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Modeling of Human Response From Vehicle Performance Characteristics Using Artificial Neural Networks
ISSN: 0148-7191, e-ISSN: 2688-3627
Published May 07, 2002 by SAE International in United States
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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.
CitationMoon, K., Osborne, M., Kuykendall, D., and Poirier, W., "Modeling of Human Response From Vehicle Performance Characteristics Using Artificial Neural Networks," SAE Technical Paper 2002-01-1570, 2002, https://doi.org/10.4271/2002-01-1570.
- Chryssolouris, G., and Guillot M., “An A.I. Approach to the Selection of Process Parameters in Intelligent Machining,” Sensors and Controls for Manufacturing - 1988, ASME, PED-Vol. 33, 1988, pp. 199∼206.
- Domroese, M., and Chryssolouris G., “Sensor Integration for Tool Wear Estimation in Machining,” Sensors and Controls for Manufacturing - 1988, ASME, PED-Vol. 33, 1988, pp. 115∼123.
- Gillespie, Thomas D., Fundamentals of Vehicle Dynamics, Society of Automotive Engineers, Inc., 1992
- Klimasauskas, C., Guiver J., and Pelton G., Neural Works Explorer, NeuralWare Inc., 1989.
- Kubota, Midori, Ushijima Takayuki, and Brown Jac, “Correlation of Driver Confidence and Dynamic Measurements and the Effect of 4WD,” SAE Paper 950972, 1995
- Minsky, M.L., Papert, S., Perceptrons: An Introduction to Computational Geometry, Cambridge MA: MIT Press, 1969.
- Ottinger, Eric G., “A Study of Robotic Control Using Vision and Neural Network Techniques,” Masters Thesis, Michigan Technological University, Houghton MI, 1992.
- Reichelt, Warner, “Correlation Analysis of Open/Closed Loop Data for Objective Assessment of Handling Characteristics of Cars,” SAE Paper 910238, 1991.
- Ueda, Matsui, Ohno Y., Taniguchi Y., and Aoki H., “A New Method Using Neural Networks to Evaluate the Transitional Thermal Sensation of an Automobile Occupant,” SAE Paper 920217, 1992.
- Weir, D.H., and DiMarco R.J., “Correlation and Evaluation of Driver/Vehicle Directional Handling Data,” SAE Paper 780010, 1978