HisTrackMap: 通过历史地图追踪构建全局向量高精度地图
HisTrackMap: Global Vectorized High-Definition Map Construction via History Map Tracking
AI总结：
HisTrackMap通过历史地图追踪构建全局向量高精度地图，提升时间连续性和几何构造质量。
摘要：
作为自动驾驶系统的重要组成部分，高精度（HD）地图为自动驾驶场景提供丰富的精确环境信息；然而，现有方法主要依赖于基于查询的检测框架，直接建模地图元素或隐式地在时间上传播查询，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收集系统的稳定性和可靠性构成了重大挑战。为了解决这一限制，我们提出了一种新颖的端到端跟踪框架，用于全局地图构建，通过时间跟踪地图元素的历史轨迹。首先，设计了实例级历史栅格化地图表示，以显式存储先前的感知结果，可以以细粒度的方式控制和维护不同全局实例的历史信息。其次，我们在这个跟踪框架中引入了Map-轨迹先验融合模块，利用历史先验来改善时间的平滑性和连续性。第三，我们提出了一种全局视角度量标准，用于评估HD地图中时间几何构造的质量，填补了当前评估全局几何感知结果的度量标准的空白。在nuScenes和Argoverse2数据集上的大量实验表明，所提出的方法在单帧和时间度量指标上均优于现有最先进（SOTA）方法。项目页面可在：https://yj772881654.github.io/HisTrackMap上找到。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
