Modeling of Human Response From Vehicle Performance Characteristics Using Artificial Neural Networks 2002-01-1570
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.
Citation: Moon, 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. Download Citation
Kee S. Moon, Mark D. Osborne, Dustin J. Kuykendall, William H. Poirier
Michigan Technological University, General Motors
SAE 2002 Automotive Dynamics & Stability Conference and Exhibition
Proceedings of the 2002 SAE Automotive Dynamics and Stability Conference-P-377