PredMapNet: Future and Historical Reasoning for Consistent Online HD Vectorized Map Construction
Bo Lang, Nirav Savaliya, Zhihao Zheng, Jinglun Feng, Zheng-Hang Yeh, Mooi Choo Chuah

TL;DR
PredMapNet is an innovative framework for consistent online high-definition vectorized map construction in autonomous driving, integrating semantic-aware queries, historical map memory, and future motion prediction to enhance temporal stability and accuracy.
Contribution
It introduces a novel end-to-end approach combining semantic-aware query generation, explicit historical map memory, and short-term future prediction for improved map consistency.
Findings
Outperforms state-of-the-art methods on nuScenes and Argoverse2 datasets.
Achieves higher temporal consistency and accuracy in map construction.
Demonstrates good efficiency in online HD map updates.
Abstract
High-definition (HD) maps are crucial to autonomous driving, providing structured representations of road elements to support navigation and planning. However, existing query-based methods often employ random query initialization and depend on implicit temporal modeling, which lead to temporal inconsistencies and instabilities during the construction of a global map. To overcome these challenges, we introduce a novel end-to-end framework for consistent online HD vectorized map construction, which jointly performs map instance tracking and short-term prediction. First, we propose a Semantic-Aware Query Generator that initializes queries with spatially aligned semantic masks to capture scene-level context globally. Next, we design a History Rasterized Map Memory to store fine-grained instance-level maps for each tracked instance, enabling explicit historical priors. A History-Map Guidance…
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Taxonomy
TopicsData Management and Algorithms · Automated Road and Building Extraction · Autonomous Vehicle Technology and Safety
