# Augmenting Lane Perception and Topology Understanding with Standard Definition Navigation Maps

arXiv: https://arxiv.org/abs/2311.04079 · HTML: https://mlsanity.com/paper/2311.04079/

## Key facts

- Cited by 58 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 35 references that are in the map.
- First posted on arXiv 7 Nov 2023.

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [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)
- [DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries](https://mlsanity.com/paper/2110.06922/) (2021)
- [PETR: Position Embedding Transformation for Multi-View 3D Object Detection](https://mlsanity.com/paper/2203.05625/) (2022)
- [VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation](https://mlsanity.com/paper/2005.04259/) (2020)
- [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)
- [Motion Transformer with Global Intention Localization and Local Movement Refinement](https://mlsanity.com/paper/2209.13508/) (2022)
- [VectorMapNet: End-to-end Vectorized HD Map Learning](https://mlsanity.com/paper/2206.08920/) (2022)
- [FUTR3D: A Unified Sensor Fusion Framework for 3D Detection](https://mlsanity.com/paper/2203.10642/) (2022)
- [Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations](https://mlsanity.com/paper/2008.05930/) (2020)
- [StreamMapNet: Streaming Mapping Network for Vectorized Online HD Map Construction](https://mlsanity.com/paper/2308.12570/) (2023)
- [PivotNet: Vectorized Pivot Learning for End-to-end HD Map Construction](https://mlsanity.com/paper/2308.16477/) (2023)
- [HDNET: Exploiting HD Maps for 3D Object Detection](https://mlsanity.com/paper/2012.11704/) (2020)
- [Neural Map Prior for Autonomous Driving](https://mlsanity.com/paper/2304.08481/) (2023)
- [Graph-based Topology Reasoning for Driving Scenes](https://mlsanity.com/paper/2304.05277/) (2023)
- [Online Map Vectorization for Autonomous Driving: A Rasterization Perspective](https://mlsanity.com/paper/2306.10502/) (2023)
- [Lane Graph as Path: Continuity-preserving Path-wise Modeling for Online Lane Graph Construction](https://mlsanity.com/paper/2303.08815/) (2023)
- [InstaGraM: Instance-level Graph Modeling for Vectorized HD Map Learning](https://mlsanity.com/paper/2301.04470/) (2023)
- [Complementing Onboard Sensors with Satellite Map: A New Perspective for HD Map Construction](https://mlsanity.com/paper/2308.15427/) (2023)
- [Are we Missing Confidence in Pseudo-LiDAR Methods for Monocular 3D Object Detection?](https://mlsanity.com/paper/2012.05796/) (2020)
- [Bi-Mapper: Holistic BEV Semantic Mapping for Autonomous Driving](https://mlsanity.com/paper/2305.04205/) (2023)
- [NeMO: Neural Map Growing System for Spatiotemporal Fusion in Bird's-Eye-View and BDD-Map Benchmark](https://mlsanity.com/paper/2306.04540/) (2023)
- [The 1st-place Solution for CVPR 2023 OpenLane Topology in Autonomous Driving Challenge](https://mlsanity.com/paper/2306.09590/) (2023)
- [Prior Based Online Lane Graph Extraction from Single Onboard Camera Image](https://mlsanity.com/paper/2307.13344/) (2023)
- [Separated RoadTopoFormer](https://mlsanity.com/paper/2307.01557/) (2023)

