Using Physiological Signals to Diagnose Driver Situation Awareness Deficits for Takeover Support in Automated Driving

Abstract

Takeover support requires identifying the situation awareness (SA) deficit affecting a driver’s response. A single SA measure cannot identify the deficit or support need. This study therefore defines support-relevant SA across two layers. The traffic-event layer identifies support timing and location through temporal (materialized and unmaterialized hazards) and spatial (forward path and adjacent lane) dimensions. The information-processing layer uses Quantitative Analysis of Situation Awareness (QASA) to separate actual SA (ASA; situation knowledge), perceived SA (PSA; confidence), and response bias (response criterion). Their intersection yields 12 support-relevant targets. Using physiological signals from 66 participants, the study applied the Weighted Importance Score and Frequency Count (WISFC) framework across six model families to compare retrospective window-level feature rankings. ASA and response bias rankings showed preliminary variation, whereas PSA shared pupil and gaze features across dimensions. These patterns suggest which targets may be physiologically distinguishable or require contextual evidence for takeover support.