Ride comfort assessment for automated vehicles utilizing a road surface model and Monte Carlo simulations

Abstract

The growing number of automated vehicles (AVs) necessitates good ride comfort for passengers. This research investigates currently available ride comfort methods and evaluates their performance with a validated simulation framework. The methodology developed encompasses a high-precision road surface model and uses Monte Carlo simulations to compile accurate and representative virtual chassis acceleration data. By utilizing a threshold method and standard ISO 2631 ride comfort guidelines, results are compared to classifications based on empirical International Roughness Index data. A case study conducted in Austria specifies that ISO 2631 comfort estimates are most similar to International Roughness Index classifications and that the thresholding procedure detects preventable situations and over- or underestimated ride comfort. Thus, this methodology can help to better understand requirements for AVs' comfort, as well as justifying the importance of developing a sophisticated performance metric.