Real-Time Lane Detection for Autonomous Driving

Engineering notes

We further survey the benchmark datasets and evaluation metrics that shape how the community measures progress, and critically assess persistent challenges including adverse weather, occlusion, non-standard lane markings, and the sim-to-real gap.

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

Lane detection stands as a foundational capability for modern autonomous vehicles and advanced driver assistance systems (ADAS). Despite decades of progress, the problem is far from solved — road environments remain unpredictable, lane markings vary dramatically across geographies, and real-time constraints impose strict computational budgets on embedded hardware. This paper offers a structured review of the state of the art in deep learning-based lane detection, tracing the field from early convolutional segmentation approaches through the latest transformer-based architectures. We examine the progression from pixel-level classification frameworks to anchor-driven regression models, polynomial curve fitting strategies, and end-to-end detection transformers. We further survey the benchmark datasets and evaluation metrics that shape how the community measures progress, and critically assess persistent challenges including adverse weather, occlusion, non-standard lane markings, and the sim-to-real gap. Drawing on these findings, we identify directions that are likely to define the next generation of lane detection systems, including multi-task learning, domain-adaptive training, 3D lane reconstruction, and lightweight deployment on embedded automotive platforms.

Links and sources

• Official / arXiv page

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