# Automatic Building and Labeling of HD Maps with Deep Learning

arXiv: https://arxiv.org/abs/2006.00644 · HTML: https://mlsanity.com/paper/2006.00644/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jun 2020.

## Built on by (most cited first, 7 of 7)

- [SLEDGE: Synthesizing Driving Environments with Generative Models and Rule-Based Traffic](https://mlsanity.com/paper/2403.17933/) (2024)
- [CenterLineDet: CenterLine Graph Detection for Road Lanes with Vehicle-mounted Sensors by Transformer for HD Map Generation](https://mlsanity.com/paper/2209.07734/) (2022)
- [THMA: Tencent HD Map AI System for Creating HD Map Annotations](https://mlsanity.com/paper/2212.11123/) (2022)
- [PlaNeRF: SVD Unsupervised 3D Plane Regularization for NeRF Large-Scale Scene Reconstruction](https://mlsanity.com/paper/2305.16914/) (2023)
- [Generative AI for Autonomous Driving: A Review](https://mlsanity.com/paper/2505.15863/) (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)

## Abstract (arXiv)

In a world where autonomous driving cars are becoming increasingly more common, creating an adequate infrastructure for this new technology is essential. This includes building and labeling high-definition (HD) maps accurately and efficiently. Today, the process of creating HD maps requires a lot of human input, which takes time and is prone to errors. In this paper, we propose a novel method capable of generating labelled HD maps from raw sensor data. We implemented and tested our methods on several urban scenarios using data collected from our test vehicle. The results show that the pro-posed deep learning based method can produce highly accurate HD maps. This approach speeds up the process of building and labeling HD maps, which can make meaningful contribution to the deployment of autonomous vehicle.