Occupant Monitoring in Automated Vehicles: Classification of Situation Awareness Based on Head Movements While Cornering. (November 2019)

• Record Type: Journal Article
• Authors: Schewe, Frederik; Cheng, Hao; Hafner, Alexander; Sester, Monika; Vollrath, Mark
• Is Part Of: Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Volume 63:Part 1(2019)
• Page Start: 2078
• Page End: 2082
• Publication Date: 2019-11
• Subjects: Human engineering -- Congresses; 620.8205
• DOI: 10.1177/1071181319631048
• Languages: English
• ISSNs: 1541-9312

Abstract

We tested whether head-movements under automated driving can be used to classify a vehicle occupant as either situation-aware or unaware. While manually cornering, an active driver's head tilt correlates with the road angle which serves as a visual reference, whereas an inactive passenger's head follows the g-forces. Transferred to partial/conditional automation, the question arises whether aware occupant's head-movements are comparable to drivers and if this can be used for classification. In a driving-simulator-study (n=43, within-subject design), four scenarios were used to generate or deteriorate situation awareness (manipulation checked). Recurrent neural networks were trained with the resulting head-movements. Inference statistics were used to extract the discriminating feature, ensuring explainability. A very accurate classification was achieved and the mean side rotation-rate was identified as the most differentiating factor. Aware occupants behave more like drivers. Therefore, head-movements can be used to classify situation awareness in experimental settings but also in real driving.