How Can the Trust-Change Direction be Measured and Identified During Takeover Transitions in Conditionally Automated Driving? Using Physiological Responses and Takeover-Related Factors.

Objective: This paper proposes an objective method to measure and identify trust-change directions during takeover transitions (TTs) in conditionally automated vehicles (AVs).

Background: Takeover requests (TORs) will be recurring events in conditionally automated driving that could undermine trust, and then lead to inappropriate reliance on conditionally AVs, such as misuse and disuse.

Method: 34 drivers engaged in the non-driving-related task were involved in a sequence of takeover events in a driving simulator.

The mediating effect of driver characteristics on risky driving behaviors moderated by gender, and the classification model of driver's driving risk.

High-risk drivers are more likely to be involved in traffic accidents, and the driving risk level of drivers could be affected by many potential factors, such as demographics and personality traits. Based on the Structural Equation Model (SEM), this study involves a sample of 3150 drivers from the Strategic Highway Research Program 2 (SHRP 2), to explore the relationships among drivers' demographic characteristics (gender, age, and cumulative driving years), sensation seeking, risk perception, and risky driving behaviors. More specifically, the mediation model of driver characteristics on risky driving behaviors moderated by gender is constructed by the SEM.