1. Anaïs Halin. Understanding the Interplay Between the Driver, the Vehicle, and the Environment for Adapting Driving Automation. PhD thesis, University of Liège, Belgium, January 2026. Keyword(s): Driving automation, Automation level, Vehicle engagement, Driver involvement, Driving environment complexity, Adaptive automation, Human-automation interaction, Cooperation, Risk assessment, Risk management, Driver, Vehicle, Environment. [bibtex-entry]



2. Michaël Fonder. Reliable Monocular Depth Estimation for Unmanned Aerial Vehicles. PhD thesis, University of Liège, Belgium, July 2023. Keyword(s): Depth estimation, M4Depth, M4Depth+U. [bibtex-entry]

1. Michaël Fonder and Marc Ernst, DamienVan Droogenbroeck. M4Depth: Monocular depth estimation for autonomous vehicles in unseen environments. ArXiv, abs/2105.09847, May 2021. Keyword(s): Depth estimation, Drone, UAV, Artificial Intelligence, Deep learning. [bibtex-entry]

1. Anaïs Halin, Christel Devue, and Marc Van Droogenbroeck. DEV: A Driver-Environment-Vehicle Closed-Loop Framework for Risk-Aware Adaptive Automation of Driving. In Adjunct Proceedings of the International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI), Brisbane, Australia, pages 47-52, September 2025. ACM. Keyword(s): Driving automation, Automation level, Vehicle engagement, Driver involvement, Driving environment complexity, Adaptive automation, Human-automation interaction, Cooperation, Risk assessment, Risk management. [bibtex-entry]



2. Michaël Fonder and Marc Van Droogenbroeck. A technique to jointly estimate depth and depth uncertainty for unmanned aerial vehicles. In IEEE International Conference on Systems, Signals and Image Processing (IWSSIP), Ohrid, North Macedonia, pages 1-5, June 2023. Keyword(s): Depth, Depth estimation, Depth uncertainty, Deep learning, Drone, UAV, Parallax. [bibtex-entry]

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