# RCLane: Relay Chain Prediction for Lane Detection

arXiv: https://arxiv.org/abs/2207.09399 · HTML: https://mlsanity.com/paper/2207.09399/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 36 references that are in the map.
- First posted on arXiv 19 Jul 2022.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [Training Region-based Object Detectors with Online Hard Example Mining](https://mlsanity.com/paper/1604.03540/) (2016)
- [Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation](https://mlsanity.com/paper/2003.07853/) (2020)
- [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)
- [Ultra Fast Structure-aware Deep Lane Detection](https://mlsanity.com/paper/2004.11757/) (2020)
- [End-to-end Lane Shape Prediction with Transformers](https://mlsanity.com/paper/2011.04233/) (2020)
- [PolyLaneNet: Lane Estimation via Deep Polynomial Regression](https://mlsanity.com/paper/2004.10924/) (2020)
- [CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution](https://mlsanity.com/paper/2105.05003/) (2021)
- [CurveLane-NAS: Unifying Lane-Sensitive Architecture Search and Adaptive Point Blending](https://mlsanity.com/paper/2007.12147/) (2020)
- [Key Points Estimation and Point Instance Segmentation Approach for Lane Detection](https://mlsanity.com/paper/2002.06604/) (2020)
- [Focus on Local: Detecting Lane Marker from Bottom Up via Key Point](https://mlsanity.com/paper/2105.13680/) (2021)
- [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)
- [LaneAF: Robust Multi-Lane Detection with Affinity Fields](https://mlsanity.com/paper/2103.12040/) (2021)
- [FastDraw: Addressing the Long Tail of Lane Detection by Adapting a Sequential Prediction Network](https://mlsanity.com/paper/1905.04354/) (2019)

## Built on by (most cited first, 20 of 20)

- [SparseOcc: Rethinking Sparse Latent Representation for Vision-Based Semantic Occupancy Prediction](https://mlsanity.com/paper/2404.09502/) (2024)
- [Anchor3DLane: Learning to Regress 3D Anchors for Monocular 3D Lane Detection](https://mlsanity.com/paper/2301.02371/) (2023)
- [Recursive Video Lane Detection](https://mlsanity.com/paper/2308.11106/) (2023)
- [DV-3DLane: End-to-end Multi-modal 3D Lane Detection with Dual-view Representation](https://mlsanity.com/paper/2406.16072/) (2024)
- [Anchor3DLane++: 3D Lane Detection via Sample-Adaptive Sparse 3D Anchor Regression](https://mlsanity.com/paper/2412.16889/) (2024)
- [LaneCorrect: Self-supervised Lane Detection](https://mlsanity.com/paper/2404.14671/) (2024)
- [BezierFormer: A Unified Architecture for 2D and 3D Lane Detection](https://mlsanity.com/paper/2404.16304/) (2024)
- [Decoupling the Curve Modeling and Pavement Regression for Lane Detection](https://mlsanity.com/paper/2309.10533/) (2023)
- [ALINA: Advanced Line Identification and Notation Algorithm](https://mlsanity.com/paper/2406.08775/) (2024)
- [OMR: Occlusion-Aware Memory-Based Refinement for Video Lane Detection](https://mlsanity.com/paper/2408.07486/) (2024)
- [Monocular Lane Detection Based on Deep Learning: A Survey](https://mlsanity.com/paper/2411.16316/) (2024)
- [Sketch and Refine: Towards Fast and Accurate Lane Detection](https://mlsanity.com/paper/2401.14729/) (2024)
- [Translating Images to Road Network: A Sequence-to-Sequence Perspective](https://mlsanity.com/paper/2402.08207/) (2024)
- [GLane3D : Detecting Lanes with Graph of 3D Keypoints](https://mlsanity.com/paper/2503.23882/) (2025)
- [LanePtrNet: Revisiting Lane Detection as Point Voting and Grouping on Curves](https://mlsanity.com/paper/2403.05155/) (2024)
- [Depth3DLane: Fusing Monocular 3D Lane Detection with Self-Supervised Monocular Depth Estimation](https://mlsanity.com/paper/2507.13857/) (2025)
- [PoseDriver: A Unified Approach to Multi-Category Skeleton Detection for Autonomous Driving](https://mlsanity.com/paper/2603.23215/) (2026)
- [HSDF-Lane: Height-Aligned Signed Distance Field with Semantic Lane Prior for 3D Lane Detection](https://mlsanity.com/paper/2606.31172/) (2026)
- [SparseOcc++: Geometry-Aware Sparse Latent Representation for Semantic Occupancy Prediction](https://mlsanity.com/paper/2607.04732/) (2026)
- [SIGMA-Lane: Scale-pyramId Gated MAmba for Temporally Consistent Video Lane Detection](https://mlsanity.com/paper/2608.16338/) (2026)

## Abstract (arXiv)

Lane detection is an important component of many real-world autonomous systems. Despite a wide variety of lane detection approaches have been proposed, reporting steady benchmark improvements over time, lane detection remains a largely unsolved problem. This is because most of the existing lane detection methods either treat the lane detection as a dense prediction or a detection task, few of them consider the unique topologies (Y-shape, Fork-shape, nearly horizontal lane) of the lane markers, which leads to sub-optimal solution. In this paper, we present a new method for lane detection based on relay chain prediction. Specifically, our model predicts a segmentation map to classify the foreground and background region. For each pixel point in the foreground region, we go through the forward branch and backward branch to recover the whole lane. Each branch decodes a transfer map and a distance map to produce the direction moving to the next point, and how many steps to progressively predict a relay station (next point). As such, our model is able to capture the keypoints along the lanes. Despite its simplicity, our strategy allows us to establish new state-of-the-art on four major benchmarks including TuSimple, CULane, CurveLanes and LLAMAS.