  Ensuring the robustness of lane detection systems is essential for the
reliability of autonomous vehicles, particularly in the face of diverse weather
conditions. While numerous algorithms have been proposed, addressing challenges
posed by varying weather remains an ongoing issue. Geometric-based lane
detection methods, rooted in the inherent properties of road geometry, provide
enhanced generalizability. However, these methods often require manual
parameter tuning to accommodate it fluctuating illumination and weather
conditions. Conversely, learning-based approaches, trained on pre-labeled
datasets, excel in localizing intricate and curved lane configurations but
grapple with the absence of diverse weather datasets. This paper introduces a
promising hybrid approach that merges the strengths of both methodologies. A
novel adaptive preprocessing method is proposed in this work. Utilizing a fuzzy
inference system (FIS), the algorithm dynamically adjusts parameters in
geometric-based image processing functions and enhances adaptability to diverse
weather conditions. Notably, this preprocessing algorithm is designed to
seamlessly integrate with all learning-based lane detection models. When
implemented in conjunction with CNN-based models, the hybrid approach
demonstrates commendable generalizability across weather conditions and
adaptability to complex lane configurations. Rigorous testing on datasets
featuring challenging weather conditions showcases the proposed method's
significant improvements over existing models, underscoring its efficacy in
addressing the persistent challenges associated with lane detection in adverse
weather scenarios.
