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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2604.00832 |
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| _version_ | 1866917377431568384 |
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| author | Hakiminejad, Yasaman Azimi, Shiva Gomero, Luis Pantesco, Elizabeth Kan, Irene P. Izzetoglu, Meltem Tavakoli, Arash |
| author_facet | Hakiminejad, Yasaman Azimi, Shiva Gomero, Luis Pantesco, Elizabeth Kan, Irene P. Izzetoglu, Meltem Tavakoli, Arash |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_00832 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Steering through Time: Blending Longitudinal Data with Simulation to Rethink Human-Autonomous Vehicle Interaction Hakiminejad, Yasaman Azimi, Shiva Gomero, Luis Pantesco, Elizabeth Kan, Irene P. Izzetoglu, Meltem Tavakoli, Arash Human-Computer Interaction H.5; I.2 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. |
| title | Steering through Time: Blending Longitudinal Data with Simulation to Rethink Human-Autonomous Vehicle Interaction |
| topic | Human-Computer Interaction H.5; I.2 |
| url | https://arxiv.org/abs/2604.00832 |