MapGCLR: Geospatial Contrastive Learning of Representations for Online Vectorized HD Map Construction
Autonomous vehicles rely on map information to understand the world around them. However, the creation and maintenance of offline high-definition (HD) maps remains costly. A more scalable alternative lies in online HD map construction, which only requires map annotations at training time. To further reduce the need for annotating vast training labels, self-supervised training provides an alternative. This work focuses on improving the latent birds-eye-view (BEV) feature grid representation within a vectorized online HD map construction model by enforcing geospatial consistency between overlapping BEV feature grids as part of a contrastive loss function. To ensure geospatial overlap for co...
| Authors | Jonas Merkert, Alexander Blumberg, Jan-Hendrik Pauls et al. |
|---|---|
| Primary category | cs.RO |
| Published | 11 March 2026 |
| Last updated at source | 22 May 2026 |
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