Predicting Driver's Transition Time to a Secondary Task Given an in-Vehicle Alert

Steven Hwang, Ashis G. Banerjee, Linda Ng Boyle

Research output: Contribution to journalArticlepeer-review


The goal of this study is to provide a framework, using hidden semi-Markov models, for modeling a driver's response time after an alert is provided in manual driving. Given the plethora of alerts and warning within a vehicle, there is a need to understand when a driver will respond after an alert is provided. Data from a previous driving simulator study, where drivers were interacting with an in-vehicle information system (IVIS) were used for model training. The final data set included 16 participants, with 288 task initiations. The proposed model could predict a driver's response time accurately using only a small portion of the available data, and had a mean absolute error of 0.51 seconds with 84% of predictions within an absolute error of 1 second. This framework has applicability in mitigating the risk of transitions in driver distraction. This includes transitions from the road to the secondary task and back to the road.

Original languageEnglish (US)
Pages (from-to)4739-4745
Number of pages7
JournalIEEE Transactions on Intelligent Transportation Systems
Issue number5
StatePublished - May 1 2022


  • In-vehicle alert
  • hidden semi-Markov model
  • prediction
  • warning

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications


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