Abstract
Conditional autonomous driving requires the description of sufficient time reserves for drivers in take-over situations. The definition of this time reserve has not been addressed for the truck context thus far. Through the observation of physiological measures, the possibility of estimating reaction times is considered. Driver data is collected with a remote eye-tracker and body posture camera. Empirical data from a simulator study is utilized to train and compare four machine learning algorithms and generate driver features. The estimation of take-over times is defined as a classification problem with four reaction time classes, leading to a misclassification rate of a linear support vector machine (SVM) of 38.7%. Utility of driver features for reaction time estimation are discussed.
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Lotz, A., Weissenberger, S. (2019). Predicting Take-Over Times of Truck Drivers in Conditional Autonomous Driving. In: Stanton, N. (eds) Advances in Human Aspects of Transportation. AHFE 2018. Advances in Intelligent Systems and Computing, vol 786. Springer, Cham. https://doi.org/10.1007/978-3-319-93885-1_30
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DOI: https://doi.org/10.1007/978-3-319-93885-1_30
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