Modeling of human response from vehicle performance characteristics using artificial neural networks
Document Type
Conference Proceeding
Publication Date
5-7-2002
Department
Department of Mechanical Engineering-Engineering Mechanics
Abstract
This study investigates a methodology in which the general public's subjective interpretation of vehicle handling and performance can be predicted. Several vehicle handling measurements were acquired, and associated metrics calculated, in a controlled setting. Human evaluators were then asked to drive and evaluate each vehicle in a winter driving school setting. Using the acquired data, multiple linear regression and artificial neural network (ANN) techniques were used to create and refine mathematical models of human subjective responses. It is shown that artificial neural networks, which have been trained with the sets of objective and subjective data, are both more accurate and more robust than multiple linear regression models created from the same data.
Publication Title
SAE Technical Papers
Recommended Citation
Moon, K.,
Osborne, M.,
Kuykendall, D.,
&
Poirier, W.
(2002).
Modeling of human response from vehicle performance characteristics using artificial neural networks.
SAE Technical Papers.
http://doi.org/10.4271/2002-01-1570
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p/2836
Publisher's Statement
Copyright © 2002 Society of Automotive Engineers, Inc. Publisher’s version of record: https://doi.org/10.4271/2002-01-1570