Not All Situation Awareness Is the Same: Decoding Driver Awareness with Physiological Signals

Abstract

Takeover warnings in conditionally automated vehicles are typically triggered when the automated driving system detects that it is approaching its operational limits. However, without evidence of what drivers have perceived, interpreted, and anticipated about traffic events, such warnings may not provide precise support. While physiological modeling can provide non-intrusive, real-time evidence of driver situation awareness (SA), previous studies using physiological signals often represent driver SA using a single score, obscuring whether support should address spatial risk location or temporal hazard progression. This video presents target-specific SA modeling across four SA targets organized by these two event dimensions: forward-path and adjacent-lane SA for spatial risk location, and materialized- and unmaterialized-hazard SA for temporal hazard progression. In the video, we present an adaptive takeover support strategy based on target-specific SA modeling and our preliminary video-based takeover study. Overall, we demonstrate how fine-grained driver SA assessment can guide adaptive takeover support.