ITSC 2025 Paper Abstract

Paper VP-VP.47

Ji, Wenfei (Tsinghua University), Velenis, Efstathios (Cranfield University), Tian, Guangyu (Tsinghua University)

A Vestibular-Based Trajectory Optimization Framework for Minimizing Motion Sickness

Scheduled for presentation during the Video Session "On-Demand Video Presentations" (VP-VP), Saturday, November 22, 2025, 08:00−18:00, On-Demand Platform

2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC), November 18-21, 2025, Gold Coast, Australia

This information is tentative and subject to change. Compiled on April 2, 2026

Keywords Real-time Motion Planning and Control for Autonomous Vehicles in ITS Networks, Human-Machine Interaction Systems for Enhanced Driver Assistance and Safety

Abstract

Motion sickness poses a significant barrier to the widespread adoption of autonomous vehicles, preventing the realization of their potential safety, environmental, and accessibility benefits. This study proposes a comfort-oriented trajectory optimization framework aimed at minimizing motion sickness incidence (MSI) under cornering scenarios. The 6DOF-SVC model, rooted in vestibular physiology and sensory conflict theory, is integrated here into a trajectory optimization framework for the first time. This study specifically examines the generation of optimal motion trajectories along curved paths between two predefined points, reflecting a typical structure of daily driving scenarios. Numerical solutions are obtained using CasADi and systematically compared with results derived from the scheme based on the Motion Sickness Dosage Value model (MSDV), which has been widely employed in related motion optimization research. The results show that the control profiles derived from the SVC-based approach consistently produce significantly lower MSI values across all evaluated cases. Moreover, a quasi-convex relationship is observed between travel duration and resulting MSI, enabling the identification of the global optimal control problem through free-terminal-time optimization. These findings demonstrate the significance of embedding physiology-based motion sickness modeling into trajectory planning, which could offer a more realistic basis for developing comfort-aware control strategies in intelligent vehicle systems.