Interruption Management in the Context of Take-Over-Requests in Conditional Driving Automation

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4 Scopus citations


Drivers of partially automated vehicles are relieved from parts of the driving tasks allocated to the automated driver. This reduction in driving demands encourages them to engage with nondriving related tasks, which may impair awareness of the road environment once a takeover request (TOR) is initiated. This article examined the four suggested strategies drivers that take to regain control following a TOR, from the perspective of interruption management principles. Thirty students participated in a simulated study of two drives, where we manipulated TOR alerts, time to regain control, and potential road hazards. We hypothesized that all four interruption management strategies will be observed. Our hypothesis was confirmed. Four strategies were identified. Most drivers chose strategy 2 to accept and initiate the takeover immediately after the TOR started. The second frequent strategy was to reject the TOR but look at the road. Drivers' strategy choices changed following alert type and the chronological drive order. With simulated driving experience (i.e., second drive), drivers postponed taking control, adapting to the time budget. Yet, inaccurate understanding of the situation or over-trust affected the chosen strategy. We conclude that interruption management principles are beneficial for studying how drivers respond to TORs and evaluating options to improve TOR performance.

Original languageEnglish
Pages (from-to)1015-1024
Number of pages10
JournalIEEE Transactions on Human-Machine Systems
Issue number5
StatePublished - 1 Oct 2022


  • Autonomous driving
  • conditional automation
  • interruption methods strategies
  • interruptions
  • takeover requests (TORs)
  • transfer of control

ASJC Scopus subject areas

  • Human Factors and Ergonomics
  • Control and Systems Engineering
  • Signal Processing
  • Human-Computer Interaction
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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