# Automatic Construction of Lane-level HD Maps for Urban Scenes

arXiv: https://arxiv.org/abs/2107.10972 · HTML: https://mlsanity.com/paper/2107.10972/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jul 2021.

## Built on by (most cited first, 15 of 15)

- [RNGDet: Road Network Graph Detection by Transformer in Aerial Images](https://mlsanity.com/paper/2202.07824/) (2022)
- [Learning and Aggregating Lane Graphs for Urban Automated Driving](https://mlsanity.com/paper/2302.06175/) (2023)
- [LiDAR2Map: In Defense of LiDAR-Based Semantic Map Construction Using Online Camera Distillation](https://mlsanity.com/paper/2304.11379/) (2023)
- [CenterLineDet: CenterLine Graph Detection for Road Lanes with Vehicle-mounted Sensors by Transformer for HD Map Generation](https://mlsanity.com/paper/2209.07734/) (2022)
- [SemVecNet: Generalizable Vector Map Generation for Arbitrary Sensor Configurations](https://mlsanity.com/paper/2405.00250/) (2024)
- [Enhancing Online Road Network Perception and Reasoning with Standard Definition Maps](https://mlsanity.com/paper/2408.01471/) (2024)
- [THMA: Tencent HD Map AI System for Creating HD Map Annotations](https://mlsanity.com/paper/2212.11123/) (2022)
- [Reusing Attention for One-stage Lane Topology Understanding](https://mlsanity.com/paper/2507.17617/) (2025)
- [SST-Calib: Simultaneous Spatial-Temporal Parameter Calibration between LIDAR and Camera](https://mlsanity.com/paper/2207.03704/) (2022)
- [Exploring Navigation Maps for Learning-Based Motion Prediction](https://mlsanity.com/paper/2302.06195/) (2023)
- [Lane Graph Extraction from Aerial Imagery via Lane Segmentation Refinement with Diffusion Models](https://mlsanity.com/paper/2405.00620/) (2024)
- [SD++: Enhancing Standard Definition Maps by Incorporating Road Knowledge using LLMs](https://mlsanity.com/paper/2502.02773/) (2025)
- [Video Killed the HD-Map: Predicting Multi-Agent Behavior Directly From Aerial Images](https://mlsanity.com/paper/2305.11856/) (2023)
- [Semantic Map Learning of Traffic Light to Lane Assignment based on Motion Data](https://mlsanity.com/paper/2309.14793/) (2023)
- [CSMapping: Scalable Crowdsourced Semantic Mapping and Topology Inference for Autonomous Driving](https://mlsanity.com/paper/2512.03510/) (2025)

## Abstract (arXiv)

High definition (HD) maps have demonstrated their essential roles in enabling full autonomy, especially in complex urban scenarios. As a crucial layer of the HD map, lane-level maps are particularly useful: they contain geometrical and topological information for both lanes and intersections. However, large scale construction of HD maps is limited by tedious human labeling and high maintenance costs, especially for urban scenarios with complicated road structures and irregular markings. This paper proposes an approach based on semantic-particle filter to tackle the automatic lane-level mapping problem in urban scenes. The map skeleton is firstly structured as a directed cyclic graph from online mapping database OpenStreetMap. Our proposed method then performs semantic segmentation on 2D front-view images from ego vehicles and explores the lane semantics on a birds-eye-view domain with true topographical projection. Exploiting OpenStreetMap, we further infer lane topology and reference trajectory at intersections with the aforementioned lane semantics. The proposed algorithm has been tested in densely urbanized areas, and the results demonstrate accurate and robust reconstruction of the lane-level HD map.