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System and method for establishing target division remote damage assessment of different vehicle types based artificial intelligence radial basis function neural network method

A neural network and artificial intelligence technology, applied in biological neural network models, instruments, character and pattern recognition, etc., to achieve the effect of improving accuracy

Inactive Publication Date: 2016-10-26
DALIAN ROILAND SCI & TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In order to solve the problem of detecting the target of the colliding vehicle after the collision, the present invention proposes an artificial intelligence radial basis function neural network method to establish a remote damage assessment system for different types of vehicles and targets, so as to realize the damage assessment process. Object detection and judgment in

Method used

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  • System and method for establishing target division remote damage assessment of different vehicle types based artificial intelligence radial basis function neural network method
  • System and method for establishing target division remote damage assessment of different vehicle types based artificial intelligence radial basis function neural network method
  • System and method for establishing target division remote damage assessment of different vehicle types based artificial intelligence radial basis function neural network method

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Embodiment 1

[0026] An artificial intelligence radial basis function neural network method to establish a remote damage assessment system for different vehicle types and targets, including:

[0027] The model selection subsystem selects the model data corresponding to the vehicle as the total data set;

[0028] The data classification subsystem reads CAE simulation data and real vehicle data, and classifies the data accordingly;

[0029] The collision detection subsystem judges whether the vehicle collides during driving; the collision detection subsystem learns the collision training data to generate a collision model, and the collision model is established using a radial basis function neural network method;

[0030] The working condition detection subsystem judges all the working condition information of the collision; the working condition detection subsystem learns the working condition training data to generate a working condition model, and the working condition model is established...

Embodiment 2

[0050] A method for establishing a remote damage assessment method based on an artificial intelligence radial basis function neural network method for different sub-vehicle sub-targets, comprising the following steps:

[0051] Step 1. Select the model data corresponding to the vehicle as the total data set;

[0052] Step 2. Read the CAE simulation data and real vehicle data, and classify the data accordingly;

[0053] Step 3. Judging whether the vehicle collides during driving; the collision detection subsystem learns the collision training data to generate a collision model, and the collision model is established using a radial basis function neural network method;

[0054] Step 4. Judgment of all working condition information where the collision occurs; the working condition detection subsystem learns working condition training data to generate a working condition model, and the working condition model is established using a radial basis function neural network method;

[0...

Embodiment 3

[0086] Supplement for the radial basis function neural network method described in embodiment 1 or 2: the RBF network can approach any nonlinear function, can handle the regularity that is difficult to resolve in the system, has good generalization ability, and has Fast learning convergence speed, has been successfully applied to nonlinear function approximation, time series analysis, data classification, pattern recognition, information processing, image processing, system modeling, control and fault diagnosis, etc.

[0087] RBF (Radial Basis Function) can be regarded as a surface fitting (approximation) problem in a high-dimensional space. Learning is to find a surface that can best match the training data in a multi-dimensional space, and then a batch of new data, Use the surface you just trained for processing (such as classification, regression). The essential idea of ​​RBF is that the backpropagation learning algorithm applies a recursive technique, which is called stoch...

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Abstract

The invention relates to a system and method for establishing target division remote damage assessment of different vehicle types based an artificial intelligence radial basis function neural network method and belongs to the vehicle damage assessment field. The objective of the invention is to solve problems in target detection of collision vehicles after a vehicle collision. According to the technical schemes of the invention, a target detection subsystem is adopted to judge collision objects in the vehicle collision; the target detection subsystem learns target training data so as to generate a target model, wherein the target model is built by adopting the radial basis function neural network method. With the system and method provided by the technical schemes of the invention adopted, target detection in the vehicle collision can be realized; and a machine learning method is used in the remote damage assessment technical field, so that the accuracy of judgment in a damage assessment process can be improved.

Description

technical field [0001] The invention belongs to the field of vehicle damage assessment, and relates to a remote damage assessment system and method based on an artificial intelligence radial basis function neural network method for establishing different vehicle types and targets. Background technique [0002] Aiming at the problem of claim settlement disputes caused by frequent collisions of vehicles during low-speed movement (including low-speed road driving, vehicle parking, etc.), the remote damage assessment technology collects various signals (such as speed, acceleration, angular velocity, Sound, etc.) and analyzed with signal processing and machine learning techniques to determine whether a collision has occurred and the damage to the vehicle after the collision. [0003] After a vehicle collision accident, the front-end equipment can detect the occurrence of the collision and intercept the signal of the collision process, and send it to the cloud through the wireless...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/02
CPCG06N3/02G06F18/24G06F18/214
Inventor 田雨农刘俊俍
Owner DALIAN ROILAND SCI & TECH CO LTD
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