  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 will be made publicly
available at https://github.com/lynn-yu/DTCLMapper.
