6533b7dafe1ef96bd126ee24
RESEARCH PRODUCT
Data-based modeling of vehicle crash using adaptive neural-fuzzy inference system
Witold PawlusHamid Reza KarimiKjell G. RobbersmyrLin Zhaosubject
Adaptive neuro fuzzy inference systemEngineeringVehicle crash reconstructionAdaptive neural-fuzzy inference system (ANFIS)-based prediction; Time-series analysis; Vehicle crash reconstruction; Vehicle dynamics modeling; Control and Systems Engineering; Computer Science Applications1707 Computer Vision and Pattern Recognition; Electrical and Electronic Engineeringbusiness.industryControl engineeringComputer Science Applications1707 Computer Vision and Pattern RecognitionKinematicsCollisionDisplacement (vector)Computer Science ApplicationsVehicle dynamicsAccelerationAdaptive neural-fuzzy inference system (ANFIS)-based predictionControl and Systems EngineeringTime-series analysisTime seriesElectrical and Electronic EngineeringbusinessReliability (statistics)Vehicle dynamics modelingdescription
Vehicle crashes are considered to be events that are extremely complex to be analyzed from the mathematical point of view. In order to establish a mathematical model of a vehicle crash, one needs to consider various areas of research. For this reason, to simplify the analysis and improve the modeling process, in this paper, a novel adaptive neurofuzzy inference system (ANFIS-based) approach to reconstruct kinematics of colliding vehicles is presented. A typical five-layered ANFIS structure is trained to reproduce kinematics (acceleration, velocity, and displacement) of a vehicle involved in an oblique barrier collision. Subsequently, the same ANFIS structure is applied to simulate different types of collisions than the one which was used in the training stage. Finally, the simulation outcomes are compared with the results obtained by applying different modeling techniques. The reliability of the proposed method is evaluated thanks to this comparative analysis.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2014-04-01 |