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/projects/InsightMapper .

翻译：矢量化高清地图包含周围道路元素的详细信息，这对现代自动驾驶车辆中规划与控制等下游任务至关重要。近期研究尝试将矢量化高清地图直接检测任务建模为点集预测，显著提升了检测性能。然而，现有方法未能分析与利用预测点之间的实例内关联，阻碍了进一步优化。为应对这一挑战，本文通过Transformer探究实例内信息在矢量化高清地图构建中的应用，并提出InsightMapper。本文在InsightMapper中设计了三项创新机制，分别从混合查询生成、实例内查询融合和实例内特征聚合三个不同维度利用实例内信息。基于NuScenes数据集的对比实验表明，所提方法性能优越：InsightMapper在平均精度（mAP）和拓扑正确性评估指标TOPO上分别超越此前最优方法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。项目页面见https://tonyxuqaq.github.io/projects/InsightMapper。