Optimizing Driver Position Using ANOVA and ANNs

Abstract

This study investigates how the optimal driving position is shaped by the forces acting on the driver and the performance requirements of an FSAE vehicle. Using a comprehensive dataset based on a parametric model, key SAE-standardized input variables are parameterized: the horizontal and vertical coordinates of the H-point, the horizontal distance between the AHP and H-point, the seatback angle, and the steering wheel position. The objective is to identify the ideal driving setup that balances vehicle performance with driver comfort. ANOVA is utilized to determine which of these ergonomic factors most significantly affect acceleration performance, while artificial neural networks provide a comparative method for validating results and predicting outcomes for new configurations. The novelty of this study lies in integrating driver positioning dynamics into performance optimization, providing a systematic approach to vehicle design before the CAD prototyping phase.