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Conference Papers Year : 2023

Driver Model for Take-Over-Request in Autonomous Vehicles


This work presents a driver model for TakeOver Request (TOR) in autonomous vehicles (AVs) that considers the driver's mental state and response time during the transition from automatic to manual driving modes. The Fallback Ready User (FRU) is introduced as a key component that defines the minimum attention required from the driver to respond to TORs and system failures. Highly adaptive FRU models are essential for ensuring the safety of AVs. By using nonintrusive methods, such as facial expression tracking, to capture the driver's mental state and improve AV safety we study shared control between the vehicle and driver during TOR. The paper presents two application scenarios for TOR analysis in ADAVEC system: detecting when the driver is ready for TOR and reacting to unpredictable situations, such as driver sickness or drowsiness. The proposed driver model considers user personalization based on high-level features, long-term changes, and real-time evidence.
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hal-04205128 , version 1 (12-09-2023)



Ankica Barisic, Pierre Sigrist, Sylvain Oliver, Aurélien Sciarra, Marco Winckler. Driver Model for Take-Over-Request in Autonomous Vehicles. UMAP 2023 - 31st ACM Conference on User Modeling, Adaptation and Personalization, Jun 2023, Limassol, Cyprus. pp.317-324, ⟨10.1145/3563359.3596994⟩. ⟨hal-04205128⟩
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