Take-Over Requests after Waking in Autonomous Vehicles

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 per...

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Bibliographic Details
Published in:Applied sciences 2022-02, Vol.12 (3), p.1438
Main Authors: Kim, Won, Jeon, Eunki, Kim, Gwangbin, Yeo, Dohyeon, Kim, SeungJun
Format: Article
Language:English
Subjects:
automated driving
Automation
Autonomous vehicles
Cognitive load
Driving
Driving ability
feedforward
Intelligibility
modality
Motion systems
Physiological effects
Physiological responses
Simulator fidelity
Sleep
Sleep and wakefulness
sleeping
take-over request
Text messaging
Traffic congestion
User experience
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Summary: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
ISSN:2076-3417
2076-3417
DOI:10.3390/app12031438