Digital Human Body Model for Occupant Monitoring System

Abstract

Occupant monitoring systems have been developed and used for Autonomous Driving (AD) level 3+. These occupant monitoring systems have limitations in accuracy and measurement items. To compensate for this, a Digital Human Body Model (DHBM) based on the Modelica language is developed, and its features are introduced. Inverse Kinematics (IK) and Inverse Dynamics (ID) DHBMs are interlocked with the occupant monitoring system to increase measurement accuracy and calculate various information such as motion sickness and fatigue. However, simulation of occupant behavior prediction is impossible. Forward Dynamics (FD) DHBM is a model that implements the characteristics of the live human studied through experiments and can predict occupant behavior. However, parameter verification is necessary to trust the results of FD DHBM. It is developing real-time validation and parameter update algorithms for FD DHBM using occupant monitoring data, which are expected to be available in various fields such as comfort and safety.