基于车路协同的视觉感知，构建实时高精矢量地图
A Benchmark for Vision-Centric HD Mapping by V2I Systems
- 利用车路协同摄像头数据，端到端生成矢量高精地图
- 在真实场景下实现毫秒级推理速度，支持车载部署
- 适合自动驾驶地图构建与车路协同系统研究者
自动驾驶因缺乏全局视野和矢量高精地图的语义信息而面临安全挑战。通过车路通信（V2I），路边摄像头可显著扩展地图感知范围。然而，当前尚无真实世界数据集支持车载环境下车路协同的高精地图矢量化研究。为此，我们发布了一个真实世界数据集，包含车辆与路边基础设施协同采集的摄像头画面，并提供人工标注的高精地图元素。同时，提出一种面向视觉的端到端神经框架V2I-HD，用于构建矢量地图。为降低计算开销并实现在自动驾驶车辆上的部署，引入方向解耦自注意力机制。大量实验表明，该方法在真实数据集上具备优异的实时推理性能；定性结果也显示其在复杂多变场景中具有稳定、鲁棒且低成本的地图构建能力。作为基准，源代码与数据集已开源至OneDrive供进一步研究。
原文摘要 · Abstract (English)
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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