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 pioneering unified 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 at https://github.com/pnnnnnnn/Uni-PrevPredMap.

翻译：安全性是自动驾驶系统的根本要求，这需要最大限度地整合可获取的先验信息。本研究论证了时序感知缓冲区与低成本高精地图本质上构成了在线矢量化高精地图构建的互补先验源。我们提出了Uni-PrevPredMap，这是一个开创性的统一框架，系统性地将历史预测与存在缺陷的高精地图相融合。我们的框架引入了一种三模态范式，在无先验、时序先验以及时序-地图融合三种模式下保持操作一致性。该三模态范式一方面使框架摆脱了对理想地图的假设依赖，同时确保了在地图存在与地图缺失场景下均具备鲁棒性能。此外，我们开发了一种基于瓦片索引的3D矢量化全局地图处理器，能够实现高效的3D先验数据更新、紧凑存储与实时检索。Uni-PrevPredMap在现有在线矢量化高精地图构建基准测试中，取得了地图缺失场景下的最先进性能。当提供存在缺陷的高精地图时，该框架展现出强大的容错先验融合能力，从实证角度证实了时序预测与不完美地图数据之间的协同互补性。代码发布于 https://github.com/pnnnnnnn/Uni-PrevPredMap。