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Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research
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Designing for the Extremes: Modeling Drivers' Response Time to Take Back Control From Automation Using Bayesian
Azadeh DinparastDjadid1, John D Lee1, Joshua Domeyer1,2
15228 University of Wisconsin-Madison, USA.
Human Factors
|December 25, 2019
Summary
Analyzing driver response time in automated vehicles is crucial. Focusing on the 85th percentile, rather than the average, better reveals risks associated with takeover requests and system performance.
Area of Science:
- Human-Computer Interaction
- Automotive Engineering
- Cognitive Psychology
Background:
- Driver response time to takeover requests is a key metric for automated vehicle safety.
- Existing studies predominantly focus on mean response time, potentially overlooking critical performance aspects.
Purpose of the Study:
- To investigate how takeover request timing, event type, and visual demand influence driver response time.
- To evaluate the impact of these factors on different quantiles of the response time distribution, particularly the 85th percentile.
Main Methods:
- An advanced driving simulator was used to collect data on driver responses.
- Bayesian quantile regression was employed to model the effects of various factors on response time distributions.
Main Results:
- Takeover request timing, event type, and visual demand significantly affect driver response time.
- These factors demonstrated differential impacts on the 85th percentile compared to the median response time.
- The revealed stopped vehicle event, longer time budgets, and lower visual demand were particularly influential at the 85th percentile.
Conclusions:
- Analyzing only the mean response time can misrepresent automated system performance.
- The 85th percentile is a critical performance metric, highlighting factors contributing to delayed responses and individual driver differences.
- Considering upper quantiles is essential for designing safer vehicle automation systems.
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