Title: Uni-PrevPredMap: Extending PrevPredMap to a Unified Framework of Prior-Informed Modeling for Online Vectorized HD Map Construction
Primary area: applications to computer vision, audio, language, and other modalities
Keywords: online vectorized HD map construction, prior-informed modeling, unified framework

Abstract: Safety constitutes a foundational imperative for autonomous driving systems, necessitating maximal incorporation of accessible prior information. This study establishes that temporal perception buffers and cost-efficient high-definition (HD) maps inherently form complementary prior sources for online vectorized HD map construction. We present Uni-PrevPredMap, a unified prior-informed framework systematically integrating previous predictions with corrupted HD maps. Our framework introduces a tri-mode paradigm maintaining operational consistency across non-prior, temporal-prior, and temporal-map-fusion modes. This tri-mode paradigm simultaneously decouples the framework from ideal map assumptions while ensuring robust performance in both map-present and map-absent scenarios. Additionally, we develop a tile-indexed 3D vectorized global map processor enabling efficient 3D prior data refreshment, compact storage, and real-time retrieval. Uni-PrevPredMap achieves state-of-the-art map-absent performance across established online vectorized HD map construction benchmarks. When provided with corrupted HD maps, it exhibits robust capabilities in error-resilient prior fusion, empirically confirming the synergistic complementarity between temporal predictions and imperfect map data. Code is available in supplementary materials.

TL;DR: This paper introduces  a unified prior-informed framework to integrate two synergistic priors: previous predictions and corrupted HD maps.