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arXiv · 2503.23963

A Benchmark for Vision-Centric HD Mapping by V2I Systems

Miao Fan·Shanshan Yu·Shengtong Xu·Kun Jiang·Haoyi Xiong·Xiangzeng Liu
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Abstract

Autonomous driving faces safety challenges due to a lack of global perspective and the semantic information of vectorized high-definition (HD) maps. Information from roadside cameras can greatly expand the map perception range through vehicle-to-infrastructure (V2I) communications. However, there is still no dataset from the real world available for the study on map vectorization onboard under the scenario of vehicle-infrastructure cooperation. To prosper the research on online HD mapping for Vehicle-Infrastructure Cooperative Autonomous Driving (VICAD), we release a real-world dataset, which contains collaborative camera frames from both vehicles and roadside infrastructures, and provides human annotations of HD map elements. We also present an end-to-end neural framework (i.e., V2I-HD) leveraging vision-centric V2I systems to construct vectorized maps. To reduce computation costs and further deploy V2I-HD on autonomous vehicles, we introduce a directionally decoupled self-attention mechanism to V2I-HD. Extensive experiments show that V2I-HD has superior performance in real-time inference speed, as tested by our real-world dataset. Abundant qualitative results also demonstrate stable and robust map construction quality with low cost in complex and various driving scenes. As a benchmark, both source codes and the dataset have been released at OneDrive for the purpose of further study.

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Miao Fan, Shanshan Yu, Shengtong Xu, Kun Jiang, Haoyi Xiong, Xiangzeng Liu. 2025-03-31. A Benchmark for Vision-Centric HD Mapping by V2I Systems. https://arxiv.org/abs/2503.23963

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