Optimizing Driver Position Using ANOVA and ANNs
Year: 2025
Type: article
Abstract: The primary purpose of this study is to investigate how the optimal driving position is influenced by the forces acting on the driver and the performance requirements of a 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 innovative aspect of this study lies in its integration of driver positioning dynamics into performance optimization, offering a systematic approach to vehicle design before the CAD prototyping phase.
Source: IEEE Access
Institution: University of Bologna
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,075
OpenAlex ID: W4414404577