Robust Lane Detection by Cascaded Deep Learning Network

Abstract

Despite the recent advances in lane detection models, assuring the correct lane segmentation as output is nearly impossible because a deep learning network is a sort of black-box model. According to National Highway Traffic Safety Administration (NHTSA), traffic accidents caused 36,120 deaths in the U.S in 2019. Given the importance of the lane detection task on the autonomous driving system, the result of lane segmentation must be ensured. To provide the lane detection of high quality, we propose a novel lane segmentation framework by cascading the supervised version of the convolutional recurrent reconstructive network (CRRN) [1] to the existing lane detection network, ENet-SAD [2]. The supervised CRRN utilizes spatio-temporal information to reconstruct the lane segment that is detected by ENet-SAD. Experimental results for the CULane dataset confirm that our proposed framework improves the performance of the conventional lane detection network. Our framework achieves 0.435 of pixel-wise F1 measure, while vanilla ENet-SAD achieves 0.429.