Autonomous Driving Roadway Feature Interpretation Using Integrated Semantic Analysis and Domain Adaptation

Abstract

Lane detection is fundamental to autonomous driving, yet remains challenging in complex environments with occlusions, ambiguous markings, and varied lighting. We introduce the Global Semantic Enhancement Network (GSENet), a groundbreaking framework that significantly advances lane detection accuracy and robustness. GSENet’s core innovations include the Global feature Extraction Module (GEM) and the Top Layer Auxiliary Module (TLAM). GEM revolutionizes the extraction of fine-grained global features, overcoming limitations of traditional deep convolutional approaches without compromising inference speed. TLAM leverages self-attention mechanisms to capture rich contextual information and learn task-specific representations, dramatically enhancing the network’s performance in complex scenarios. We further propose the Generalized Line Intersection over Union (GLIoU) Loss, a novel optimization approach that considers spatial relationships between lane points and introduces a geometric penalty term. This loss function promotes globally coherent and smooth lane predictions, addressing key limitations in existing methods. Our comprehensive mathematical analyses, including gradient derivations and complexity assessments, provide theoretical foundations for the effectiveness of these innovations. Extensive experiments on challenging benchmarks demonstrate GSENet’s superior accuracy and robustness, significantly outperforming state-of-the-art methods. Notably, our framework’s modular design extends its applicability beyond lane detection to various computer vision tasks involving elongated or curved structures, opening new avenues for research and practical applications in autonomous systems and beyond.

Keywords

• Autonomous driving
• domain adaption optimization
• object detection application