A Framework for Camera-Based Real-Time Lane and Road Surface Marking Detection and Recognition

Abstract

We propose a novel real-time integrated unsupervised learning framework for lane detection, tracking, and road surface marking detection and recognition using live feed from a camera mounted on the dashboard of a moving vehicle. Accurate and automated lane and road surface marking detection and recognition from cameras play an important role in intelligent vehicles. It helps increase driving safety in advanced driver assistant systems and enhance global localization and path planning in autonomous driving vehicles. Map building and updation can also benefit from automated detection of lanes and road surface markings. In our framework, we propose a spatio-temporal incremental clustering algorithm coupled with curve-fitting for simultaneously detecting lanes and road surface markings. The clusters that do not represent the lanes are further processed using Grassmann manifold learning to accurately recognize the road surface markings. Our framework is independent of the number and type of lanes, be it continuous, non-continuous, straight, or curved. Moreover, the framework works for all types, sizes, and orientations of road surface markings. It is only limited by the type of road surface markings present in the training data. We have measured the performance of our framework on state-of-the-art datasets.