To enhance perception performance in complex and extensive scenarios within the realm of autonomous driving, there has been a noteworthy focus on temporal modeling, with a particular emphasis on streaming methods. The prevailing trend in streaming models involves the utilization of stream queries for the propagation of temporal information. Despite the prevalence of this approach, the direct application of the streaming paradigm to the construction of vectorized high-definition maps (HD-maps) fails to fully harness the inherent potential of temporal information. This paper introduces the Stream Query Denoising (SQD) strategy as a novel approach for temporal modeling in high-definition map (HD-map) construction. SQD is designed to facilitate the learning of temporal consistency among map elements within the streaming model. The methodology involves denoising the queries that have been perturbed by the addition of noise to the ground-truth information from the preceding frame. This denoising process aims to reconstruct the ground-truth information for the current frame, thereby simulating the prediction process inherent in stream queries. The SQD strategy can be applied to those streaming methods (e.g., StreamMapNet) to enhance the temporal modeling. The proposed SQD-MapNet is the StreamMapNet equipped with SQD. Extensive experiments on nuScenes and Argoverse2 show that our method is remarkably superior to other existing methods across all settings of close range and long range. The code will be available soon.

翻译：在自动驾驶领域复杂且广阔的场景中，为增强感知性能，时间建模受到了显著关注，尤其是流式方法。流式模型的普遍趋势是采用流式查询来传播时间信息。尽管该方法较为常见，但将流式范式直接应用于矢量高清地图（HD-map）的构建，未能充分挖掘时间信息的内在潜力。本文提出流式查询去噪（SQD）策略，作为高清地图（HD-map）构建中时间建模的一种新颖方法。SQD旨在促进流式模型中地图元素间时间一致性的学习。该方法通过去除前帧真实信息添加噪声后扰动的查询噪声，重构当前帧的真实信息，从而模拟流式查询的预测过程。SQD策略可应用于各类流式方法（如StreamMapNet），以增强时间建模能力。所提出的SQD-MapNet即融合SQD的StreamMapNet。在nuScenes和Argoverse2上的大量实验表明，我们的方法在近距和远距所有设置下均显著优于现有方法。代码即将开源。