  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.
