Human factors of transitions in automated driving: A general framework and literature survey

Zhenji Lu, Riender Happee, Christopher Cabrall, Miltos Kyriakidis, Joost de Winter

Research output: Contribution to journalArticleScientificpeer-review

100 Citations (Scopus)
417 Downloads (Pure)


The topic of transitions in automated driving is becoming important now that cars are automated to ever greater extents. This paper proposes a theoretical framework to support and align human factors research on transitions in automated driving. Driving states are defined based on the allocation of primary driving tasks (i.e., lateral control, longitudinal control, and monitoring) between the driver and the automation. A transition in automated driving is defined as the process during which the human-automation system changes from one driving state to another, with transitions of monitoring activity and transitions of control being among the possibilities. Based on ‘Is the transition required?’, ‘Who initiates the transition?’, and ‘Who is in control after the transition?’, we define six types of control transitions between the driver and automation: (1) Optional Driver-Initiated Driver-in-Control, (2) Mandatory Driver-Initiated Driver-in-Control, (3) Optional Driver-Initiated Automation-in-Control, (4) Mandatory Driver-Initiated Automation-in-Control, (5) Automation-Initiated Driver-in-Control, and (6) Automation-Initiated Automation-in-Control. Use cases per transition type are introduced. Finally, we interpret previous experimental studies on transitions using our framework and identify areas for future research. We conclude that our framework of driving states and transitions is an important complement to the levels of automation proposed by transportation agencies, because it describes what the driver and automation are doing, rather than should be doing, at a moment of time.
Original languageEnglish
Pages (from-to)183–198
JournalTransportation Research. Part F: Traffic Psychology and Behaviour
Publication statusPublished - 2016


  • Human factors
  • Automated driving
  • Transitions
  • Transition classification


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