Deep Learning for 3D Lane Detection in Autonomous Driving: A Survey

Xiaoqiang Teng, Zuo Chen, Shunpeng Chen, Zherui Zhang, Shibiao Xu, Zhihao Hao, Deke Guo, Haisheng Li

Abstract: 3D lane detection has become a critical component in the perception task of autonomous vehicles. Unlike 2D lane detection, which operates in the image plane, 3D lane detection estimates the spatial layout of lanes in real-world coordinates, enabling fine-grained localization, map construction, and planning. However, the task remains challenging due to depth ambiguity, sensor limitations, and diverse road conditions. Existing surveys mostly focus on 2D or organize 3D lane detection by sensor modality, lacking a systematic treatment of algorithmic designs. In this paper, we present a comprehensive survey of deep learning-based 3D lane detection methods. We introduce a dual-axis taxonomy that jointly considers modeling paradigms and representation spaces. Based on this framework, we categorize existing methods into four primary paradigms: geometry-based, end-to-end, query-based, and implicit field-based. We analyze how each paradigm interacts with spatial representations such as image, BEV, 3D, and topological spaces. For each category, we review representative frameworks, architectural principles, and performance trade-offs. We also provide an extensive summary of public datasets, evaluation metrics, and state-of-the-art results across multiple benchmarks. Finally, we identify current limitations and outline future research directions toward robust, scalable, and interpretable 3D lane detection.

External IDs:doi:10.1109/jiot.2026.3654005