Lane classification is a fundamental problem for autonomous driving and map-aided localization. Many existing algorithms rely on special designed 1D or 2D filters to extract features of lane markings from either color images or LiDAR data. However, these handcrafted features could not be robust under various driving and lighting conditions.   In this paper, we propose a novel algorithm to fuse color images and LiDAR data together. Our algorithm consists of two stages. In the first stage, we segment road surfaces and register LiDAR data with the corresponding color images. In the second stage, we train convolutional neural networks (CNNs) to classify image patches into lane markings and non-markings. Comparing with the algorithms based on handcrafted features, our algorithm learns a set of kernels to extract and integrate features from two different modalities. The pixel-level classification rate in our experiments shows that our algorithm is robust to different conditions such as shadows and occlusions.
['Xiaodong Gu', 'Andi Zang', 'Xinyu Huang', 'Alade O. Tokuta', 'Xin Chen']
Fusion of color images and LiDAR data for lane classification