Lane detection method based on lane structural analysis and CNNs

Abstract

Advanced driving assistance systems (ADASs) play a vital role in the safety of transportation. The detection of lane markings is a very important part of ADASs. For the safety of autonomous vehicles and vehicles driven by human drivers, accurate detection results are necessary. In this study, the authors propose a novel algorithm based on lane structural analysis and convolutional neural networks (CNNs) for lane marking detection. First, a pre-processing stage is used to remove the pavement that constitutes the background of the lane markings. Next, a set of local waveforms from local images is used to generate a region of interest and a CNN classifier is employed to detect lane marking candidates. Finally, a lane geometry analysis stage determines whether or not the candidate is a part of a lane marking. The major contributions of this study can be summarised as follows. First, they propose a novel method to describe a road using waveforms. Second, they analyse the local and global characteristics of the road geometry to detect the lane markings. Third, they provide an effective method to obtain training data for the proposed machine learning method. Experimental results demonstrate that the proposed method outperforms conventional methods.