## Built on by (most cited first, 30 of 58)

- [TopoLogic: An Interpretable Pipeline for Lane Topology Reasoning on Driving Scenes](https://mlsanity.com/paper/2405.14747/) (2024)
- [Enhancing Vectorized Map Perception with Historical Rasterized Maps](https://mlsanity.com/paper/2409.00620/) (2024)
- [RoadPainter: Points Are Ideal Navigators for Topology transformER](https://mlsanity.com/paper/2407.15349/) (2024)
- [Chameleon: Fast-slow Neuro-symbolic Lane Topology Extraction](https://mlsanity.com/paper/2503.07485/) (2025)
- [DiffMap: Enhancing Map Segmentation with Map Prior Using Diffusion Model](https://mlsanity.com/paper/2405.02008/) (2024)
- [PrevPredMap: Exploring Temporal Modeling with Previous Predictions for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2407.17378/) (2024)
- [TopoSD: Topology-Enhanced Lane Segment Perception with SDMap Prior](https://mlsanity.com/paper/2411.14751/) (2024)
- [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)
- [SeqGrowGraph: Learning Lane Topology as a Chain of Graph Expansions](https://mlsanity.com/paper/2507.04822/) (2025)
- [PriorMapNet: Enhancing Online Vectorized HD Map Construction with Priors](https://mlsanity.com/paper/2408.08802/) (2024)
- [SEPT: Standard-Definition Map Enhanced Scene Perception and Topology Reasoning for Autonomous Driving](https://mlsanity.com/paper/2505.12246/) (2025)
- [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)
- [HisTrackMap: Global Vectorized High-Definition Map Construction via History Map Tracking](https://mlsanity.com/paper/2503.07168/) (2025)
- [SDTagNet: Leveraging Text-Annotated Navigation Maps for Online HD Map Construction](https://mlsanity.com/paper/2506.08997/) (2025)
- [TopoMaskV2: Enhanced Instance-Mask-Based Formulation for the Road Topology Problem](https://mlsanity.com/paper/2409.11325/) (2024)
- [Mind the map! Accounting for existing map information when estimating online HDMaps from sensor](https://mlsanity.com/paper/2311.10517/) (2023)
- [AugMapNet: Improving Spatial Latent Structure via BEV Grid Augmentation for Enhanced Vectorized Online HD Map Construction](https://mlsanity.com/paper/2503.13430/) (2025)
- [LaneDiffusion: Improving Centerline Graph Learning via Prior Injected BEV Feature Generation](https://mlsanity.com/paper/2511.06272/) (2025)
- [Enhancing Lane Segment Perception and Topology Reasoning with Crowdsourcing Trajectory Priors](https://mlsanity.com/paper/2411.17161/) (2024)
- [TopoBDA: Towards Bezier Deformable Attention for Road Topology Understanding](https://mlsanity.com/paper/2412.18951/) (2024)
- [RTMap: Real-Time Recursive Mapping with Change Detection and Localization](https://mlsanity.com/paper/2507.00980/) (2025)
- [MapVision: CVPR 2024 Autonomous Grand Challenge Mapless Driving Tech Report](https://mlsanity.com/paper/2406.10125/) (2024)
- [Uni-PrevPredMap: Extending PrevPredMap to a Unified Framework of Prior-Informed Modeling for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2504.06647/) (2025)
- [ExelMap: Explainable Element-based HD-Map Change Detection and Update](https://mlsanity.com/paper/2409.10178/) (2024)
- [M3TR: A Generalist Model for Real-World HD Map Completion](https://mlsanity.com/paper/2411.10316/) (2024)
- [MapGS: Generalizable Pretraining and Data Augmentation for Online Mapping via Novel View Synthesis](https://mlsanity.com/paper/2501.06660/) (2025)
- [Coherent Online Road Topology Estimation and Reasoning with Standard-Definition Maps](https://mlsanity.com/paper/2507.01397/) (2025)
- [RATopo: Improving Lane Topology Reasoning via Redundancy Assignment](https://mlsanity.com/paper/2508.15272/) (2025)

## Abstract (arXiv)

Autonomous driving has traditionally relied heavily on costly and labor-intensive High Definition (HD) maps, hindering scalability. In contrast, Standard Definition (SD) maps are more affordable and have worldwide coverage, offering a scalable alternative. In this work, we systematically explore the effect of SD maps for real-time lane-topology understanding. We propose a novel framework to integrate SD maps into online map prediction and propose a Transformer-based encoder, SD Map Encoder Representations from transFormers, to leverage priors in SD maps for the lane-topology prediction task. This enhancement consistently and significantly boosts (by up to 60%) lane detection and topology prediction on current state-of-the-art online map prediction methods without bells and whistles and can be immediately incorporated into any Transformer-based lane-topology method. Code is available at https://github.com/NVlabs/SMERF.