11 - Operations and Sustainment

PROVIDING SENSOR INFORMATION FOR BOARDING TIME PREDICTION

M. Schultz¹, S. Göppel¹, S. Reitmann, University of Technology Chemnitz, Germany; ¹University of the Bundeswehr Munich, Germany

We focus on providing operationally relevant sensor information to observe passenger states and the amount of carry on baggage during the boarding process. We develop a machine learning based perception approach to quantify carry on baggage within the boarding sequence, using video based and LiDAR based sensing in a mixed reality aircraft cabin environment under realistic boarding scenarios.

View Paper

DOI: 10.71945/icas2026_0611