| Summary: | Road detection remains a captivating and crucial aspect of any form of autonomous driving. In this manuscript, we furnish a comprehensive appraisal of recent advancements in road lane detection, a fundamental component integral to autonomous driving. Despite numerous methodologies being proposed to augment accuracy while expediting speed, various hindrances, including lane marking variations, lighting fluctuations, and shadowy conditions, necessitate the establishment of dependable detection systems. Model-based and learning-based methods represent the two predominant techniques for lane detection. Model-based methods afford rapid computation speeds, while learning-based methods extend robustness amidst complexity. This paper delves into the techniques of lane detection and forecasts upcoming trends in the field. Collectively, this review offers a sturdy foundation for prospective research in the realm of road lane detection.
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