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Take-Over Requests after Waking in Autonomous Vehicles

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Abstract
Autonomous vehicles (AVs) enable drivers to devote their primary attention to non-driving-related tasks (NDRTs). Consequently, AVs must provide intelligibility services appropriate to drivers' in-situ states and in-car activities to ensure driver safety, and accounting for the type of NDRT being performed can result in higher intelligibility. We discovered that sleeping is drivers' most preferred NDRT, and this could also result in a critical scenario when a take-over request (TOR) occurs. In this study, we designed TOR situations where drivers are woken from sleep in a high-fidelity AV simulator with motion systems, aiming to examine how drivers react to a TOR provided with our experimental conditions. We investigated how driving performance, perceived task workload, AV acceptance, and physiological responses in a TOR vary according to two factors: (1) feedforward timings and (2) presentation modalities. The results showed that when awakened by a TOR alert delivered >10 s prior to an event, drivers were more focused on the driving context and were unlikely to be influenced by TOR modality, whereas TOR alerts delivered <5 s prior needed a visual accompaniment to quickly inform drivers of on-road situations. This study furthers understanding of how a driver's cognitive and physical demands interact with TOR situations at the moment of waking from sleep and designs effective interventions for intelligibility services to best comply with safety and driver experience in AVs.
Author(s)
Kim, WonJeon, EunkiKim, GwangbinYeo, DohyeonKim, SeungJun
Issued Date
2022-02
Type
Article
DOI
10.3390/app12031438
URI
https://scholar.gist.ac.kr/handle/local/11011
Publisher
MDPI
Citation
APPLIED SCIENCES-BASEL, v.12, no.3
ISSN
2076-3417
Appears in Collections:
Department of AI Convergence > 1. Journal Articles
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