# Enhancing Online Road Network Perception and Reasoning with Standard Definition Maps

arXiv: https://arxiv.org/abs/2408.01471 · HTML: https://mlsanity.com/paper/2408.01471/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 39 references that are in the map.
- First posted on arXiv 1 Aug 2024.

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

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers](https://mlsanity.com/paper/2203.17270/) (2022)
- [Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting](https://mlsanity.com/paper/2301.00493/) (2023)
- [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)
- [MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2208.14437/) (2022)
- [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)
- [End-to-end Interpretable Neural Motion Planner](https://mlsanity.com/paper/2101.06679/) (2021)
- [MapTRv2: An End-to-End Framework for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2308.05736/) (2023)
- [An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection](https://mlsanity.com/paper/1904.09730/) (2019)
- [Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations](https://mlsanity.com/paper/2008.05930/) (2020)
- [Baidu Apollo EM Motion Planner](https://mlsanity.com/paper/1807.08048/) (2018)
- [Structured Bird's-Eye-View Traffic Scene Understanding from Onboard Images](https://mlsanity.com/paper/2110.01997/) (2021)
- [Key Points Estimation and Point Instance Segmentation Approach for Lane Detection](https://mlsanity.com/paper/2002.06604/) (2020)
- [Gen-LaneNet: A Generalized and Scalable Approach for 3D Lane Detection](https://mlsanity.com/paper/2003.10656/) (2020)
- [Graph-based Topology Reasoning for Driving Scenes](https://mlsanity.com/paper/2304.05277/) (2023)
- [TopoMLP: A Simple yet Strong Pipeline for Driving Topology Reasoning](https://mlsanity.com/paper/2310.06753/) (2023)
- [Variational End-to-End Navigation and Localization](https://mlsanity.com/paper/1811.10119/) (2018)
- [Understanding Bird's-Eye View of Road Semantics using an Onboard Camera](https://mlsanity.com/paper/2012.03040/) (2020)
- [End-to-End Learning of Driving Models with Surround-View Cameras and Route Planners](https://mlsanity.com/paper/1803.10158/) (2018)
- [Automatic Construction of Lane-level HD Maps for Urban Scenes](https://mlsanity.com/paper/2107.10972/) (2021)
- [The 1st-place Solution for CVPR 2023 OpenLane Topology in Autonomous Driving Challenge](https://mlsanity.com/paper/2306.09590/) (2023)
- [OSM vs HD Maps: Map Representations for Trajectory Prediction](https://mlsanity.com/paper/2311.02305/) (2023)

## Built on by (most cited first, 11 of 11)

- [SMART: Advancing Scalable Map Priors for Driving Topology Reasoning](https://mlsanity.com/paper/2502.04329/) (2025)
- [PriorDrive: Enhancing Online HD Mapping with Unified Vector Priors](https://mlsanity.com/paper/2409.05352/) (2024)
- [SEPT: Standard-Definition Map Enhanced Scene Perception and Topology Reasoning for Autonomous Driving](https://mlsanity.com/paper/2505.12246/) (2025)
- [Coherent Online Road Topology Estimation and Reasoning with Standard-Definition Maps](https://mlsanity.com/paper/2507.01397/) (2025)
- [SD++: Enhancing Standard Definition Maps by Incorporating Road Knowledge using LLMs](https://mlsanity.com/paper/2502.02773/) (2025)
- [RelMap: Enhancing Online Map Construction with Class-Aware Spatial Relation and Semantic Priors](https://mlsanity.com/paper/2507.21567/) (2025)
- [NavMapFusion: Diffusion-based Fusion of Navigation Maps for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2512.03317/) (2025)
- [D2HDMap: Non-visible Driveline Map Prior for Online Vectorized HD Map Prediction](https://mlsanity.com/paper/2606.20725/) (2026)
- [GeoReFormer: Geometry-Aware Refinement for Lane Segment Detection and Topology Reasoning](https://mlsanity.com/paper/2603.26018/) (2026)
- [Unified Map Prior Encoder for Mapping and Planning](https://mlsanity.com/paper/2605.02762/) (2026)
- [Driving with Context: Online Map Matching for Complex Roads Using Lane Markings and Scenario Recognition](https://mlsanity.com/paper/2505.05007/) (2025)

## Abstract (arXiv)

Autonomous driving for urban and highway driving applications often requires High Definition (HD) maps to generate a navigation plan. Nevertheless, various challenges arise when generating and maintaining HD maps at scale. While recent online mapping methods have started to emerge, their performance especially for longer ranges is limited by heavy occlusion in dynamic environments. With these considerations in mind, our work focuses on leveraging lightweight and scalable priors-Standard Definition (SD) maps-in the development of online vectorized HD map representations. We first examine the integration of prototypical rasterized SD map representations into various online mapping architectures. Furthermore, to identify lightweight strategies, we extend the OpenLane-V2 dataset with OpenStreetMaps and evaluate the benefits of graphical SD map representations. A key finding from designing SD map integration components is that SD map encoders are model agnostic and can be quickly adapted to new architectures that utilize bird's eye view (BEV) encoders. Our results show that making use of SD maps as priors for the online mapping task can significantly speed up convergence and boost the performance of the online centerline perception task by 30% (mAP). Furthermore, we show that the introduction of the SD maps leads to a reduction of the number of parameters in the perception and reasoning task by leveraging SD map graphs while improving the overall performance. Project Page: https://henryzhangzhy.github.io/sdhdmap/.