Electronic Thesis and Dissertation Repository

Thesis Format

Integrated Article

Degree

Doctor of Philosophy

Program

Computer Science

Supervisor

Dr. Michael A. Bauer

Abstract

With over 90\% of car crashes attributed to human errors, the focus on drivers as active participants in Advanced Driving Assistance Systems (ADAS) feedback mechanisms becomes crucial. The primary focus of our research is to model driver behavior for ADAS by utilizing gaze-based information in correlation with other vehicle sensor data with the aim of predicting driver maneuvers and assessing the utility of various types of information toward applying such models in driver assistance systems. The research utilized an instrumented vehicle equipped with diverse sensors, emphasizing the importance of calibrating these sensors to establish a common reference frame. The research focuses on three key objectives: mapping the driver's gaze to other sensors, creating a dataset of driving maneuvers and employing driver gaze and vehicle sensor data to develop predictive models. The methodology involves implementing robust cross-calibration algorithms to map the driver's gaze to the 3D vehicle surroundings. We generated a dataset of driving maneuvers by utilizing the instrumented vehicle to collect real driving data from different drivers, aligning different sensor outputs in spatio-temporal correspondence. This dataset serves as the foundation for developing predictive models of driver maneuvers. The study then employs two deep neural network models, Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), to effectively handle temporal data and assess the impact of the driver's gaze in correlation with vehicle dynamics to predict the next driver's maneuvers. Our experimental results underscore the effectiveness of the proposed approach, showing that leveraging the driver's gaze data in correlation with vehicle dynamics data can improve prediction performance. The proposed approaches and findings in this thesis pave the way for the development of intelligent co-driver systems for on-road vehicles, ultimately leading to safer and more efficient transportation systems.

Summary for Lay Audience

Have you ever wondered how self-driving cars or Advanced Driver Assistance Systems (ADAS) can predict what a driver will do next? Imagine if your car could predict your maneuvers and provide assistance to keep you safe on the road. That's exactly what our research aims to achieve! Our study focuses on using information about where the driver is looking (their gaze) along with other data to predict what they will do next while driving. To do this, we used an instrumented vehicle with different sensors to gather real driving data from different people. We then created a dataset of driving data, which served as the basis for our predictive models. We used advanced computer algorithms called deep neural networks to analyze this data. These algorithms can learn patterns and make predictions based on the information they receive. By training these algorithms with our dataset, we were able to teach the computer to understand a driver's behavior to predict their actions behind the wheel. Our research is a step towards creating vehicles that can collaborate with us to make driving safer and more comfortable. In the future, vehicles equipped with these intelligent systems could potentially prevent accidents by alerting the driver or even taking proactive measures to avoid danger.

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Available for download on Wednesday, December 31, 2025

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