VI-Map: Infrastructure-Assisted Real-Time HD Mapping for Autonomous Driving
Yuze He, Chen Bian, Jingfei Xia, Shuyao Shi, Zhenyu Yan, Qun Song, Guoliang Xing
Abstract
HD map is a key enabling technology towards fully autonomous driving. We propose VI-Map, the first system that leverages roadside infrastructure to enhance real-time HD mapping for autonomous driving. The core concept of VI-Map is to exploit the unique cumulative observations made by roadside infrastructure to build and maintain an accurate and current HD map. This HD map is then fused with on-vehicle HD maps in real time, resulting in a more comprehensive and up-to-date HD map. By extracting concise bird-eye-view features from infrastructure observations and utilizing vectorized map representations, VI-Map incurs low compute and communication overhead. We conducted end-to-end evaluations of VI-Map on a real-world testbed and a simulator. Experiment results show that VI-Map can construct decentimeter-level (up to 0.3 m) HD maps and achieve real-time (up to a delay of 42 ms) map fusion between driving vehicles and roadside infrastructure. This represents a significant improvement of 2.8× and 3× in map accuracy and coverage compared to the state-of-the-art online HD mapping approaches. A video demo of VI-Map on our real-world testbed is available at https://youtu.be/p2RO65R5Ezg.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Your agent calls
Luneget_paper_fulltext
Free to start. No credit card required.
Terminal
Install the CLIlune papers fulltext be79891a-ea5a-4f57-b7fd-3c702d79794aCited by top-tier papers5
- AutoIOT: LLM-Driven Automated Natural Language Programming for AIoT ApplicationsLeming Shen, Qiang Yang, Yuanqing Zheng, Mo LiMobiCom 2025 · 14 citations
- BlinkBud: Detecting Hazards from Behind via Sampled Monocular 3D Detection on a Single EarbudYunzhe Li, Jiajun Yan, Yuzhou Wei, Kechen Liu et al.UbiComp 2026
- UrgenGo: Urgency-Aware Transparent GPU Kernel Launching for Autonomous DrivingHanqi Zhu, Wuyang Zhang, Xinran Zhang, Ziyang Tao et al.MobiCom 2025
- Towards Real-Time Defense against Object-Based LiDAR Attacks in Autonomous DrivingYan Zhang, Zihao Liu, Yi Zhu, Chenglin MiaoCCS 2025
- Towards White-Box Deep Wireless SensingXie Zhang, Yina Wang, Chenshu WuUbiComp 2026
Builds on8
- Large Scale Interactive Motion Forecasting for Autonomous Driving : The Waymo Open Motion DatasetScott Ettinger, Shuyang Cheng, Benjamin Caine, Chenxi Liu et al.ICCV 2021 · 817 citations
- VectorMapNet: End-to-end Vectorized HD Map LearningYicheng Liu, Tianyuan Yuan, Yue Wang, Yilun Wang et al.ICML 2023 · 332 citations
- Structured Bird's-Eye-View Traffic Scene Understanding from Onboard ImagesYigit Baran Can, Alexander Liniger, Danda Pani Paudel, Luc Van GoolICCV 2021 · 147 citations
- VIPS: real-time perception fusion for infrastructure-assisted autonomous drivingShuyao Shi, Jiahe Cui, Zhehao Jiang, Zhenyu Yan et al.MobiCom 2022 · 126 citations
- DAGMapper: Learning to Map by Discovering Lane TopologyNamdar Homayounfar, Justin Liang, Wei-Chiu Ma, Jack Fan et al.ICCV 2019 · 104 citations
Related papers
- VILAM: Infrastructure-assisted 3D Visual Localization and Mapping for Autonomous DrivingJiahe Cui, Shuyao Shi, Yuze He, Jianwei Niu et al.NSDI 2024 · 17 citations
- VI-Planning: Infrastructure-Assisted Real-Time Planning Optimization for Autonomous DrivingYang Lu, Jie Wang, Xiaoyun Dong, Ziyao Huang et al.MobiCom 2025 · 1 citation
- VI-eye: semantic-based 3D point cloud registration for infrastructure-assisted autonomous drivingYuze He, Li Ma, Zhehao Jiang, Yi Tang et al.MobiCom 2021 · 76 citations
- RTMap: Real-Time Recursive Mapping with Change Detection and LocalizationYuheng Du, Sheng Yang, Lingxuan Wang, Zhenghua Hou et al.ICCV 2025 · 2 citations
- PivotNet: Vectorized Pivot Learning for End-to-end HD Map ConstructionWenjie Ding, Limeng Qiao, Xi Qiu, Chi ZhangICCV 2023 · 119 citations
