Towards Using Active Learning Methods for Human-Seat Interactions To Generate Realistic Occupant Motion

TL;DR

This paper presents an active learning approach integrated with finite element simulations to predict occupant motion and contact forces in vehicle scenarios, aiding the design of safer, more comfortable autonomous vehicle interiors.

Contribution

It introduces an active learning method to efficiently generate training data for occupant contact models, improving prediction accuracy and reducing manual effort.

Findings

Accurately predicts contact forces and moments during head-headrest interactions.

Reduces the amount of high-fidelity simulation data needed for training.

Demonstrates feasibility in a case study with promising results.

Abstract

In the context of developing new vehicle concepts, especially autonomous vehicles with novel seating arrangements and occupant activities, predicting occupant motion can be a tool for ensuring safety and comfort. In this study, a data-driven surrogate contact model integrated into an optimal control framework to predict human occupant behavior during driving maneuvers is presented. High-fidelity finite element simulations are utilized to generate a dataset of interaction forces and moments for various human body configurations and velocities. To automate the generation of training data, an active learning approach is introduced, which iteratively queries the high-fidelity finite element simulation for an additional dataset. The feasibility and effectiveness of the proposed method are demonstrated through a case study of a head interaction with an automotive headrest, showing promising results in accurately replicating contact forces and moments while reducing manual effort.