dangerous chemical vehicle transportation accident on a highway, and constructs a lower-layer model with the minimum secondary accident risk based on CvaR; a highway dangerous chemical vehicle accident rescue double-layer game planning model is formed; in the evacuation stage, a dangerous chemical substance accident vehicle is a path from an accident occurrence point to a vehicle placement point, the possibility of pollution caused by dangerous chemical substance leakage in the evacuation stage is considered, and an accident vehicle evacuation path planning model based on the CVaR dangerous chemical substance leakage risk is established. According to the highway hazardous chemical vehicle accident rescue and evacuation path planning method provided by the invention, the road traffic safety level and the emergency rescue efficiency can be improved, and smooth and orderly operation of highway traffic is ensured.

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 belongs to the field of artificial intelligence and financial science and technology, and relates to a vehicle loss assessment method and device based on artificial intelligence, computer equipment and a storage medium, and the method comprises the steps: receiving image data and text data which are input by a user and are related to a vehicle accident of a target vehicle; preprocessing the image data to obtain target image data; integrating the text data to obtain target text data; performing feature extraction on the target image data based on an image processing module in the survey loss assessment model to obtain image features; performing feature extraction on the target text data based on a text processing module in the survey and loss assessment model to obtain text features; fusing the image features and the text features to obtain target fusion features; and performing loss assessment processing on the target fusion feature based on a target full connection layer in the survey loss assessment model to obtain a corresponding vehicle loss assessment result. The processing efficiency of vehicle loss assessment is improved, and the accuracy of the vehicle loss assessment result is improved.

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.

Methods and systems described herein are directed to automatedly generating and issuing accident data gathering recommendations for a vehicle accident occurrence. In response to a recommendation system detecting an occurrence of a vehicle accident, the system can retrieve initial accident data for the occurrence. Using the initial accident data, the system can generate accident data gathering recommendations to obtain additional accident data via mapping to one or more characteristics for the vehicle accident occurrence. The recommendations, when executed, can yield additional accident data that can be compiled with the initial accident data into a vehicle accident evidence package.

Some implementations of the technology relate to anonymizing personally identifiable information (PII) by creating a quick response (QR) code linked to a website that can verify that driver's license information and insurance information of a driver is available and valid, without showing the PII itself. Some implementations can allow another driver in an automobile accident to scan the QR code, enter her own insurance information, and can push the driver's PII to her insurance company. Some implementations can allow the other driver to download an encrypted file with the PII that can be shared with his insurance company. The QR code can be used in other contexts outside of automobile accidents as well, such as when a driver wishes to test drive a car, rent a car, buy a new car, etc., and needs to provide proof of a valid driver's license and insurance coverage.

The invention discloses a vehicle accident detection method, a vehicle accident detection device, vehicle accident detection equipment and a storage medium. Obtaining a vehicle video in a time period when the distance between the moving object and the vehicle is within a preset threshold value, a time point when the distance between the moving object and the vehicle is shortest, and the distance; detecting a relative position area of the moving object and the vehicle at the target time point; determining a vehicle accident risk level according to the vibration condition of the vehicle and the target distance; acquiring an image of a moving object, performing feature extraction on the image, determining an object type of the moving object, and identifying object features of the moving object according to the object type; and obtaining a current parking place of the vehicle, generating a corresponding vehicle accident report, and sending the vehicle accident report and the target video to a corresponding client. According to the invention, the event backtracking efficiency of the vehicle owner when the accident occurs can be improved.

The invention discloses a vehicle accident emergency escape system which comprises an independent power supply module, a multi-mode sensor array, an intelligent control unit and an execution driving module. The state of a vehicle is monitored in real time through the multi-sensor fusion technology, when accidents such as collision, fire or falling into water are detected, the system is automatically switched to an independent power source, a glass lifting motor is directly driven to forcibly open vehicle windows, a skylight and a trunk door, and an emergency escape channel is provided for people in the vehicle. One-key forced full-open and voice prompt functions are configured, and the independent power supply part is waterproof and fireproof, so that reliable work in an extreme environment can be ensured.

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.

Methods and systems described herein are directed to automatedly detecting a vehicle accident occurrence based on a generated accident suspicion. An accident detection system can retrieve a myriad of information as data relating to a driver or a vehicle of the driver. The retrieved information can be assessed to determine assignment of a suspicion of a vehicle accident occurrence. The suspicion assignment can be based on, with respect to historical insurance data, predetermined thresholds and / or machine learning for the retrieved information. The retrieved information can be verified by auxiliary data remote from the driver or the vehicle of the driver. The accident detection system can verify the suspicion by dispatching one or more notifications to the driver, and in the absence of response, determine whether the suspicion is true via the collection of information for a suspected location of the accident or from individuals associated with the driver.

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.

The invention provides an intelligent insurance claim settlement triggering system based on the Internet of Things technology, and the system collects data in real time through Internet of Things monitoring equipment disposed at the place of an insurance object, and carries out the analysis through a data processing unit. The core of the system is an intelligent claim settlement triggering algorithm which can automatically judge whether claim settlement requirements are met or not according to preset conditions. Once a specific event, such as a vehicle accident, a family fire or a health crisis, is detected, the system automatically triggers a claim settlement process through an intelligent contract to realize quick response. In addition, the system further comprises a user interaction interface, an insurance client and an insurance company are allowed to check the equipment state and the claim settlement progress in real time, and the safety and non-tampering performance of all data are ensured through the block chain technology. According to the system, through real-time monitoring and prediction analysis, the efficiency and customer satisfaction of the insurance industry are improved, the risk and cost are reduced, and an innovative automatic claim settlement solution is provided for the insurance industry.

