Lane detection algorithm (LDA) is a crucial and necessary component for autonomous vehicles to ensure safe driving in various environments. Deep learning-based lane detection algorithms (DL-LDAs) have gained significant attention recently, and there have been a number of DL-LDAs, introduced in the literature, showing a continuous performance improvement in lane detection. In general, DL-LDAs are composed of pre-processing, lane feature extraction, lane detection head, and an optional lane fitting. For a systematic overview of various DL-LDAs, we provide detailed explanations for each functional component of DL-LDAs. Moreover, this paper presents the first survey to comprehensively analyze various DL-LDAs using camera and LiDAR, such as 2D (2-Dimensional) and 3D DL-LDAs using camera images and DL-LDAs using LiDAR point cloud or sensor fusion. In addition to the analysis, we present recent public lane detection benchmarks for DL-LDAs and discussions concerning technical issues that need to be addressed in future DL-LDA studies.