Temporal information is crucial for detecting occluded instances. Existing temporal representations have progressed from BEV or PV features to more compact query features. Compared to these aforementioned features, predictions offer the highest level of abstraction, providing explicit information. In the context of online vectorized HD map construction, this unique characteristic of predictions is potentially advantageous for long-term temporal modeling and the integration of map priors. This paper introduces PrevPredMap, a pioneering temporal modeling framework that leverages previous predictions for constructing online vectorized HD maps. We have meticulously crafted two essential modules for PrevPredMap: the previous-predictions-based query generator and the dynamic-position-query decoder. Specifically, the previous-predictions-based query generator is designed to separately encode different types of information from previous predictions, which are then effectively utilized by the dynamic-position-query decoder to generate current predictions. Furthermore, we have developed a dual-mode strategy to ensure PrevPredMap's robust performance across both single-frame and temporal modes. Extensive experiments demonstrate that PrevPredMap achieves state-of-the-art performance on the nuScenes and Argoverse2 datasets. Code will be available at https://github.com/pnnnnnnn/PrevPredMap.

翻译：时序信息对于检测被遮挡实例至关重要。现有时序表征已从BEV或PV特征发展到更紧凑的查询特征。与前述特征相比，预测结果提供了最高层级的抽象，能够传达显式信息。在在线矢量化高精地图构建的背景下，预测的这种独特特性可能对长期时序建模与地图先验的整合具有优势。本文提出了PrevPredMap——一种开创性的时序建模框架，该框架利用历史预测来构建在线矢量化高精地图。我们为PrevPredMap精心设计了两个核心模块：基于历史预测的查询生成器与动态位置查询解码器。具体而言，基于历史预测的查询生成器旨在分别编码历史预测中不同类型的信息，这些信息随后被动态位置查询解码器有效利用以生成当前预测。此外，我们开发了一种双模式策略，以确保PrevPredMap在单帧模式和时序模式下均能保持鲁棒性能。大量实验表明，PrevPredMap在nuScenes和Argoverse2数据集上实现了最先进的性能。代码将在https://github.com/pnnnnnnn/PrevPredMap 发布。