Vectorized high-definition (HD) maps contain detailed information about surrounding road elements, which are crucial for various downstream tasks in modern autonomous driving vehicles, such as vehicle planning and control. Recent works have attempted to directly detect the vectorized HD map as a point set prediction task, resulting in significant improvements in detection performance. However, these approaches fail to analyze and exploit the inner-instance correlations between predicted points, impeding further advancements. To address these challenges, we investigate the utilization of inner-$\textbf{INS}$tance information for vectorized h$\textbf{IGH}$-definition mapping through $\textbf{T}$ransformers and introduce InsightMapper. This paper presents three novel designs within InsightMapper that leverage inner-instance information in distinct ways, including hybrid query generation, inner-instance query fusion, and inner-instance feature aggregation. Comparative experiments are conducted on the NuScenes dataset, showcasing the superiority of our proposed method. InsightMapper surpasses previous state-of-the-art (SOTA) methods by 5.78 mAP and 5.12 TOPO, which assess topology correctness. Simultaneously, InsightMapper maintains high efficiency during both training and inference phases, resulting in remarkable comprehensive performance. The project page for this work is available at https://tonyxuqaq.github.io/InsightMapper/ .

翻译：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多种下游任务（如车辆规划与控制）至关重要。近期研究尝试将矢量化高清地图检测直接视为点集预测任务，从而显著提升了检测性能。然而，这些方法未能分析与利用预测点之间的实例内部相关性，阻碍了进一步发展。为解决上述挑战，我们通过Transformer探究实例内部信息在矢量化高清地图构建中的应用，并提出了InsightMapper。本文在InsightMapper中设计了三种创新方法，以不同方式利用实例内部信息，包括混合查询生成、实例内部查询融合及实例内部特征聚合。在NuScenes数据集上的对比实验展示了所提方法的优越性。InsightMapper在平均精度（mAP）和拓扑正确性评估指标（TOPO）上分别超越此前最先进方法5.78和5.12。同时，InsightMapper在训练与推理阶段均保持高效性，展现出卓越的综合性能。该工作的项目页面可通过https://tonyxuqaq.github.io/InsightMapper/ 访问。