A Comparative Study of Deep Learning based Lane Detection Methods

Abstract

Lane detection is the most common application for detecting lane boundaries in autonomous vehicles intelligent driving systems. Lane detection performance has a significant impact on autonomous vehicle driving systems. Lane detection helps with both vehicle positioning and lane departure. However, lane detection is still a problem that needs to be solved entirely for self-driving cars. These strategies must be not only effective but also be efficient. Due to improvements in computer vision and deep learning-based technologies, lane detection currently achieves better results in the accuracy of lane detection. However, accurate lane detection is still the most challenging task in difficult conditions such as poor light, unclear lanes, and occlusions. The results of recent research on lane detection systems are presented in this review paper. First, we discussed the history of traditional and deep learning-based lane detection methods. Then we have discussed the importance of loss function in lane detection. Second, we have compared the experimental results of each technique for deep learning and state-of-the-art methods. Third, the summarized list of existing datasets for lane detection, performance evaluation criteria, and lane detection based on deep learning methods are discussed. Finally, we looked into some of the current issues of deep learning algorithms.