As an essential component of autonomous driving systems, high-definition (HD) maps provide rich and precise environmental information for auto-driving scenarios; however, existing methods, which primarily rely on query-based detection frameworks to directly model map elements or implicitly propagate queries over time, often struggle to maintain consistent temporal perception outcomes. These inconsistencies pose significant challenges to the stability and reliability of real-world autonomous driving and map data collection systems. To address this limitation, we propose a novel end-to-end tracking framework for global map construction by temporally tracking map elements' historical trajectories. Firstly, instance-level historical rasterization map representation is designed to explicitly store previous perception results, which can control and maintain different global instances' history information in a fine-grained way. Secondly, we introduce a Map-Trajectory Prior Fusion module within this tracking framework, leveraging historical priors for tracked instances to improve temporal smoothness and continuity. Thirdly, we propose a global perspective metric to evaluate the quality of temporal geometry construction in HD maps, filling the gap in current metrics for assessing global geometric perception results. Substantial experiments on the nuScenes and Argoverse2 datasets demonstrate that the proposed method outperforms state-of-the-art (SOTA) methods in both single-frame and temporal metrics. The project page is available at: https://yj772881654.github.io/HisTrackMap.

翻译：作为自动驾驶系统的关键组成部分，高精地图为自动驾驶场景提供丰富且精确的环境信息；然而，现有方法主要依赖基于查询的检测框架直接建模地图元素或随时间隐式传播查询，往往难以保持一致的时序感知结果。这些不一致性对现实世界自动驾驶和地图数据采集系统的稳定性与可靠性构成了重大挑战。为克服这一局限，我们提出了一种新颖的端到端跟踪框架，通过时序跟踪地图元素的历史轨迹来实现全局地图构建。首先，设计了实例级历史栅格化地图表示方法，显式存储先前的感知结果，从而能以细粒度方式控制并维护不同全局实例的历史信息。其次，在该跟踪框架中引入了地图-轨迹先验融合模块，利用被跟踪实例的历史先验信息来提升时序平滑性与连续性。再次，我们提出了一种全局视角度量标准，用于评估高精地图中时序几何构建的质量，填补了当前评估全局几何感知结果的度量空白。在 nuScenes 和 Argoverse2 数据集上进行的大量实验表明，所提方法在单帧指标和时序指标上均优于当前最先进方法。项目页面位于：https://yj772881654.github.io/HisTrackMap。