Accurate ride comfort estimation combining accelerometer measurements, anthropometric data and neural networks

Ride comfort can heavily influence user experience and therefore comprises one of the most important vehicle design targets. Although ride comfort has been heavily researched, there is still no definite solution to its accurate estimation. This can be attributed, to a large extent, to the subjective nature of the problem. Aim of this study was to explore the use of neural networks for the accurate estimation of ride comfort by combining anthropometric data and acceleration measurements. Different acceleration inputs, neural network architectures, training algorithms and objective functions were systematically investigated, and optimal parameters were derived. New insight into the influence of anthropometric data on ride comfort has been gained. The results indicate that the proposed method improves the accuracy of subjective ride comfort estimation compared to current standards. Neural networks were trained using data derived from a range of field trials involving ten participants, on public roads and controlled environment. A clustering and sensitivity analysis complements the study and identifies the most important factors influencing subjective ride comfort evaluation.