Accurate ego-lane recognition utilizing multiple road characteristics in a Bayesian network framework

Accurate lateral localization of an ego-vehicle is one of the core technologies for autonomous driving. Conventional approaches have utilized GPS data, pre-built map information, and lane detection results to estimate the lateral location of an ego-vehicle. However, these approaches demonstrate several performance limitations due to inaccurate data from GPS, high costs for building and maintaining maps, and insufficient visual cues for handling various tasks in diverse driving environments. In this paper, we propose an accurate ego-lane recognition framework that utilizes multiple evidence from visual processing upon the theory of the Bayesian Network to overcome these limitation. We show that more accurate and reliable lateral localization results can be achieved by combining several visual cues, which increases confidence and reliability of the results. We also show that our approach can be applicable to various driving environments without maps because the framework analyzes multiple context information of driving environments simultaneously. We verify the robustness of our algorithm in various driving scenarios such as highways and wide/narrow urban roadways.