# HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps

arXiv: https://arxiv.org/abs/2106.14880 · HTML: https://mlsanity.com/paper/2106.14880/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 40 references that are in the map.
- First posted on arXiv 28 Jun 2021.

## Builds on (most cited first, 21 of 21)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Argoverse: 3D Tracking and Forecasting with Rich Maps](https://mlsanity.com/paper/1911.02620/) (2019)
- [VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation](https://mlsanity.com/paper/2005.04259/) (2020)
- [MultiPath: Multiple Probabilistic Anchor Trajectory Hypotheses for Behavior Prediction](https://mlsanity.com/paper/1910.05449/) (2019)
- [GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models](https://mlsanity.com/paper/1802.08773/) (2018)
- [MolGAN: An implicit generative model for small molecular graphs](https://mlsanity.com/paper/1805.11973/) (2018)
- [Learning Lane Graph Representations for Motion Forecasting](https://mlsanity.com/paper/2007.13732/) (2020)
- [TNT: Target-driveN Trajectory Prediction](https://mlsanity.com/paper/2008.08294/) (2020)
- [A Neural Representation of Sketch Drawings](https://mlsanity.com/paper/1704.03477/) (2017)
- [PolyGen: An Autoregressive Generative Model of 3D Meshes](https://mlsanity.com/paper/2002.10880/) (2020)
- [Efficient Graph Generation with Graph Recurrent Attention Networks](https://mlsanity.com/paper/1910.00760/) (2019)
- [GraphGAN: Graph Representation Learning with Generative Adversarial Nets](https://mlsanity.com/paper/1711.08267/) (2017)
- [RoadTracer: Automatic Extraction of Road Networks from Aerial Images](https://mlsanity.com/paper/1802.03680/) (2018)
- [Variational Graph Recurrent Neural Networks](https://mlsanity.com/paper/1908.09710/) (2019)
- [NeVAE: A Deep Generative Model for Molecular Graphs](https://mlsanity.com/paper/1802.05283/) (2018)
- [Deep AutoRegressive Networks](https://mlsanity.com/paper/1310.8499/) (2013)
- [DAGMapper: Learning to Map by Discovering Lane Topology](https://mlsanity.com/paper/2012.12377/) (2020)
- [Neural Turtle Graphics for Modeling City Road Layouts](https://mlsanity.com/paper/1910.02055/) (2019)

## Built on by (most cited first, 30 of 40)

- [End-to-end Autonomous Driving: Challenges and Frontiers](https://mlsanity.com/paper/2306.16927/) (2023)
- [HDMapNet: An Online HD Map Construction and Evaluation Framework](https://mlsanity.com/paper/2107.06307/) (2021)
- [VectorMapNet: End-to-end Vectorized HD Map Learning](https://mlsanity.com/paper/2206.08920/) (2022)
- [PivotNet: Vectorized Pivot Learning for End-to-end HD Map Construction](https://mlsanity.com/paper/2308.16477/) (2023)
- [End-to-End Vectorized HD-map Construction with Piecewise Bezier Curve](https://mlsanity.com/paper/2306.09700/) (2023)
- [Lane Graph as Path: Continuity-preserving Path-wise Modeling for Online Lane Graph Construction](https://mlsanity.com/paper/2303.08815/) (2023)
- [HIMap: HybrId Representation Learning for End-to-end Vectorized HD Map Construction](https://mlsanity.com/paper/2403.08639/) (2024)
- [Online Vectorized HD Map Construction using Geometry](https://mlsanity.com/paper/2312.03341/) (2023)
- [Vehicle trajectory prediction works, but not everywhere](https://mlsanity.com/paper/2112.03909/) (2021)
- [The Integration of Prediction and Planning in Deep Learning Automated Driving Systems: A Review](https://mlsanity.com/paper/2308.05731/) (2023)
- [Segment Anything Model for Road Network Graph Extraction](https://mlsanity.com/paper/2403.16051/) (2024)
- [Scenario Dreamer: Vectorized Latent Diffusion for Generating Driving Simulation Environments](https://mlsanity.com/paper/2503.22496/) (2025)
- [DriveSceneGen: Generating Diverse and Realistic Driving Scenarios from Scratch](https://mlsanity.com/paper/2309.14685/) (2023)
- [SLEDGE: Synthesizing Driving Environments with Generative Models and Rule-Based Traffic](https://mlsanity.com/paper/2403.17933/) (2024)
- [Coarse-to-Fine: a Hierarchical Diffusion Model for Molecule Generation in 3D](https://mlsanity.com/paper/2305.13266/) (2023)
- [InsMapper: Exploring Inner-instance Information for Vectorized HD Mapping](https://mlsanity.com/paper/2308.08543/) (2023)
- [csBoundary: City-scale Road-boundary Detection in Aerial Images for High-definition Maps](https://mlsanity.com/paper/2111.06020/) (2021)
- [DTCLMapper: Dual Temporal Consistent Learning for Vectorized HD Map Construction](https://mlsanity.com/paper/2405.05518/) (2024)
- [End-to-end Autonomous Driving using Deep Learning: A Systematic Review](https://mlsanity.com/paper/2311.18636/) (2023)
- [Pix2Map: Cross-modal Retrieval for Inferring Street Maps from Images](https://mlsanity.com/paper/2301.04224/) (2023)
- [AugMapNet: Improving Spatial Latent Structure via BEV Grid Augmentation for Enhanced Vectorized Online HD Map Construction](https://mlsanity.com/paper/2503.13430/) (2025)
- [Exploring Navigation Maps for Learning-Based Motion Prediction](https://mlsanity.com/paper/2302.06195/) (2023)
- [Neural HD Map Generation from Multiple Vectorized Tiles Locally Produced by Autonomous Vehicles](https://mlsanity.com/paper/2409.03445/) (2024)
- [PathwayBench: Assessing Routability of Pedestrian Pathway Networks Inferred from Multi-City Imagery](https://mlsanity.com/paper/2407.16875/) (2024)
- [APE: An Open and Shared Annotated Dataset for Learning Urban Pedestrian Path Networks](https://mlsanity.com/paper/2303.02323/) (2023)
- [Learning to Predict Navigational Patterns from Partial Observations](https://mlsanity.com/paper/2304.13242/) (2023)
- [Generative AI for Autonomous Driving: A Review](https://mlsanity.com/paper/2505.15863/) (2025)
- [FLOWGEN: Fast and slow graph generation](https://mlsanity.com/paper/2207.07656/) (2022)
- [Large-Scale Auto-Regressive Modeling Of Street Networks](https://mlsanity.com/paper/2209.00281/) (2022)
- [OASIS: Automated Assessment of Urban Pedestrian Paths at Scale](https://mlsanity.com/paper/2303.02287/) (2023)

## Abstract (arXiv)

High Definition (HD) maps are maps with precise definitions of road lanes with rich semantics of the traffic rules. They are critical for several key stages in an autonomous driving system, including motion forecasting and planning. However, there are only a small amount of real-world road topologies and geometries, which significantly limits our ability to test out the self-driving stack to generalize onto new unseen scenarios. To address this issue, we introduce a new challenging task to generate HD maps. In this work, we explore several autoregressive models using different data representations, including sequence, plain graph, and hierarchical graph. We propose HDMapGen, a hierarchical graph generation model capable of producing high-quality and diverse HD maps through a coarse-to-fine approach. Experiments on the Argoverse dataset and an in-house dataset show that HDMapGen significantly outperforms baseline methods. Additionally, we demonstrate that HDMapGen achieves high scalability and efficiency.