Modeling the Effect of Vehicle Mass and Center of Gravity Height on Adaptive Cruise Control and Lane Keeping Assist Systems Using Machine Learning

It is not feasible to conduct road tests for all scenarios related to Advanced Driver Assistance Systems (ADAS) functions due to time, cost, and safety constraints. In this study, the effects of changes in vehicle weight and center of gravity (CoG) height on vehicle behavior in Adaptive Cruise Control (ACC) and Lane Keeping Assist System (LKAS) scenarios were examined. Relevant standards and regulations for ACC and LKAS were reviewed, and test matrices for simulations were created. The environmental and vehicle conditions for the selected scenarios were simulated, and tests were conducted. Key Performance Indicators (KPIs) specific to each maneuver were calculated using simulation data. Machine Learning (ML) models were developed to understand whether there is a relationship between the calculated KPIs and changes in vehicle variants. The relationships between vehicle variants and KPIs were reported as a result of the study.