Should Steering Settings be Changed by the Driver or by the Vehicle Itself?

Timo Melman, Mark Weijerman, Joost de Winter*, David Abbink

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
56 Downloads (Pure)

Abstract

Introduction: Cars are increasingly computerized, and vehicle settings such as steering gain (SG) can now be altered during driving. However, it is unknown whether transitions in SG should be adaptable (i.e., triggered by driver input) or adaptive (i.e., triggered automatically). We examined this question for road segments expected to require different SG. Objective: This paper aimed to investigate whether SG mode changes should be made by the driver or automatically. Methods: Twenty-four participants drove under four conditions in a simulator: fixed low gain (FL), fixed high gain (FH), a machine-initiated steering system, which switched between the two SG levels at predetermined locations (MI), and a driver-initiated steering system, in which the SG level could be changed by pressing a button on the steering wheel (DI). Results: Participants showed poorer lane-keeping and reported higher effort for FH compared to FL on straights, while the opposite held true on curved roads. On curved roads, the MI condition yielded better lane-keeping and lower subjective effort than the DI condition. However, a substantial portion of the drivers gave low preference rankings to the MI system. Conclusion: Drivers prefer and benefit from a steering system with a variable rather than fixed gain. Furthermore, although automatic SG transitions reduce effort, some drivers reject this concept. Application: As the state of technology advances, MI transitions are becoming increasingly feasible, but whether drivers would want to delegate their decision-making authority to a machine remains a moot point.

Original languageEnglish
Pages (from-to)1201-1215
Number of pages15
JournalHuman Factors
Volume66 (2024)
Issue number4
DOIs
Publication statusPublished - 2022

Keywords

  • adaptable automation
  • adaptive automation
  • curve driving
  • driving simulator
  • function allocation
  • variable steering ratio

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