Developing, Analyzing, and Evaluating Self-Drive Algorithms Using Electric Vehicles on a Test Course

Reliable lane-following is one of the most important tasks for an automated vehicle or ADAS. The intent of this project was to design and evaluate multiple lane-following algorithms for an automated vehicle using computer vision. The implemented algorithms' performance was then evaluated on a testing course and compared with a human driver. ROS and OpenCV were used to detect and follow lanes on the road. A street-legal vehicle with a high-definition camera and drive-by-wire system was used to implement and evaluate driving data. Each algorithm was evaluated based on time for completion, speed limit infractions, and lane positioning infractions. The recorded evaluation data determined the most reliable lane-following algorithm. All of our algorithms had a success rate of at least 60% on certain lanes of the testing course.