Monocular multi-kernel based lane marking detection

Abstract

Lane marking detection provides key information for scene understanding in structured environments. Such information has been widely exploited in Advanced Driving Assistance Systems and Autonomous Vehicle applications. This paper presents an enhanced lane marking detection approach intended for low-level perception. It relies on a multi-kernel detection framework with hierarchical weights. First, the detection strategy performs in Bird's Eye View (BEV) space and starts with an image filtering using a cell-based blob method. Then, lane marking parameters are optimized following a parabolic model. Finally, a self-assessment process provides an integrity indicator to improve the output performance of detection results. An evaluation using images from a public dataset confirms the effectiveness of the method.