Vectorized high-definition (HD) maps contain detailed information about surrounding road elements, which are crucial for various downstream tasks in modern autonomous vehicles, such as motion planning and vehicle control. Recent works attempt to directly detect the vectorized HD map as a point set prediction task, achieving notable detection performance improvements. However, these methods usually overlook and fail to analyze the important inner-instance correlations between predicted points, impeding further advancements. To address this issue, we investigate the utilization of inner-instance information for vectorized high-definition mapping through transformers, and propose a powerful system named $\textbf{InsMapper}$, which effectively harnesses inner-instance information with three exquisite designs, including hybrid query generation, inner-instance query fusion, and inner-instance feature aggregation. The first two modules can better initialize queries for line detection, while the last one refines predicted line instances. InsMapper is highly adaptable and can be seamlessly modified to align with the most recent HD map detection frameworks. Extensive experimental evaluations are conducted on the challenging NuScenes and Argoverse 2 datasets, where InsMapper surpasses the previous state-of-the-art method, demonstrating its effectiveness and generality. The project page for this work is available at https://tonyxuqaq.github.io/InsMapper/ .

翻译：摘要：矢量化高清地图包含周围道路元素的详细信息，对于现代自动驾驶车辆中的运动规划、车辆控制等下游任务至关重要。近年来，相关研究尝试将矢量化高清地图检测视为点集预测任务，取得了显著的检测性能提升。然而，现有方法通常忽略并缺乏对预测点之间重要实例内关联的分析，制约了进一步发展。为解决该问题，我们通过Transformer探索实例内信息在矢量化高清地图中的应用，提出名为$\textbf{InsMapper}$的强效系统。该系统通过混合查询生成、实例内查询融合及实例内特征聚合三种精巧设计，有效利用了实例内信息。前两个模块能够更优地初始化线检测查询，而最后一个模块则对预测的线实例进行精细化优化。InsMapper具有高度适应性，可无缝适配至最新HD地图检测框架。在具有挑战性的NuScenes和Argoverse 2数据集上的大量实验表明，InsMapper超越了先前最先进方法，验证了其有效性与泛化能力。该工作的项目页面可访问https://tonyxuqaq.github.io/InsMapper/。