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

In a future connected vehicle environment, an optimized route and motion planning should not only fulfill efficiency safety constraints but also minimize motions oscillations, causing poor ride comfort perceived by passengers. This work provides framework for large-scale cost-efficient evaluation to address AV's allow the comparison of different assessment strategies. The proposed tool gives insights data, allowing development novel algorithms, guidelines, or systems incorporating passenger comfort. A vehicle-road simulation utilizable assess most common determination strategies based on dynamics data is presented. developed methodology encompasses road surface model, non-linear model optimization, Monte Carlo simulations accurate generation virtual chassis acceleration data. Ride determined applying commonly used threshold method analysis ISO 2631. two methods are compared against classifications empirical measurements International Roughness Index (IRI). case study with three sites in Austria demonstrates framework's practical application real achieves high-resolution classifications. results highlight that 2631 estimates similar IRI thresholding procedure detects preventable situations over- underestimates Hence, shows potential risk negative AVs using simple values stresses importance robust enhancing AVs' path maximal