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How Do Drivers Respond to Silent Automation Failures? Driving Simulator Study and Comparison of Computational Driver

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Researchers developed computational models to predict driver brake reaction times (BRTs) during adaptive cruise control (ACC) failures. The looming prediction model accurately forecasts BRTs, aiding in assessing automated driving safety.

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Area of Science:

  • Human-Computer Interaction
  • Automotive Safety Engineering
  • Cognitive Psychology

Background:

  • Validated computational models for predicting driver brake reaction times (BRTs) to silent failures of automated driving systems are currently lacking.
  • Assessing the safety benefits of automated driving necessitates reliable models for driver responses during system failures.

Purpose of the Study:

  • To describe and test novel computational driver models for predicting BRTs.
  • To evaluate driver responses to lead vehicle braking under cruise control (CC) and silent adaptive cruise control (ACC) failures.

Main Methods:

  • Proposed two alternative models: a looming prediction model and a lower gain model.
  • Tested model predictions using a driving simulator study with varying kinematic criticality.

Main Results:

  • BRTs significantly decreased with increased kinematic criticality for both CC and ACC driving.
  • BRTs were significantly longer during ACC driving compared to CC driving.
  • Models were fitted to observed data as initial predictions exceeded actual BRTs.

Conclusions:

  • Both the looming prediction model and the lower gain model effectively predict BRTs during ACC driving.
  • The looming prediction model offers an advantage by predicting average BRTs with parameters consistent with CC driving data.
  • Findings contribute to the assessment of safety benefits associated with automated driving technologies.