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
