PriorMapNet：利用先验增强在线矢量化高清地图构建
PriorMapNet: Enhancing Online Vectorized HD Map Construction with Priors
AI总结：
PriorMapNet通过引入位置、结构及BEV特征先验，并设计PPS-Decoder、PF-Encoder和DMD交叉注意力，解决了参考点随机初始化导致的匹配不稳定问题，在nuScenes和Argoverse2上实现最先进性能。
摘要：
在线矢量化高清（HD）地图构建对于自动驾驶中后续的预测和规划任务至关重要。遵循MapTR范式，近期工作已取得显著成就。然而，主流方法中参考点随机初始化，导致预测与真值之间的匹配不稳定。为解决此问题，我们引入PriorMapNet，利用先验增强在线矢量化高清地图构建。我们提出了PPS-Decoder，为参考点提供位置和结构先验。这些先验参考点从数据集中的地图元素拟合而来，降低了学习难度并实现了稳定匹配。此外，我们提出了PF-Encoder，利用BEV特征先验增强图像到BEV的转换。另外，我们提出了DMD交叉注意力，分别沿多尺度和多样本解耦交叉注意力以实现高效性。我们提出的PriorMapNet在nuScenes和Argoverse2数据集上的在线矢量化高清地图构建任务中达到了最先进的性能。代码将很快公开发布。
英文摘要：
Online vectorized High-Definition (HD) map construction is crucial for subsequent prediction and planning tasks in autonomous driving. Following MapTR paradigm, recent works have made noteworthy achievements. However, reference points are randomly initialized in mainstream methods, leading to unstable matching between predictions and ground truth. To address this issue, we introduce PriorMapNet to enhance online vectorized HD map construction with priors. We propose the PPS-Decoder, which provides reference points with position and structure priors. Fitted from the map elements in the dataset, prior reference points lower the learning difficulty and achieve stable matching. Furthermore, we propose the PF-Encoder to enhance the image-to-BEV transformation with BEV feature priors. Besides, we propose the DMD cross-attention, which decouples cross-attention along multi-scale and multi-sample respectively to achieve efficiency. Our proposed PriorMapNet achieves state-of-the-art performance in the online vectorized HD map construction task on nuScenes and Argoverse2 datasets. The code will be released publicly soon.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
