Modeling the effects of perceived intuitiveness and urgency of various auditory warnings on driver takeover performance in automated vehicles

Sangjin Ko, Harsh Sanghavi, Yiqi Zhang, Myounghoon Jeon

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Existing driver models mainly account for drivers’ responses to visual cues in manually controlled vehicles. The present study is one of the few attempts to model drivers’ responses to auditory cues in automated vehicles. It developed a mathematical model to quantify the effects of characteristics of auditory cues on drivers’ response to takeover requests in automated vehicles. The current study enhanced queuing network-model human processor (QN-MHP) by modeling the effects of different auditory warnings, including speech, spearcon, and earcon. Different levels of intuitiveness and urgency of each sound were used to estimate the psychological parameters, such as perceived trust and urgency. The model predictions of takeover time were validated via an experimental study using driving simulation with resultant R squares of 0.925 and root-mean-square-error of 73 ms. The developed mathematical model can contribute to modeling the effects of auditory cues and providing design guidelines for standard takeover request warnings for automated vehicles.

Original languageEnglish (US)
Pages (from-to)70-83
Number of pages14
JournalTransportation Research Part F: Traffic Psychology and Behaviour
Volume90
DOIs
StatePublished - Oct 2022

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Automotive Engineering
  • Transportation
  • Applied Psychology

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