Road Lane Detection and Classification in Urban and Suburban Areas based on CNNs

Abstract

Road lane detection systems play a crucial role in the context of Advanced Driver Assistance Systems (ADASs) and autonomous driving. Such systems can lessen road accidents and increase driving safety by alerting the driver in risky traffic situations. Additionally, the detection of ego lanes with their left and right boundaries along with the recognition of their types is of great importance as they provide contextual information. Lane detection is a challenging problem since road conditions and illumination vary while driving. In this contribution, we investigate the use of a CNN-based regression method for detecting ego lane boundaries. After the lane detection stage, following a projective transformation, the classification stage is performed with a RseNet101 network to verify the detected lanes or a possible road boundary. We applied our framework to real images collected during drives in an urban area with the RoadLAB instrumented vehicle. Our experimental results show that our approach achieved promising results in the detection stage with an accuracy of 94.52% in the lane classification stage.