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Vision-Based Road Conditions Alerts Systems in Connected Vehicle Environment for Accident-Prone Roads
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- Author / Creator
- Cui, Haibo
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Work zones, being a critical component of roadway transportation systems, can benefit greatly from computer vision-enabled roadway infrastructures, specifically in connected vehicle (CV) environments. Connected infrastructures, such as roadside units (RSU) and on-board units (OBU), can greatly improve the environmental awareness and safety of CVs driving through a work zone. In this regard, the contribution of this thesis lies in developing a vision-based approach to generate work zone safety messages in real-time, utilizing video streams from roadside monocular traffic cameras that can be used by CV work zone safety apps on mobile devices to reliably navigate through a work zone. A monocular traffic camera calibration method is proposed to establish an accurate mapping between the image plane and Global Position System (GPS) space. Real test scenarios show that our algorithm can precisely and effectively locate work zone boundaries from a monocular traffic camera in real-time. We demonstrate the capabilities and features of our system through real-world experiments where the driver cannot see the work zone. End-to-end latency analysis reveals that the vision-based work zone safety warning system satisfies the active safety latency requirements. This vision-based work zone safety alert system ensures the safety of both the worker and the driver in a CV environment. Winter roads that are covered by snow or ice, as seen in Alberta, can cause severe traffic accidents. Current winter road surface conditions (RSC) monitoring methods often generate incomplete RSC maps in city center areas. Cameras mounted on CVs and traffic cameras can be used as sensors to detect RSC. In this case, the contribution of this thesis focuses on developing automated RSC classification applications using CVs and traffic cameras in Alberta. Three state-of-the-art machine learning algorithms are trained and tested on RSC datasets. The pipeline of automated RSC classification applications in a CV environment is proposed. Comparisons of our methods versus current methods in real-world scenarios reveal our method can provide more detailed RSC maps in city center areas and narrow roads. Our RSC methods ensure the safety of drivers on winter roads.
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- Subjects / Keywords
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- Graduation date
- Spring 2023
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- Type of Item
- Thesis
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- Degree
- Master of Science
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- License
- This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.