High-Definition (HD) maps play a crucial role in autonomous vehicle navigation, complementing onboard perception sensors for improved accuracy and safety. Traditional HD map generation relies on dedicated mapping vehicles, which are costly and fail to capture real-time infrastructure changes. This paper presents HDMapLaneNet, a novel framework leveraging V2X communication and Scene Graph Generation to collaboratively construct a localized geometric layer of HD maps. The approach extracts lane centerlines from front-facing camera images, represents them as graphs, and transmits the data for global aggregation to the cloud via V2X. Preliminary results on the nuScenes dataset demonstrate superior association prediction performance compared to a state-of-the-art method.

翻译：高精地图在自动驾驶导航中发挥着关键作用，能够补充车载感知传感器以提升定位精度与行车安全。传统高精地图生成依赖专用测绘车辆，成本高昂且无法实时反映道路基础设施变化。本文提出HDMapLaneNet——一种基于V2X通信与场景图生成技术的新型框架，通过协同方式构建局部几何层高精地图。该方法从前向摄像头图像中提取车道中心线，将其表示为图结构数据，并通过V2X通信将数据传输至云端进行全局聚合。在nuScenes数据集上的初步实验表明，相较于现有先进方法，本框架在关联预测任务中展现出更优越的性能。