VectorMapNet: End-to-end Vectorized HD Map Learning

By Yicheng Liu, Yue Wang, Yilun Wang, Hang Zhao. International Conference on Machine Learning, 2022

360 citations recorded by the source corpus.

Autonomous driving systems require High-Definition (HD) semantic maps to navigate around urban roads. Existing solutions approach the semantic mapping problem by offline manual annotation, which suffers from serious scalability issues. Recent learning-based methods produce dense rasterized segmentation predictions to construct maps. However, these predictions do not include instance information of individual map elements and require heuristic post-processing to obtain vectorized maps. To tackle these challenges, we introduce an end-to-end vectorized HD map learning pipeline, termed VectorMapNet. VectorMapNet takes onboard sensor observations and predicts a sparse set of polylines in the bird's-eye view. This pipeline can explicitly model the spatial relation between map elements and generate vectorized maps that are friendly to downstream autonomous driving tasks. Extensive experiments show that VectorMapNet achieve strong map learning performance on both nuScenes and Argoverse2 dataset, surpassing previous state-of-the-art methods by 14.2 mAP and 14.6mAP. Qualitatively, VectorMapNet is capable of generating comprehensive maps and capturing fine-grained details of road geometry. To the best of our knowledge, VectorMapNet is the first work designed towards end-to-end vectorized map learning from onboard observations. Our project website is available at \url{https://tsinghua-mars-lab.github.io/vectormapnet/}.

Subfields: Computer Vision and Pattern Recognition, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/dee343cfff76e46a4b9845e765d13be28165df06

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Xinggang Wang
2. Wenyu Liu
3. Qian Zhang
4. Chang Huang
5. Bencheng Liao
6. Shaoyu Chen
7. Qing Xu
8. Bo Jiang

Citations in the corpus

• VADv2: End-to-End Vectorized Autonomous Driving via Probabilistic Planning (2024, background): “Vec-torMapNet [32] predicts vector map elements in an autoregressive manner.”
• MapTR: Structured Modeling and Learning for Online Vectorized HD Map Construction (2022, methodology): “VectorMapNet (Liu et al., 2022a) represents each map element as a point sequence.”
• VAD: Vectorized Scene Representation for Efficient Autonomous Driving (2023, neutral): “3.4).”
• DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving (2024, neutral): “End-to-end autonomous driving has gained significant attention in recent years due to advancements in perception models (detection [5, 17, 24, 42], tracking [54–56], online mapping [ 27, 28, 32], etc .)”