SICE Journal of Control, Measurement, and System Integration (Jun 2021)

Adaptive multi-modal interface model concerning mental workload in take-over request during semi-autonomous driving

  • Weiya Chen,
  • Tetsuo Sawaragi,
  • Toshihiro Hiraoka

DOI
https://doi.org/10.1080/18824889.2021.1894023
Journal volume & issue
Vol. 14, no. 2
pp. 10 – 21

Abstract

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With the development of automated driving technologies, human factors involved in automated driving are gaining increasing attention for a balanced implementation of the convenience brought by the technology and safety risk in commercial vehicle models. One influential human factor is mental workload. In the take-over request (TOR) from autonomous to manual driving at level 3 of International Society of Automotive Engineers' (SAE) Levels of Driving Automation, the time window for the driver to have full comprehension of the driving environment is extremely short, which means the driver is under high mental workload. To support the driver during a TOR, we propose an adaptive multi-modal interface model concerning mental workload. In this study, we evaluated the reliability of only part of the proposed model in a driving-simulator experiment as well as using the experimental data from a previous study.

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