Date of Award:

5-2017

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Electrical and Computer Engineering

Committee Chair(s)

Rajnikant Sharma

Committee

Rajnikant Sharma

Committee

Donald Cripps

Committee

Xiaojun Qi

Abstract

Autonomous driving is an area of increasing investment for researchers and auto manufacturers. Integration has already begun for self-driving cars in urban environments. An essential aspect of navigation in these areas is the ability to sense and follow lane markers. This thesis focuses on the development of a vision-based control platform using lane detection to control a full-sized electric vehicle with only a monocular camera. An open-source, integrated solution is presented for automation of a stock vehicle. Aspects of reverse engineering, system identification, and low-level control of the vehicle are discussed. This work also details methods for lane detection and the design of a non-linear vision-based control strategy.

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