# Monocular 3D lane detection for Autonomous Driving: Recent Achievements, Challenges, and Outlooks arXiv: https://arxiv.org/abs/2404.06860 ยท HTML: https://mlsanity.com/paper/2404.06860/ ## Key facts - Cited by 9 papers in the map (data through 14 Oct 2026). - Builds on 85 of its 132 references that are in the map. - First posted on arXiv 10 Apr 2024. ## Builds on (most cited first, 30 of 85) - [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015) - [Flamingo: a Visual Language Model for Few-Shot Learning](https://mlsanity.com/paper/2204.14198/) (2022) - [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016) - [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017) - [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018) - [Scalability in Perception for Autonomous Driving: Waymo Open Dataset](https://mlsanity.com/paper/1912.04838/) (2019) - [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017) - [Visual Prompt Tuning](https://mlsanity.com/paper/2203.12119/) (2022) - [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019) - [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019) - [Event-based Vision: A Survey](https://mlsanity.com/paper/1904.08405/) (2019) - [A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges](https://mlsanity.com/paper/2011.06225/) (2020) - [OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework](https://mlsanity.com/paper/2202.03052/) (2022) - [Evidential Deep Learning to Quantify Classification Uncertainty](https://mlsanity.com/paper/1806.01768/) (2018) - [EVA: Exploring the Limits of Masked Visual Representation Learning at Scale](https://mlsanity.com/paper/2211.07636/) (2022) - [ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks](https://mlsanity.com/paper/1910.03151/) (2019) - [Vision Transformer Adapter for Dense Predictions](https://mlsanity.com/paper/2205.08534/) (2022) - [A Survey of Deep Active Learning](https://mlsanity.com/paper/2009.00236/) (2020) - [GIT: A Generative Image-to-text Transformer for Vision and Language](https://mlsanity.com/paper/2205.14100/) (2022) - [Image as a Foreign Language: BEiT Pretraining for All Vision and Vision-Language Tasks](https://mlsanity.com/paper/2208.10442/) (2022) - [InternImage: Exploring Large-Scale Vision Foundation Models with Deformable Convolutions](https://mlsanity.com/paper/2211.05778/) (2022) - [Images Speak in Images: A Generalist Painter for In-Context Visual Learning](https://mlsanity.com/paper/2212.02499/) (2022) - [Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges](https://mlsanity.com/paper/1902.07830/) (2019) - [One Million Scenes for Autonomous Driving: ONCE Dataset](https://mlsanity.com/paper/2106.11037/) (2021) - [Multi-Task Multi-Sensor Fusion for 3D Object Detection](https://mlsanity.com/paper/2012.12397/) (2020) - [PandaSet: Advanced Sensor Suite Dataset for Autonomous Driving](https://mlsanity.com/paper/2112.12610/) (2021) - [Spatial As Deep: Spatial CNN for Traffic Scene Understanding](https://mlsanity.com/paper/1712.06080/) (2017) - [CPT: Colorful Prompt Tuning for Pre-trained Vision-Language Models](https://mlsanity.com/paper/2109.11797/) (2021) - [Unified Vision and Language Prompt Learning](https://mlsanity.com/paper/2210.07225/) (2022) - [PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark](https://mlsanity.com/paper/2203.11089/) (2022) ## Built on by (most cited first, 9 of 9) - [Monocular Lane Detection Based on Deep Learning: A Survey](https://mlsanity.com/paper/2411.16316/) (2024) - [CLRKDNet: Speeding up Lane Detection with Knowledge Distillation](https://mlsanity.com/paper/2405.12503/) (2024) - [Rethinking Lanes and Points in Complex Scenarios for Monocular 3D Lane Detection](https://mlsanity.com/paper/2503.06237/) (2025) - [Annotation-Free Detection of Drivable Areas and Curbs Leveraging LiDAR Point Cloud Maps](https://mlsanity.com/paper/2603.27553/) (2026) - [Erase, then Redraw: A Novel Data Augmentation Approach for Free Space Detection Using Diffusion Model](https://mlsanity.com/paper/2409.20164/) (2024) - [Depth3DLane: Monocular 3D Lane Detection via Depth Prior Distillation](https://mlsanity.com/paper/2504.18325/) (2025) - [Breaking Down Monocular Ambiguity: Exploiting Temporal Evolution for 3D Lane Detection](https://mlsanity.com/paper/2504.20525/) (2025) - [Depth3DLane: Fusing Monocular 3D Lane Detection with Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/2507.13857/) (2025) - [3D Lane Detection with Odometry for High-Speed Vehicle Racing](https://mlsanity.com/paper/2607.14248/) (2026) ## Abstract (arXiv) 3D lane detection is essential in autonomous driving as it extracts structural and traffic information from the road in three-dimensional space, aiding self-driving cars in logical, safe, and comfortable path planning and motion control. Given the cost of sensors and the advantages of visual data in color information, 3D lane detection based on monocular vision is an important research direction in the realm of autonomous driving, increasingly gaining attention in both industry and academia. Regrettably, recent advancements in visual perception seem inadequate for the development of fully reliable 3D lane detection algorithms, which also hampers the progress of vision-based fully autonomous vehicles. We believe that there is still considerable room for improvement in 3D lane detection algorithms for autonomous vehicles using visual sensors, and significant enhancements are needed. This review looks back and analyzes the current state of achievements in the field of 3D lane detection research. It covers all current monocular-based 3D lane detection processes, discusses the performance of these cutting-edge algorithms, analyzes the time complexity of various algorithms, and highlights the main achievements and limitations of ongoing research efforts. The survey also includes a comprehensive discussion of available 3D lane detection datasets and the challenges that researchers face but have not yet resolved. Finally, our work outlines future research directions and invites researchers and practitioners to join this exciting field.