Occupant monitoring in automated vehicles: Classification of situation awareness based on head movements while cornering

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. Ina driving-simulator-study (n=43, within-subject design), four scenarios were used to generate or deteriorate situation awareness (manipulation checked). Recurrent neural networks weretrained 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.