Presentation
Machine Learning for Prediction of Driver Takeover Time in Automated Driving: Insights from Non-Urgent Low Consequence Scenarios
DescriptionThis work investigates driver takeover times in non-urgent, low consequence scenarios within conditionally automated driving. Using physiological and behavioral data from 46 participants in a driving simulator, classification algorithms were applied to predict metrics of takeover time following a takeover request (TOR). Eye-tracking, heart rate variability, and computer-vision based body posture features were analyzed for their predictive power. The Naïve Bayes algorithm outperformed other models, achieving an accuracy of 78% and an F1-score of 77% when predicting the time to first gaze in the driving scene following a TOR. Results from feature selection showed eye-tracking metrics to have the most predictive power. These results suggest that eye-tracking metrics and simple, computationally efficient, 2-class algorithms may be sufficient for predicting takeover time in non-urgent, low-consequence scenarios. This research provides evidence for integration of physiological sensing into adaptive automated driving systems (ADS) to develop context-aware TOR alert systems to improve road safety.
Contributors
Ph.D. Student
Assistant Professor
Associate Professor
Event Type
Lecture
TimeTuesday, September 10th11:55am - 12:15pm MST
LocationFLW Salon G
Surface Transportation