Artificial Neural Networks as a tool for ergonomic evaluations of vehicle control panels

Abstract

Unreadable and inconveniently arranged instruments make it difficult for the driver to accurately read signals and understand the relayed information. They can distract the driver and prolong response times, thus posing a risk to traffic safety. Designers also have to account for customer expectations, including a demand for esthetically appealing dashboards that incorporate vast amounts of data in limited space since such dashboards appear to be maximally adapted to the driver’s needs. However, attractive dashboards are not always adapted to human perceptual abilities. A neural model was developed in the study to objectively assess dashboard ergonomics in passenger cars. The data were used to determine the correlations between subjective driver impressions and the functionality and ergonomics of dashboards evaluated objectively based on the adopted criteria. With the best-learned networks, 3 conformance classes were obtained for the predicted cases. However, taking into account the ± 1 class, as many as 3 of the preserved ANN gave correct answers in all 6 cases.