@article{ref1,
title="Predicting automated vehicle takeover decision during the nighttime",
journal="Proceedings of the Human Factors and Ergonomic Society annual meeting",
year="2023",
author="Liang, Nade and Lim, Chiho and Yu, Denny and Prakah-Asante, Kwaku O. and Pitts, Brandon J.",
volume="67",
number="1",
pages="914-919",
abstract="Conditionally automated vehicles require drivers to take over control occasionally. To date, takeover performance has been mostly evaluated using only re-engagement time and quality metrics. However, the appropriateness of takeover decisions, which has not been considered by previous research, should also be included as a performance indicator as it reflects one's situation awareness of the takeover scenario. The goal of this study was to use eye-tracking, demographic factors, workload, and non-driving-related task (NDRT) conditions to predict takeover decisions. Forty-three participants drove a simulated conditionally automated vehicle while performing visual NDRTs and needed to decide the most appropriate maneuver around a roadway obstacle. Six classifiers were used to predict takeover decisions. The Random Forest model achieved the best performance, and driving experience and perceived workload were the most influential features. <br><br>FINDINGS may be used to assist in the design of adaptive algorithms that support drivers taking over from automated vehicles.<p /> <p>Language: en</p>",
language="en",
issn="2169-5067",
doi="10.1177/21695067231194993",
url="http://dx.doi.org/10.1177/21695067231194993"
}