Improved generalizability of CNN based lane detection in challenging weather using adaptive preprocessing parameter tuning

Ensuring the robustness of lane detection is essential for the reliability of autonomous vehicles, particularly in 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 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 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. This preprocessing algorithm is designed to seamlessly integrate with all learning-based lane detection models. When implemented with CNN-based models, the hybrid approach demonstrates commendable generalizability across weather and adaptability to complex lane configurations. Rigorous testing on datasets featuring challenging weather showcases the proposed method's significant improvements over existing models, underscoring its efficacy in addressing persistent challenges associated with lane detection in adverse weather.