Feature-Based Lane Detection Algorithms for Track Following: A Comparative Study

Abstract

Autonomous driving has been gaining momentum in recent years and is today one of the hottest areas of research and development in the mobility sector. One of the basic tasks to cover in the field of autonomous driving is lane detection. Considering that lane keeping and controlled lane change are low level autonomy tasks, those tasks are essential to any project aiming to achieve a reasonable level of autonomous driving. As a students’ playground, the University of Applied Sciences Upper Austria - along with its partners - is currently establishing a model car based future mobility race event. To make this happen, a ROS based model car is equipped with various known and newly to be developed algorithms enabling certain capabilities. Given the described topical context, in this paper two feature-based lane detection algorithms, namely Hough Line Transform algorithm and Sliding Window algorithm, are developed, tested and compared.