A Review of Deep Learning-Based Lane Detection Methods in Complex Environments

Lane detection is pivotal for enhancing the safety and functionality of Advanced Driver Assistance Systems (ADAS) and autonomous driving. Traditional image processing methods, while efficient, struggle in complex environments characterized by occlusions, lighting variations, and road clutter. Deep learning, particularly Convolutional Neural Networks (CNNs), has revolutionized lane detection by enabling automatic feature extraction from raw data, yet challenges persist in handling environmental variability and feature sparsity. This paper comprehensively reviews lane detection methodologies, encompassing both traditional techniques (e.g., Hough transforms, edge detection) and modern deep learning approaches. It emphasizes the critical role of integrating global and local contextual information to improve accuracy in challenging scenarios. Deep learning methods are categorized into three paradigms based on lane representation: segmentation-based, point-based, and parametric-based models. The review further explores how temporal feature fusion (leveraging consecutive video frames) mitigates occlusions and missing features, while spatial feature fusion captures long-range dependencies for holistic scene understanding. Key findings reveal that temporal-spatial fusion significantly enhances robustness, though real-time performance and adaptability to extreme conditions remain limitations. The paper concludes by identifying future research directions, prioritizing efficient architecture for real-time deployment and improved resilience in dynamic, unstructured environments.