The present embodiments relate to automatic claim value generation. Particularly, the present embodiments include retrieving information from various sources and processing the data to derive an overview of damage to vehicles incurred during a vehicle accident, deriving an estimated value (or “cost”) to rectify the damage, and a client-specific claim value to be presented to the client representing a value to rectify the damage based on a policy corresponding to the client. The present embodiments provide an automated (or “touchless”) claim value payout system to provide a claim value specific to the client and the accident incurred by the client that is performed by a computing device without intervention by an operator.

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 relates to a vehicle accident handling method, device and equipment and a medium, and the method comprises the steps: obtaining vehicle driving data; performing accident prediction on the vehicle driving data based on a pre-trained identification model to obtain an accident prediction result; the accident prediction result represents whether a potential accident exists or not; acquiring sensor data under the condition that the accident prediction result is that a potential accident exists, and matching with a preset accident condition according to the sensor data to determine an accident identification result; and when the accident identification result is that the vehicle accident occurs, generating an accident report of the vehicle accident. According to the technical scheme, the vehicle accident can be accurately recognized, the accident report can be generated, and the vehicle accident handling accuracy and the accident handling efficiency are improved.

The present invention provides a method for quantitatively assessing the risk of motor vehicle accidents at intersections, including: obtaining historical vehicle trajectory data within the intersection influence area and preprocessing to obtain preprocessed data; extracting vehicle dangerous driving behavior feature data including sudden acceleration and deceleration from the preprocessed data; respectively calculating the average daily frequency of dangerous vehicle behaviors under different turning labels; statistically calculating the average daily motor vehicle trajectory flow under different turning labels based on the vehicle historical trajectory data; calculating the spatiotemporal distribution of motor vehicle accident risk probabilities under different turning labels and generating a spatiotemporal map; and weighted calculating the overall motor vehicle accident risk level index at the intersection. The present invention utilizes the frequency characteristics of dangerous driving behaviors of networked vehicles to correlate with intersection accident probability characteristics, effectively making up for the difficulty in collecting traffic accident data, and effectively supporting safety warning services for vehicles passing through intersections.

The invention provides a vehicle rescue method and device and a cloud platform, and is applied to the technical field of vehicle intelligent control, and the method comprises the steps: responding to the received vehicle accident data, determining a positioning data group after a vehicle accident based on the vehicle accident data, and the positioning data group comprises a preset number of historical positioning data; obtaining the latest positioning data of the vehicle, and determining the validity of the latest positioning data based on the positioning data set; and in response to determining that the latest positioning data is valid, creating a rescue work order based on the latest positioning data. Accurate transmission and efficient utilization of rescue information are ensured, so that the success rate and timeliness of rescue actions are improved, and the life safety of a user is guaranteed to the maximum extent.

Provided is a vehicle accident loss assessment method and apparatus. The method includes: acquiring vehicle information of a vehicle and repair shop information (S110); virtualizing the vehicle into a corresponding graph in a three-dimensional coordinate system according to the vehicle information, determining a location of a collision portion of the vehicle based on the graph, and determining an accident type (S120); determining a core part involved in the collision portion and a damage form of the core part (S130) according to the vehicle information, the location of the collision portion in the three-dimensional coordinate system, and the accident type; calculating correlated damaged parts according to the core parts involved in the collision portion and the damage forms of the core parts, so as to obtain a list of damaged parts (S140); and generating a loss report by the list of damaged parts in combination with the repair shop information, wherein the loss report includes a loss assessment value (S150).

The invention discloses an intelligent loss check and assessment multi-modal system based on vehicle accident scene online evaluation, and the system comprises a user interaction interface module which is used for receiving client input and correcting input deviation through a feedback cycle regulation and control mechanism, and a multi-modal data fusion module which is used for collecting vehicle accident damage data uploaded by a client, and outputting the vehicle accident damage data to a server. The system comprises an accident data acquisition module used for acquiring accident damage data, a three-dimensional reconstruction model module used for constructing a vehicle accident three-dimensional model based on the acquired accident damage data and accurately evaluating the vehicle damage condition, an intelligent loss assessment module used for generating a loss assessment report according to an assessment result and implementing intelligent loss assessment operation, and a data storage module used for storing the accident data, the assessment result and the loss assessment report; according to the on-line evaluation intelligent loss checking and assessment multi-mode system based on the vehicle accident scene, feedback circulation of a user interaction interface is regulated and controlled, so that the problem of inaccurate loss assessment caused by client input deviation is solved.

In system and methods for loss mitigation, accident data associated with a vehicle accident involving a driver may be collected. The accident data may be analyzed, and a likely severity of the vehicle accident may be determined based upon the analysis of the accident data. A communication related to emergency assistance or an emergency assistance recommendation may be generated based upon the determined likely severity of the vehicle accident, and transmitted, via wireless communication, from one or more remote servers to a mobile device associated with the driver A wireless communication from the driver indicating approval or modification of the emergency assistance or emergency assistance recommendation may be received. A third party may be notified, via a communication sent from the remote server(s), of requested emergency assistance as approved or modified by the driver. An estimated insurance claim may also be generated based upon vehicle accident likely severity.

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.