PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
February 28, 2026Journal of Intelligent and Connected Vehicles1 citationsOpen Access

Examining driving behaviors and trust in an in-vehicle warning system under uncertainty: a roundabout study

CZCong ZhangCTChi TianTHTianfang Han

Key Points

  • The study aims to understand how uncertainties in warning systems affect driver behavior and trust.
  • Conducted a driving-simulator study with 36 participants.
  • Divided participants into two groups based on warning uncertainty levels.
  • Used low and high warning error distributions to test driving behaviors.
  • Analyzed trust levels and safety performance under varying uncertainties.
  • Developed a trust prediction model using demographic and vehicle movement data.
  • Lower warning uncertainty led to higher levels of trust among drivers.
  • Trust was shown to increase over time under low uncertainty and decrease under high uncertainty.
  • Higher warning errors negatively impacted trust and safety performance.
  • The trust prediction model achieved an accuracy of 86.42% using the XGBoost algorithm.

Abstract

Advanced Driver Assistance Systems (ADAS) can greatly enhance road safety by providing real-time warnings to drivers in imminent crash situations. However, the provided warning time may deviate from its designed time. There is limited research on how warning uncertainties influence drivers’ behavior, safety performance, and trust. This paper conducted a driving-simulator study to examine how uncertainties in warnings impact driving behaviors and trust using a roundabout driving scenario. Two warning error distributions were constructed to represent low and high warning uncertainty levels. Thirty-six participants were recruited and randomly divided into two groups under the two uncertainty levels in a driving simulator experiment. The between-group analysis shows that the lower warning uncertainty level group results in higher trust, and that trust increases (or decreases) over time under the low (or high) uncertainty levels. The within-group analysis shows that higher warning errors downgrade drivers’ trust and safety performance when the errors are high. Finally, a personalized trust prediction model was developed using demographic and vehicle movement data, and the XGBoost model achieved the best performance with 86.42% accuracy. 

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Zhang et al. (2026) studied this question.

synapsesocial.com/papers/69a287460a974eb0d3c02e99https://doi.org/10.26599/jicv.2026.9210078
Ask AI
Helpful
Bookmark
Share
View Full Paper