A Review of Convolutional Neural Network-Based Automatic Lane Detection Methods on the TuSimple Dataset

Abstract

This review explores and compares various automatic lane detection methods, shedding light on their strengths, weaknesses, and advancements. The study analyzes a diverse range of techniques, including model-based and deep learning-based approaches, employed in road lane detection. The review highlights the advantages and disadvantages of each method, providing a nuanced understanding of their performance metrics, accuracy, and applicability under different scenarios. It dives into the evolution of lane detection algorithms, emphasizing recent breakthroughs in the field. The comparison section systematically evaluates the effectiveness of these methods, considering factors such as computational efficiency, robustness in challenging conditions, and adaptability to diverse environments. It aims to guide researchers, practitioners, and developers in choosing suitable lane detection methods based on specific use cases and requirements. Ultimately, this review contributes to the ongoing discourse in the area of autonomous driving, intelligent transportation systems, and computer vision, offering valuable insights for the continuous improvement of automatic lane detection technologies.