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Title: Real-Time Trust Prediction in Conditionally Automated Driving Using Physiological Measures
Trust calibration poses a significant challenge in the interaction between drivers and automated vehicles (AVs) in the context of human-automation collaboration. To effectively calibrate trust, it becomes crucial to accurately measure drivers’ trust levels in real time, allowing for timely interventions or adjustments in the automated driving. One viable approach involves employing machine learning models and physiological measures to model the dynamic changes in trust. This study introduces a technique that leverages machine learning models to predict drivers’ real-time dynamic trust in conditional AVs using physiological measurements. We conducted the study in a driving simulator where participants were requested to take over control from automated driving in three conditions that included a control condition, a false alarm condition, and a miss condition. Each condition had eight takeover requests (TORs) in different scenarios. Drivers’ physiological measures were recorded during the experiment, including galvanic skin response (GSR), heart rate (HR) indices, and eye-tracking metrics. Using five machine learning models, we found that eXtreme Gradient Boosting (XGBoost) performed the best and was able to predict drivers’ trust in real time with an f1-score of 89.1% compared to a baseline model of K -nearest neighbor classifier of 84.5%. Our findings provide good implications on how to design an in-vehicle trust monitoring system to calibrate drivers’ trust to facilitate interaction between the driver and the AV in real time.  more » « less
Award ID(s):
2138274
PAR ID:
10500437
Author(s) / Creator(s):
; ; ;
Publisher / Repository:
IEEE Transactions on Intelligent Transportation Systems
Date Published:
Journal Name:
IEEE Transactions on Intelligent Transportation Systems
Volume:
24
Issue:
12
ISSN:
1524-9050
Page Range / eLocation ID:
14642 to 14650
Subject(s) / Keyword(s):
Trust prediction, physiological measures, real time, machine learning, automated vehicles
Format(s):
Medium: X Other: pdf
Sponsoring Org:
National Science Foundation
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