Rolling out the red (and green) carpet: Supporting driver decision making in automation-to-manual transitions

Alexander Eriksson, Bastiaan Petermeijer, Markus Zimmermann, Joost de Winter, Klaus J. Bengler, Neville A. Stanton

Research output: Contribution to journalArticleScientificpeer-review

12 Citations (Scopus)
113 Downloads (Pure)

Abstract

This paper assessed four types of human–machine interfaces (HMIs), classified according to the stages of automation proposed by Parasuraman et al. [“A model for types and levels of human interaction with automation,” IEEE Trans. Syst. Man, Cybern. A, Syst. Humans, vol. 30, no. 3, pp. 286–297, May 2000]. We hypothesized that drivers would implement decisions (lane changing or braking) faster and more correctly when receiving support at a higher automation stage during transitions from conditionally automated driving to manual driving. In total, 25 participants with a mean age of 25.7 years (range 19–36 years) drove four trials in a driving simulator, experiencing four HMIs having the following different stages of automation: baseline (information acquisition—low), sphere (information acquisition—high), carpet (information analysis), and arrow (decision selection), presented as visual overlays on the surroundings. The HMIs provided information during two scenarios, namely a lane change and a braking scenario. Results showed that the HMIs did not significantly affect the drivers’ initial reaction to the take-over request. Improvements were found, however, in the decision-making process: When drivers experienced the carpet or arrow interface, an improvement in correct decisions (i.e., to brake or change lane) occurred. It is concluded that visual HMIs can assist drivers in making a correct braking or lane change maneuver in a take-over scenario. Future research could be directed toward misuse, disuse, errors of omission, and errors of commission.
Original languageEnglish
Article number8594655
Pages (from-to)20-31
JournalIEEE Transactions on Human-Machine Systems
Volume49
Issue number1
DOIs
Publication statusPublished - 2019

Keywords

  • Augmented reality
  • automated driving
  • driver support systems
  • human factors
  • human performance
  • transitions of control

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