Electronic Thesis and Dissertation Repository

Thesis Format

Integrated Article

Degree

Master of Engineering Science

Program

Electrical and Computer Engineering

Collaborative Specialization

Artificial Intelligence

Supervisor

Nikan, Soodeh

2nd Supervisor

Zaki, Mohamed H.

Abstract

Autonomous vehicles (AVs) are poised to transform the transportation landscape, offering improved safety, efficiency, and convenience. However, achieving these benefits hinges on addressing the complexities of human-vehicle interaction, particularly during transitional phases where control shifts between the vehicle and the driver. This thesis investigates these critical dynamics through three interconnected studies. The first study provides a comprehensive review of Takeover Requests (TORs) in Level 3 AVs, emphasizing the importance of human-centred design in ensuring safe and efficient transitions. It introduces a framework that integrates environmental monitoring, driver state assessment, and ergonomic considerations to optimize handovers. The second study explores the influence of intersections on driver stress by analyzing heart rate (HR) data. Findings reveal that intersections could significantly heighten driver cognitive load, underscoring the need for AV systems and urban infrastructure to address these stressors. The third study leverages physiological signals, including electrocardiogram (ECG), electrodermal activity (EDA), and respiration (RSP), to model takeover quality using machine learning. The study demonstrates that these signals are robust predictors of driver readiness and performance during TORs, offering a pathway toward adaptive and safer AV systems. Together, these studies advance the understanding of human factors in autonomous driving, highlighting the critical role of physiological responses in designing intuitive, human-centric AV systems. This work contributes to the development of safer and more efficient mobility solutions, bridging the gap between technological innovation and human acceptance.

Summary for Lay Audience

Self-driving vehicles promise a safer, more efficient future of transportation. However, they’re not yet capable of driving entirely on their own. In critical moments—like during poor weather or at complex intersections—human drivers must take back control of the vehicle. My research explores how to improve this collaboration between people and technology, focusing on how drivers respond during these transitions and what can be done to make them safer and smoother. The goal of my research was to understand the human side of automated driving. When a car asks the driver to take over, the quality of that response depends on factors like the driver’s stress level, focus, and ability to react quickly. By studying physiological signals such as heart rate, skin response, and breathing, I developed methods to assess a driver’s readiness in real-time, allowing AV systems to adapt their behavior, making handoffs safer and reducing the risk of accidents. My research also examines the environments in which these transitions happen. For example, intersections are some of the most stressful parts of driving, requiring quick decision-making and sharp focus. Understanding how these scenarios impact drivers’ physiological and cognitive states helps identify ways to design better AV systems and road infrastructure. Together, my research aimed to address a bigger picture: how to make automated vehicles work for people. By bridging insights from human behavior, physiological data, and advanced technology, my research aimed to build trust and safety into the next generation of vehicles. Ultimately, I wanted to ensure that autonomous driving isn’t just about machines—it’s about creating systems that understand and support the humans at the wheel. This approach brings us closer to a future where autonomous vehicles can coexist seamlessly with the people who use them.

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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Available for download on Friday, January 09, 2026

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