Summary of Memfusionmap: Working Memory Fusion For Online Vectorized Hd Map Construction, by Jingyu Song et al.
MemFusionMap: Working Memory Fusion for Online Vectorized HD Map Construction
by Jingyu Song, Xudong Chen, Liupei Lu, Jie Li, Katherine A. Skinner
First submitted to arxiv on: 26 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary MemFusionMap is a novel temporal fusion model designed for online high-definition (HD) map construction in autonomous driving systems. Existing methods struggle with complex scenarios and occlusions, but MemFusionMap improves memory capacity to reason across frames using a working memory fusion module. Additionally, it incorporates a novel temporal overlap heatmap to inform the model about vehicle trajectory in Bird’s Eye View space. This design outperforms existing methods while maintaining scalability, achieving a maximum improvement of 5.4% in mean Average Precision (mAP) on open-source benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Autonomous driving systems need maps that show what’s around them. Right now, there are some good ways to make these maps, but they get stuck when things get complicated or hidden from view. We created a new way to make these maps using memories and special heatmaps. This helps the system understand where vehicles are going and how they fit together on the map. Our method is better than others at making these maps, with an improvement of 5.4%. You can find more information about our project online. |
Keywords
» Artificial intelligence » Mean average precision
