# LMT-Net: Lane Model Transformer Network for Automated HD Mapping from Sparse Vehicle Observations

arXiv: https://arxiv.org/abs/2409.12409 · HTML: https://mlsanity.com/paper/2409.12409/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 30 references that are in the map.
- First posted on arXiv 19 Sep 2024.

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers](https://mlsanity.com/paper/2203.17270/) (2022)
- [Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D](https://mlsanity.com/paper/2008.05711/) (2020)
- [VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation](https://mlsanity.com/paper/2005.04259/) (2020)
- [Graph R-CNN for Scene Graph Generation](https://mlsanity.com/paper/1808.00191/) (2018)
- [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)
- [MapTRv2: An End-to-End Framework for Online Vectorized HD Map Construction](https://mlsanity.com/paper/2308.05736/) (2023)
- [StreamMapNet: Streaming Mapping Network for Vectorized Online HD Map Construction](https://mlsanity.com/paper/2308.12570/) (2023)
- [Structured Bird's-Eye-View Traffic Scene Understanding from Onboard Images](https://mlsanity.com/paper/2110.01997/) (2021)
- [Neural Map Prior for Autonomous Driving](https://mlsanity.com/paper/2304.08481/) (2023)
- [Online Vectorized HD Map Construction using Geometry](https://mlsanity.com/paper/2312.03341/) (2023)
- [HDMapGen: A Hierarchical Graph Generative Model of High Definition Maps](https://mlsanity.com/paper/2106.14880/) (2021)
- [MV-Map: Offboard HD-Map Generation with Multi-view Consistency](https://mlsanity.com/paper/2305.08851/) (2023)
- [Lane Graph Estimation for Scene Understanding in Urban Driving](https://mlsanity.com/paper/2105.00195/) (2021)
- [HD Map Generation from Noisy Multi-Route Vehicle Fleet Data on Highways with Expectation Maximization](https://mlsanity.com/paper/2305.02080/) (2023)
- [3D map creation using crowdsourced GNSS data](https://mlsanity.com/paper/2106.00107/) (2021)

## Built on by (most cited first, 2 of 2)

- [Inferring Driving Maps by Deep Learning-based Trail Map Extraction](https://mlsanity.com/paper/2505.10258/) (2025)
- [ARETE: Attention-based Rasterized Encoding for Topology Estimation using HSV-transformed Crowdsourced Vehicle Fleet Data](https://mlsanity.com/paper/2604.24353/) (2026)

## Abstract (arXiv)

In autonomous driving, High Definition (HD) maps provide a complete lane model that is not limited by sensor range and occlusions. However, the generation and upkeep of HD maps involves periodic data collection and human annotations, limiting scalability. To address this, we investigate automating the lane model generation and the use of sparse vehicle observations instead of dense sensor measurements. For our approach, a pre-processing step generates polylines by aligning and aggregating observed lane boundaries. Aligned driven traces are used as starting points for predicting lane pairs defined by the left and right boundary points. We propose Lane Model Transformer Network (LMT-Net), an encoder-decoder neural network architecture that performs polyline encoding and predicts lane pairs and their connectivity. A lane graph is formed by using predicted lane pairs as nodes and predicted lane connectivity as edges. We evaluate the performance of LMT-Net on an internal dataset that consists of multiple vehicle observations as well as human annotations as Ground Truth (GT). The evaluation shows promising results and demonstrates superior performance compared to the implemented baseline on both highway and non-highway Operational Design Domain (ODD).