Temporal information plays a pivotal role in Bird's-Eye-View (BEV) driving scene understanding, which can alleviate the visual information sparsity. However, the indiscriminate temporal fusion method will cause the barrier of feature redundancy when constructing vectorized High-Definition (HD) maps. In this paper, we revisit the temporal fusion of vectorized HD maps, focusing on temporal instance consistency and temporal map consistency learning. To improve the representation of instances in single-frame maps, we introduce a novel method, DTCLMapper. This approach uses a dual-stream temporal consistency learning module that combines instance embedding with geometry maps. In the instance embedding component, our approach integrates temporal Instance Consistency Learning (ICL), ensuring consistency from vector points and instance features aggregated from points. A vectorized points pre-selection module is employed to enhance the regression efficiency of vector points from each instance. Then aggregated instance features obtained from the vectorized points preselection module are grounded in contrastive learning to realize temporal consistency, where positive and negative samples are selected based on position and semantic information. The geometry mapping component introduces Map Consistency Learning (MCL) designed with self-supervised learning. The MCL enhances the generalization capability of our consistent learning approach by concentrating on the global location and distribution constraints of the instances. Extensive experiments on well-recognized benchmarks indicate that the proposed DTCLMapper achieves state-of-the-art performance in vectorized mapping tasks, reaching 61.9% and 65.1% mAP scores on the nuScenes and Argoverse datasets, respectively. The source code is available at https://github.com/lynn-yu/DTCLMapper.

翻译：时序信息在鸟瞰图驾驶场景理解中起着关键作用，能够缓解视觉信息的稀疏性问题。然而，在构建矢量化高精地图时，不加区分的时序融合方法会导致特征冗余障碍。本文重新审视矢量化高精地图的时序融合问题，重点关注时序实例一致性学习与时序地图一致性学习。为提升单帧地图中实例的表征能力，我们提出一种新方法DTCLMapper。该方法采用双流时序一致性学习模块，将实例嵌入与几何地图相结合。在实例嵌入组件中，本方法融合了时序实例一致性学习，确保从矢量点及从点聚合的实例特征均保持一致性。通过引入矢量点预选模块以提升各实例矢量点的回归效率。随后，基于对比学习对预选模块获得的聚合实例特征进行时序一致性约束，其中正负样本依据位置与语义信息进行选取。几何映射组件引入了基于自监督学习设计的地图一致性学习。该组件通过关注实例的全局位置与分布约束，增强了本一致性学习方法的泛化能力。在公认基准测试上的大量实验表明，所提出的DTCLMapper在矢量化建图任务中取得了最先进的性能，在nuScenes和Argoverse数据集上分别达到61.9%和65.1%的mAP分数。源代码公开于https://github.com/lynn-yu/DTCLMapper。