Autonomous vehicles need to detect road lanes to navigate safely. However, recognizing lanes in real-world conditions, such as on a university campus, can be challenging due to poor lighting, faded markings, and obstacles. This project aims to develop a lane detection system using computer vision and AI. We will test two approaches: traditional methods (which use simple image processing) and AI-based methods (which learn patterns from road images). Students will collect images of campus roads, apply different detection techniques, and compare their accuracy. The goal is to improve lane recognition for safer autonomous driving in small, structured environments like university campuses. This research will help students learn programming, image processing, and AI techniques while working on a real-world problem in smart transportation.