A FIVE-ERA TAXONOMY AND BENCHMARK FRAMEWORK FOR LANE DETECTION: FROM CLASSICAL HEURISTICS TO VISION FOUNDATION MODELS IN AUTONOMOUS DRIVING

Engineering notes

Key topics: autonomous driving, lane detection, foundation model, perception. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

This survey paper traces the technological history of lane detection systems over the last quarter century, discussing paradigm shifts from classical computer vision methods to modern foundation models. The evolution is divided into five eras: Classical Vision-based (2000–2010), Feature + Geometry (2006–2014), CNN Segmentation (2015–2019), Anchor/Curve-based and Transformer Methods (2020–2022), and the Foundation Model generation (2023–present). Each phase is discussed based on methodological developments, pivotal contributions, performance attributes, and shortcomings. The survey synthesizes the original literature, showing how machine learning, deep learning, and scale-based pre-training have tackled robustness, generalization and real-time issues. We identify research gaps in edge cases, system integration, and suggest future directions towards cohesive perception models achieving optimal accuracy, efficiency, and interpretability.

Links and sources

• Official / arXiv page

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