6. Passenger-centric operations – Enlighten the dark aircraft cabin

This catalyst project addressed the structural lack of real-time operational information from inside the aircraft cabin during boarding. In current turnaround management, the cabin remains a black box, limiting the reliability of boarding time prediction and dynamic process control. The project established a sensor-based laboratory cabin environment replicating a single-aisle A220 configuration and implemented a multi-sensor architecture combining LiDAR as primary detection source with Lighthouse-based tracking and wearable localization sensors to ensure spatial stability under multi-participant conditions. A coherent data acquisition and processing pipeline was developed and linked to a mixed-reality environment for validation and demonstrator purposes. Structured laboratory experiments were conducted to identify and evaluate operationally meaningful sensor-derived input variables for boarding time prediction. Aggregated hand luggage count was validated as a robust candidate feature. Model-based evaluation indicated an improvement of approximately 10% in boarding time prediction accuracy when incorporating luggage information compared to baseline configurations. The project advanced the concept from simulation-based studies to a functional laboratory demonstrator corresponding to TRL 4.