Steering through Time: Blending Longitudinal Data with Simulation to Rethink Human-Autonomous Vehicle Interaction

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Abstract:As semi-automated vehicles (SAVs) become more common, ensuring effective human-vehicle interaction during control handovers remains a critical safety challenge. Existing studies often rely on single-session simulator experiments or naturalistic driving datasets, which often lack temporal context on drivers' cognitive and physiological states before takeover events. This study introduces a hybrid framework combining longitudinal mobile sensing with high-fidelity driving simulation to examine driver readiness in semi-automated contexts. In a pilot study with 38 participants, we collected 7 days of wearable physiological data and daily surveys on stress, arousal, valence, and sleep quality, followed by an in-lab simulation with scripted takeover events under varying secondary task conditions. Multimodal sensing, including eye tracking, fNIRS, and physiological measures, captured real-time responses. Preliminary analysis shows the framework's feasibility and individual variability in baseline and in-task measures; for example, fixation duration and takeover control time differed by task type, and RMSSD showed high inter-individual stability. This proof-of-concept supports the development of personalized, context-aware driver monitoring by linking temporally layered data with real-time performance.

Subjects:

Human-Computer Interaction (cs.HC)

ACM classes: H.5; I.2

Cite as: arXiv:2604.00832 [cs.HC]

(or arXiv:2604.00832v1 [cs.HC] for this version)

https://doi.org/10.48550/arXiv.2604.00832

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Shiva Azimi [view email] [v1] Wed, 1 Apr 2026 12:42:57 UTC (10,910 KB)