# LaneAF: Robust Multi-Lane Detection with Affinity Fields

arXiv: https://arxiv.org/abs/2103.12040 · HTML: https://mlsanity.com/paper/2103.12040/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 43 references that are in the map.
- First posted on arXiv 22 Mar 2021.

## Builds on (most cited first, 30 of 30)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Deformable Convolutional Networks](https://mlsanity.com/paper/1703.06211/) (2017)
- [Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1611.08050/) (2016)
- [Deep Layer Aggregation](https://mlsanity.com/paper/1707.06484/) (2017)
- [ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation](https://mlsanity.com/paper/1606.02147/) (2016)
- [Spatial As Deep: Spatial CNN for Traffic Scene Understanding](https://mlsanity.com/paper/1712.06080/) (2017)
- [Learning Lightweight Lane Detection CNNs by Self Attention Distillation](https://mlsanity.com/paper/1908.00821/) (2019)
- [Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection](https://mlsanity.com/paper/2010.12035/) (2020)
- [Towards End-to-End Lane Detection: an Instance Segmentation Approach](https://mlsanity.com/paper/1802.05591/) (2018)
- [3D-LaneNet: End-to-End 3D Multiple Lane Detection](https://mlsanity.com/paper/1811.10203/) (2018)
- [PolyLaneNet: Lane Estimation via Deep Polynomial Regression](https://mlsanity.com/paper/2004.10924/) (2020)
- [CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending](https://mlsanity.com/paper/2007.12147/) (2020)
- [Deep Watershed Transform for Instance Segmentation](https://mlsanity.com/paper/1611.08303/) (2016)
- [Key Points Estimation and Point Instance Segmentation Approach for Lane Detection](https://mlsanity.com/paper/2002.06604/) (2020)
- [RESA: Recurrent Feature-Shift Aggregator for Lane Detection](https://mlsanity.com/paper/2008.13719/) (2020)
- [Inter-Region Affinity Distillation for Road Marking Segmentation](https://mlsanity.com/paper/2004.05304/) (2020)
- [VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition](https://mlsanity.com/paper/1710.06288/) (2017)
- [End-to-End Lane Marker Detection via Row-wise Classification](https://mlsanity.com/paper/2005.08630/) (2020)
- [How would surround vehicles move? A Unified Framework for Maneuver Classification and Motion Prediction](https://mlsanity.com/paper/1801.06523/) (2018)
- [Robust Lane Detection from Continuous Driving Scenes Using Deep Neural Networks](https://mlsanity.com/paper/1903.02193/) (2019)
- [End-to-end Lane Detection through Differentiable Least-Squares Fitting](https://mlsanity.com/paper/1902.00293/) (2019)
- [EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection](https://mlsanity.com/paper/1806.05525/) (2018)
- [FastDraw: Addressing the Long Tail of Lane Detection by Adapting a Sequential Prediction Network](https://mlsanity.com/paper/1905.04354/) (2019)
- [Deep Multi-Sensor Lane Detection](https://mlsanity.com/paper/1905.01555/) (2019)
- [Trajectory Prediction in Autonomous Driving with a Lane Heading Auxiliary Loss](https://mlsanity.com/paper/2011.06679/) (2020)
- [Lane Detection and Classification using Cascaded CNNs](https://mlsanity.com/paper/1907.01294/) (2019)
- [Lane Detection in Low-light Conditions Using an Efficient Data Enhancement : Light Conditions Style Transfer](https://mlsanity.com/paper/2002.01177/) (2020)
- [Pixel-level Encoding and Depth Layering for Instance-level Semantic Labeling](https://mlsanity.com/paper/1604.05096/) (2016)
- [Looking at Hands in Autonomous Vehicles: A ConvNet Approach using Part Affinity Fields](https://mlsanity.com/paper/1804.01176/) (2018)

