aceSupporting the driver's perception cognition action loop using an embodied intelligent cockpit framework for explainable fatigue intervention
Objectives: This study evaluated a driver-support framework combining multimodal fatigue detection, feature attribution, and state-specific auditory feedback during conditional automated driving. Methods: Forty licensed drivers completed three counterbalanced 20-min simulator conditions: nonverbal warnings, generic speech, and explanatory speech. A fixed detector used electroencephalography (EEG), eye tracking, and vehicle dynamics. Shapley additive explanations (SHAP) selected a dominant cue, which a large lang…
In a 40-driver simulator study, explanatory speech about the detected fatigue cue shortened mean braking reaction time by 0.192 s and lowered lane-position variability by 0.085 m compared with generic speech.
The findings come from a simulator study, and the authors say further work should separate message content from acoustic properties and assess performance in independent driving settings.
Evidence
- Peer-reviewedTraffic Injury Prevention2026-11-03
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Truvace Impact Record TRV-2026-2366, v1: “Supporting the driver's perception cognition action loop using an embodied intelligent cockpit framework for explainable fatigue intervention.” Truvace, 2026-11-04. /record/TRV-2026-2366 (accessed at citation time). sha256 a57dd66a9d82c5a2…
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