DAMap: Distance-aware MapNet for High Quality HD Map Construction
Jinpeng Dong, Chen Li, Yutong Lin, Jingwen Fu, Sanping Zhou, Nanning Zheng

TL;DR
DAMap introduces a novel approach for high-quality HD map construction in autonomous driving by addressing task misalignment issues with specialized loss functions and attention mechanisms, leading to improved performance on key benchmarks.
Contribution
The paper proposes DAMap, a new method with Distance-aware Focal Loss, Task Modulated Deformable Attention, and a Hybrid Loss Scheme to enhance HD map prediction accuracy.
Findings
Consistent performance improvements on NuScenes and Argoverse2 benchmarks.
Effective handling of one-to-many matching issues in HD map prediction.
Robustness across different metrics, backbones, and training schedules.
Abstract
Predicting High-definition (HD) map elements with high quality (high classification and localization scores) is crucial to the safety of autonomous driving vehicles. However, current methods perform poorly in high quality predictions due to inherent task misalignment. Two main factors are responsible for misalignment: 1) inappropriate task labels due to one-to-many matching queries sharing the same labels, and 2) sub-optimal task features due to task-shared sampling mechanism. In this paper, we reveal two inherent defects in current methods and develop a novel HD map construction method named DAMap to address these problems. Specifically, DAMap consists of three components: Distance-aware Focal Loss (DAFL), Hybrid Loss Scheme (HLS), and Task Modulated Deformable Attention (TMDA). The DAFL is introduced to assign appropriate classification labels for one-to-many matching samples. The…
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