Date of Award

Spring 2026

Document Type

Thesis

Degree Name

Master of Science (MS)

Department

Psychology

Committee Director

Yusuke Yamani

Committee Member

Mary Still

Committee Member

Kun Xie

Committee Member

Abby Braitman

Abstract

Advanced technologies such as sensors and AI/ML algorithms have enabled increasing levels of automated driving system that detects, responds, and even predicts changes in a driving environment supported by wireless connectivity to nearby vehicles and infrastructure. Such connected and automated vehicles (CAVs) can be particularly vulnerable to cyberattacks targeting not only infotainment systems but also firmware and other applications, critically compromising driver safety. As we anticipate a “mixed” traffic where vehicles with various levels of automated technologies share the road for the foreseeable future, it is urgent to systematically examine types of possible cyberattacks and control human behaviors in such malicious events to promote road safety. This research program aims to model different types of cyberattack scenarios (behavioral, informational, and system hacking) in a high-fidelity driving simulator, examine psychological and behavioral factors that influence driver responses to cyberattacks in the driving simulator, and develop a training program that specifically enhances their skills to confront cyberattacks during automated driving. Participants were randomly assigned to a placebo or training group, received the respective training program, and their responses to the three types of cyberattack scenarios were evaluated in a driving simulator. Their vehicle control performance and eye movements were measured to examine the effectiveness of the training program. Relative to the placebo program, drivers trained using the 3M training method displayed more frequent attention to critical cyberattack-relevant information and exhibited more stable vehicle control, suggesting improved situation awareness and information processing. These results support the effectiveness of the 3M training method in helping drivers recognize and respond to automated vehicle cyberattacks and suggest potential transferability of the method across domains.

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DOI

https://doi.org/10.25777/rh6a-9n41

ISBN

9798197809445

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