Zitat
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.
Abstract
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.
Referenzen und Relationen
DOI 10.1109/CSITSS67709.2025.11294628