Predicting driver takeover performance in conditionally automated driving. (December 2020)

• Record Type: Journal Article
• Authors: Du, Na; Zhou, Feng; Pulver, Elizabeth M.; Tilbury, Dawn M.; Robert, Lionel P.; Pradhan, Anuj K.; Yang, X. Jessie
• Is Part Of: Accident analysis and prevention. Volume 148(2020)
• Publication Date: 2020-12
• Subjects: Transition of control -- Predictive modeling -- Human–automation interaction -- Human–autonomy interaction -- Human–robot interaction; Accidents -- Prevention -- Periodicals; Accident Prevention -- Periodicals; Accidents -- Prévention -- Périodiques; 363.106
• DOI: 10.1016/j.aap.2020.105748
• Languages: English
• ISSNs: 0001-4575

Abstract

Highlights: We develop a model to predict takeover performance in Level 3 automated driving. The model predicts takeover performance when drivers have an NDRT with varying load. We recommend 3 s as the optimal time window to predict takeover performance We identify important physiological features for takeover performance prediction. 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.