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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

A novel end-to-end framework for consistent online HD vectorized map construction, which jointly performs map instance tracking and short-term prediction, and outperforms state-of-the-art (SOTA) methods with good efficiency.

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 Module then integrates rasterized map information into track queries, improving temporal continuity. Finally, we propose a Short-Term Future Guidance module to forecast the immediate motion of map instances based on the stored history trajectories. These predicted future locations serve as hints for tracked instances to further avoid implausible predictions and keep temporal consistency. Extensive experiments on the nuScenes and Argoverse2 datasets demonstrate that our proposed method outperforms state-of-the-art (SOTA) methods with good efficiency.

PredMapNet: Future and Historical Reasoning for Consistent Online HD Vectorized Map Construction

TL;DR

A novel end-to-end framework for consistent online HD vectorized map construction, which jointly performs map instance tracking and short-term prediction, and outperforms state-of-the-art (SOTA) methods with good efficiency.

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 Module then integrates rasterized map information into track queries, improving temporal continuity. Finally, we propose a Short-Term Future Guidance module to forecast the immediate motion of map instances based on the stored history trajectories. These predicted future locations serve as hints for tracked instances to further avoid implausible predictions and keep temporal consistency. Extensive experiments on the nuScenes and Argoverse2 datasets demonstrate that our proposed method outperforms state-of-the-art (SOTA) methods with good efficiency.
Paper Structure (21 sections, 15 equations, 3 figures, 5 tables)

This paper contains 21 sections, 15 equations, 3 figures, 5 tables.

Table of Contents

  1. Introduction
  2. Related Works
  3. Online Vectorized HD Map Construction
  4. Temporal Modeling in Perspective & BEV Perception
  5. Methodology
  6. Architecture Overview
  7. Semantic-Aware Query Generator
  8. History Rasterized Map Memory
  9. History-Map Guidance Module
  10. Short-Term Future Guidance Module
  11. Training Loss
  12. Experiments
  13. Implementation Details
  14. Datasets and Evaluation Metrics
  15. Comparison with the State-of-the-art Methods
  16. ...and 6 more sections

Figures (3)

  • Figure 1: Comparison of several HD map construction methods: (a) Single-Frame, (b) Temporal Propagation, (c) PredMapNet (Ours) utilizes semantic-aware information and enables both historical & future reasoning for better query decoding.
  • Figure 2: The architecture pipeline of PredMapNet. At each frame, multi-view images are processed by a BEV encoder to extract perception features. The Semantic-Aware Query Generator \ref{['SAQG']} produces semantic-aware detection queries and rasterized map from BEV features. A History Rasterized Map Memory \ref{['memory']} is maintained to store instance-level segmentation masks over time. The History-Map Guidance Module \ref{['HMG']} refines track queries with historical geometric priors from memory. Simultaneously, the Short-Term Future Guidance Module \ref{['STFG']} predicts future polylines from historical trajectories and fuses them into track queries to guide query initialization in the next frame. Together, these modules enable temporally consistent and robust map instance construction across frames.
  • Figure 3: Qualitative visualization on nuScenes val set.