Quote
N. Kruthika, M. N. Chandana, B. K. Kavyashree, S. Manonmani, K. Gajalakshmi, and J. Jakob, "Advances in Lane Detection: From Classical Methods to Transformer-Based Architectures," in 2025 9th International Conference on Computational System and Information Technology for Sustainable Solutions, 2025.
Content
Lane detection is a critical component of Advanced Driver Assistance Systems (ADAS) and autonomous navigation, especially in unstructured environments where lane markings are often degraded or missing. Ever-increasing driving complexity has shifted research from traditional image processing to deep learning approaches. This paper reviews state-of-the-art lane detection frameworks, tracing their evolution from classical geometric models (e.g., Hough Transform) to convolutional neural networks (CNNs), anchor-based models, and transformer-based architectures, with a focus on unstructured roads. The review covers traditional vision-based approaches, deep learning methods such as semantic segmentation, anchor-based models, and transformer architectures, while addressing challenges like illumination changes, inconsistent markings, and environmental noise. Applications range from commercial ADAS to research prototypes and simulation platforms.
References
DOI 10.1109/CSITSS67709.2025.11294628