Detection and Classification of Lanes Using Reannotated Lane Dataset to Aid Autonomous Driving

Abstract

The core functionality of advanced driver assistance systems and self-driving cars depends on the ability to recognize drivable road areas. As there are many different categories of useful markings incorporated within the road area, lane detection and classification form a major step for taking appropriate actions leading to truly autonomous driving. Existing datasets do not provide ample classification of lane types and adequate granularity for precise localization of lane markings. A new dedicated dataset for semantic segmentation of 11 varied lane types obtained by reannotating the BDD100K dataset is presented. The reannotation process involves pixel-level lane markings of 76,000 image instances of the BDD100K dataset, which were originally represented as a sequence of coordinate points. This opens up the possibility of high resolution both spatially and semantically in the context of lane understanding for autonomous driving. Baseline results on the proposed dataset based on the Bilateral Segmentation Network (BiSeNetV2), which considers the spatial data and the categorical semantics distinctly, are presented. The performance is pinned at 85% accuracy during testing using BiSeNetV2 architecture. The dataset is expected to open up new directions of research to address problems such as severe class imbalance, segmentation of multiple classes of texture-less and eccentric features (lanes), and so forth. As a result, applications such as lane-centric activity interpretation, future event prediction, and continuous learning are expected.