## Built on by (most cited first, 30 of 41)

- [PersFormer: 3D Lane Detection via Perspective Transformer and the OpenLane Benchmark](https://mlsanity.com/paper/2203.11089/) (2022)
- [CLRNet: Cross Layer Refinement Network for Lane Detection](https://mlsanity.com/paper/2203.10350/) (2022)
- [ONCE-3DLanes: Building Monocular 3D Lane Detection](https://mlsanity.com/paper/2205.00301/) (2022)
- [Ultra Fast Deep Lane Detection with Hybrid Anchor Driven Ordinal Classification](https://mlsanity.com/paper/2206.07389/) (2022)
- [Towards Explainable, Safe Autonomous Driving with Language Embeddings for Novelty Identification and Active Learning: Framework and Experimental Analysis with Real-World Data Sets](https://mlsanity.com/paper/2402.07320/) (2024)
- [Sim-to-Real Domain Adaptation for Lane Detection and Classification in Autonomous Driving](https://mlsanity.com/paper/2202.07133/) (2022)
- [RCLane: Relay Chain Prediction for Lane Detection](https://mlsanity.com/paper/2207.09399/) (2022)
- [The Why, When, and How to Use Active Learning in Large-Data-Driven 3D Object Detection for Safe Autonomous Driving: An Empirical Exploration](https://mlsanity.com/paper/2401.16634/) (2024)
- [ADNet: Lane Shape Prediction via Anchor Decomposition](https://mlsanity.com/paper/2308.10481/) (2023)
- [BSNet: Lane Detection via Draw B-spline Curves Nearby](https://mlsanity.com/paper/2301.06910/) (2023)
- [GroupLane: End-to-End 3D Lane Detection with Channel-wise Grouping](https://mlsanity.com/paper/2307.09472/) (2023)
- [WS-3D-Lane: Weakly Supervised 3D Lane Detection With 2D Lane Labels](https://mlsanity.com/paper/2209.11523/) (2022)
- [Recurrent Generic Contour-based Instance Segmentation with Progressive Learning](https://mlsanity.com/paper/2301.08898/) (2023)
- [Lane Detection with Versatile AtrousFormer and Local Semantic Guidance](https://mlsanity.com/paper/2203.04067/) (2022)
- [PriorLane: A Prior Knowledge Enhanced Lane Detection Approach Based on Transformer](https://mlsanity.com/paper/2209.06994/) (2022)
- [Row-wise LiDAR Lane Detection Network with Lane Correlation Refinement](https://mlsanity.com/paper/2210.08745/) (2022)
- [Dense Hybrid Proposal Modulation for Lane Detection](https://mlsanity.com/paper/2304.14874/) (2023)
- [HoughLaneNet: Lane Detection with Deep Hough Transform and Dynamic Convolution](https://mlsanity.com/paper/2307.03494/) (2023)
- [Decoupling the Curve Modeling and Pavement Regression for Lane Detection](https://mlsanity.com/paper/2309.10533/) (2023)
- [Monocular Lane Detection Based on Deep Learning: A Survey](https://mlsanity.com/paper/2411.16316/) (2024)
- [Learned Two-Plane Perspective Prior based Image Resampling for Efficient Object Detection](https://mlsanity.com/paper/2303.14311/) (2023)
- [CANet: Curved Guide Line Network with Adaptive Decoder for Lane Detection](https://mlsanity.com/paper/2304.11546/) (2023)
- [Sketch and Refine: Towards Fast and Accurate Lane Detection](https://mlsanity.com/paper/2401.14729/) (2024)
- [LDTR: Transformer-based Lane Detection with Anchor-chain Representation](https://mlsanity.com/paper/2403.14354/) (2024)
- [Sparse Laneformer](https://mlsanity.com/paper/2404.07821/) (2024)
- [Towards Vision Zero: The TUM Traffic Accid3nD Dataset](https://mlsanity.com/paper/2503.12095/) (2025)
- [An intelligent modular real-time vision-based system for environment perception](https://mlsanity.com/paper/2303.16710/) (2023)
- [ENet-21: An Optimized light CNN Structure for Lane Detection](https://mlsanity.com/paper/2403.19782/) (2024)
- [Polar R-CNN: End-to-End Lane Detection with Fewer Anchors](https://mlsanity.com/paper/2411.01499/) (2024)
- [Enhancing Highway Safety: Accident Detection on the A9 Test Stretch Using Roadside Sensors](https://mlsanity.com/paper/2502.00402/) (2025)

## Abstract (arXiv)

This study presents an approach to lane detection involving the prediction of binary segmentation masks and per-pixel affinity fields. These affinity fields, along with the binary masks, can then be used to cluster lane pixels horizontally and vertically into corresponding lane instances in a post-processing step. This clustering is achieved through a simple row-by-row decoding process with little overhead; such an approach allows LaneAF to detect a variable number of lanes without assuming a fixed or maximum number of lanes. Moreover, this form of clustering is more interpretable in comparison to previous visual clustering approaches, and can be analyzed to identify and correct sources of error. Qualitative and quantitative results obtained on popular lane detection datasets demonstrate the model's ability to detect and cluster lanes effectively and robustly. Our proposed approach sets a new state-of-the-art on the challenging CULane dataset and the recently introduced Unsupervised LLAMAS dataset.