On-road evaluation of sickness-less motion planning in automated vehicles: 'Head motion first' helps!

With the rapid advancement of automated driving, motion sickness (MS) has gathered significant attention from both academia and industry. Advanced sensing, decision-making, and control systems in autonomous vehicles offer promising opportunities to mitigate MS in next-generation transportation. Traditionally, objective metrics such as Motion Sickness Dose Value (MSDV) and Motion Sickness Incidence (MSI) are derived directly from vehicle motion states to quantify MS severity. However, these metrics often overlook the filtering or amplification effects of human body dynamics on the motion stimuli experienced in occupant heads. To address this, we designed two motion planning algorithms to generate vehicle trajectories: one optimized solely based on vehicle motion (control group) and another incorporating occupant head motion dynamics (experimental group). Real-road experiments with 23 participants, using a within-subject design, were conducted on an automated vehicle. Results demonstrated that trajectories with 'head-motion-first' concept can significantly reduce MSDV, with subjective assessments via the Misery Scale (MISC) showing notable reductions in MS severity. This study represents one of the few occupant-in-the-loop on-road validations of MS mitigation through automated driving, confirming the effectiveness of incorporating head motion dynamics into MS-oriented trajectory planning.