Dr. Shuchi Deb of the Industrial, Manufacturing, and Systems Engineering (IMSE) Department has received a grant from the Center for Transportation, Equity, Decisions and Dollars (CTEDD), a USDOT (Tier-1) University Transportation Center, supported by the United States Department of Transportation. The project focuses on driver fatigue and their readiness for prolonged and automated driving. The objective is to develop a driver readiness monitoring system to improve drivers’ takeover performance using driving simulator studies for both consumer and commercial drivers. The grant provides a total funding of $149,987 for one year and supports five untenured and two tenured faculties (PI: Dr. Shuchi Deb, co-PIs: Dr. Anurag Pande and Dr. David A. Noyce), three graduate students, and two undergraduate students over the project period. The project has started on June 1, 2022.
Vehicle automation technology is being designed to handle driving tasks for human drivers. However, this technology is not expected to handle all possible driving conditions successfully in the foreseeable future. The system can fail anytime and may require drivers to take over control within a short period of time. Additionally, automation can induce boredom, daydreaming, and drowsiness due to driver inactivity and can worsen driver readiness to take over control of the vehicle. The researchers claim that Driver readiness can be measured using drivers’ postural data, gaze behaviors, and emotional expressions. Specific thresholds for these measures can be used to alert drivers to be ready to take over from automated driving or avert them from driving, when necessary. The objectives are to identify effective measures to define driver readiness and assess the association between divers’ takeover performance and their readiness measures. Researchers will measure driver readiness using postural data, gaze and head orientations, and emotional status from video recordings; drivers’ subjective responses on readiness and situational awareness; and their takeover performance using reaction time, collision rate, and driving behavior. Different machine learning algorithms will be applied for feature extraction, feature selection, and classification and prediction models development for readiness monitoring. The rationales for this research are to inform policy makers about an effective driver-readiness monitoring system for prolonged automated driving; provide safer driving conditions and address equity during prolonged driving for older adults, and occupational drivers (Uber, taxi, or city transportation) driving long hours; and enhance educational and research infrastructures combining human-computer interactions and machine learning at a Hispanic-Serving Institution. Stakeholder involvement from the city, state, and automobile manufacturer will strengthen our tech-transfer and project outcome dissemination.