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.

该研究论文通过引入 SQD 策略作为一种新的时态建模方法，以提升自动驾驶中复杂场景感知性能，并且在构建高清地图方面展现出潜在的时间信息潜力。