Towards a human body model for prediction of vehicle occupant kinematics in omni-directional pre-crash events

Licentiate thesis, 2021

As the vehicle fleet becomes more equipped with crash avoidance systems, the proportion of crashes preceded by evasive manoeuvres is expected to increase. In an evasive manoeuvre, occupant position and posture can be influenced by the induced loading. Therefore, there is a need to predict the occupant response from evasive manoeuvres. During evasive manoeuvres, the occupant kinematics can also be affected by muscle activity, and as such, taking the effect from active muscles into account in simulations of occupant response to evasive manoeuvres is important.
In this thesis, a method for activation of the neck and lumbar muscles in an active human body model, based on recorded muscle activity from volunteers, was enhanced and evaluated. The active human body model successfully predicted passenger kinematics in lane change, braking, and combined manoeuvres. As a step towards a model capable of predicting driver kinematics in evasive manoeuvres, the same method was adapted to control the shoulder muscles. The model with active shoulder muscles was evaluated in a simplified test setup. The active model successfully predicted peak elbow displacement for all loading directions.
Based on the results from the included studies, an active muscle controller based on directionally dependent muscle activity data can successfully predict kinematics from reflex response to loading in a finite element human body model. These findings represent an important step towards developing an active human body model able to predict occupant kinematics and muscle forces in omni-directional pre-crash events.