# Lane Graph Estimation for Scene Understanding in Urban Driving

arXiv: https://arxiv.org/abs/2105.00195 · HTML: https://mlsanity.com/paper/2105.00195/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 26 references that are in the map.
- First posted on arXiv 1 May 2021.

## Builds on (most cited first, 16 of 16)

- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Center-based 3D Object Detection and Tracking](https://mlsanity.com/paper/2006.11275/) (2020)
- [Learning Lane Graph Representations for Motion Forecasting](https://mlsanity.com/paper/2007.13732/) (2020)
- [Graph R-CNN for Scene Graph Generation](https://mlsanity.com/paper/1808.00191/) (2018)
- [SpaceNet: A Remote Sensing Dataset and Challenge Series](https://mlsanity.com/paper/1807.01232/) (2018)
- [RoadTracer: Automatic Extraction of Road Networks from Aerial Images](https://mlsanity.com/paper/1802.03680/) (2018)
- [Topological Map Extraction from Overhead Images](https://mlsanity.com/paper/1812.01497/) (2018)
- [Sat2Graph: Road Graph Extraction through Graph-Tensor Encoding](https://mlsanity.com/paper/2007.09547/) (2020)
- [DAGMapper: Learning to Map by Discovering Lane Topology](https://mlsanity.com/paper/2012.12377/) (2020)
- [Hierarchical Recurrent Attention Networks for Structured Online Maps](https://mlsanity.com/paper/2012.12314/) (2020)
- [Convolutional Recurrent Network for Road Boundary Extraction](https://mlsanity.com/paper/2012.12160/) (2020)
- [3D-LaneNet+: Anchor Free Lane Detection using a Semi-Local Representation](https://mlsanity.com/paper/2011.01535/) (2020)
- [Deep Multi-Sensor Lane Detection](https://mlsanity.com/paper/1905.01555/) (2019)
- [Neural Turtle Graphics for Modeling City Road Layouts](https://mlsanity.com/paper/1910.02055/) (2019)
- [Anytime Lane-Level Intersection Estimation Based on Trajectories of Other Traffic Participants](https://mlsanity.com/paper/1906.02495/) (2019)

## Built on by (most cited first, 18 of 18)

- [Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting](https://mlsanity.com/paper/2301.00493/) (2023)
- [VectorMapNet: End-to-end Vectorized HD Map Learning](https://mlsanity.com/paper/2206.08920/) (2022)
- [Graph-based Topology Reasoning for Driving Scenes](https://mlsanity.com/paper/2304.05277/) (2023)
- [TopoLogic: An Interpretable Pipeline for Lane Topology Reasoning on Driving Scenes](https://mlsanity.com/paper/2405.14747/) (2024)
- [Learning and Aggregating Lane Graphs for Urban Automated Driving](https://mlsanity.com/paper/2302.06175/) (2023)
- [TopoPoint: Enhance Topology Reasoning via Endpoint Detection in Autonomous Driving](https://mlsanity.com/paper/2505.17771/) (2025)
- [Reusing Attention for One-stage Lane Topology Understanding](https://mlsanity.com/paper/2507.17617/) (2025)
- [AugMapNet: Improving Spatial Latent Structure via BEV Grid Augmentation for Enhanced Vectorized Online HD Map Construction](https://mlsanity.com/paper/2503.13430/) (2025)
- [Lane Graph Extraction from Aerial Imagery via Lane Segmentation Refinement with Diffusion Models](https://mlsanity.com/paper/2405.00620/) (2024)
- [AutoGraph: Predicting Lane Graphs from Traffic Observations](https://mlsanity.com/paper/2306.15410/) (2023)
- [Learning to Predict Navigational Patterns from Partial Observations](https://mlsanity.com/paper/2304.13242/) (2023)
- [LMT-Net: Lane Model Transformer Network for Automated HD Mapping from Sparse Vehicle Observations](https://mlsanity.com/paper/2409.12409/) (2024)
- [nuScenes Revisited: Progress and Challenges in Autonomous Driving](https://mlsanity.com/paper/2512.02448/) (2025)
- [Explainable Scene Understanding with Qualitative Representations and Graph Neural Networks](https://mlsanity.com/paper/2504.12817/) (2025)
- [Learning Isometric Embeddings of Road Networks using Multidimensional Scaling](https://mlsanity.com/paper/2504.17534/) (2025)
- [Generating and Explaining Corner Cases Using Learnt Probabilistic Lane Graphs](https://mlsanity.com/paper/2308.13658/) (2023)
- [YOLinO++: Single-Shot Estimation of Generic Polylines for Mapless Automated Diving](https://mlsanity.com/paper/2402.00989/) (2024)
- [Generative Lane Topology Reasoning via Autoregressive Model with Geometry Prior](https://mlsanity.com/paper/2606.31814/) (2026)

## Abstract (arXiv)

Lane-level scene annotations provide invaluable data in autonomous vehicles for trajectory planning in complex environments such as urban areas and cities. However, obtaining such data is time-consuming and expensive since lane annotations have to be annotated manually by humans and are as such hard to scale to large areas. In this work, we propose a novel approach for lane geometry estimation from bird's-eye-view images. We formulate the problem of lane shape and lane connections estimation as a graph estimation problem where lane anchor points are graph nodes and lane segments are graph edges. We train a graph estimation model on multimodal bird's-eye-view data processed from the popular NuScenes dataset and its map expansion pack. We furthermore estimate the direction of the lane connection for each lane segment with a separate model which results in a directed lane graph. We illustrate the performance of our LaneGraphNet model on the challenging NuScenes dataset and provide extensive qualitative and quantitative evaluation. Our model shows promising performance for most evaluated urban scenes and can serve as a step towards automated generation of HD lane annotations for autonomous driving.