Probabilistic Estimation of Occupant Body Motion for Investigating Discomfort

The chapter delves into the challenge of estimating occupant body motion in vehicles to understand passenger discomfort, particularly focusing on the limitations of existing methods. It introduces a novel motion capture system that integrates inertial sensors and marker-based computer vision, enhanced by probabilistic sensor fusion using an Extended Kalman Filter (EKF). The system is designed to estimate the 3D translational and rotational motion of the occupant's head and torso, addressing the issues of drift and noise inherent in individual measurement techniques. Experimental results validate the system's accuracy, showing significant reductions in estimation errors compared to traditional methods. The chapter concludes with a discussion on the system's feasibility and potential future enhancements, making it a valuable resource for professionals seeking advanced solutions in occupant motion estimation and vehicle dynamics.