Steering through Time: Blending Longitudinal Data with Simulation to Rethink Human-Autonomous Vehicle Interaction
This addresses safety challenges for drivers and manufacturers in semi-automated vehicles, but it is incremental as it builds on existing sensing and simulation methods.
The study tackled the problem of ensuring effective human-vehicle interaction during control handovers in semi-automated vehicles by introducing a hybrid framework that combines longitudinal mobile sensing with high-fidelity driving simulation to examine driver readiness. The result from a pilot with 38 participants showed the framework's feasibility, with individual variability in measures like fixation duration and takeover control time differing by task type, and RMSSD showing high inter-individual stability.
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.