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Citation
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HERO ID
7223222
Reference Type
Journal Article
Title
Predicting driver takeover performance in conditionally automated driving
Author(s)
Du, N; Zhou, F; Pulver, EM; Tilbury, DM; Robert, LP; Pradhan, AK; Yang, XJ; ,
Year
2020
Is Peer Reviewed?
Yes
Journal
Accident Analysis and Prevention
ISSN:
0001-4575
Language
English
PMID
33099127
DOI
10.1016/j.aap.2020.105748
Abstract
In conditionally automated driving, drivers have difficulty taking over control when requested. To address this challenge, we aimed to predict drivers' takeover performance before the issue of a takeover request (TOR) by analyzing drivers' physiological data and external environment data. We used data sets from two human-in-the-loop experiments, wherein drivers engaged in non-driving-related tasks (NDRTs) were requested to take over control from automated driving in various situations. Drivers' physiological data included heart rate indices, galvanic skin response indices, and eye-tracking metrics. Driving environment data included scenario type, traffic density, and TOR lead time. Drivers' takeover performance was categorized as good or bad according to their driving behaviors during the transition period and was treated as the ground truth. Using six machine learning methods, we found that the random forest classifier performed the best and was able to predict drivers' takeover performance when they were engaged in NDRTs with different levels of cognitive load. We recommended 3 s as the optimal time window to predict takeover performance using the random forest classifier, with an accuracy of 84.3% and an F1-score of 64.0%. Our findings have implications for the algorithm development of driver state detection and the design of adaptive in-vehicle alert systems in conditionally automated driving.
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