RelMap: Enhancing Online Map Construction with Class-Aware Spatial Relation and Semantic Priors

• URL: http://arxiv.org/abs/2507.21567v1
• Date: Tue, 29 Jul 2025 07:58:52 GMT
• Title: RelMap: Enhancing Online Map Construction with Class-Aware Spatial Relation and Semantic Priors
• Authors: Tianhui Cai, Yun Zhang, Zewei Zhou, Zhiyu Huang, Jiaqi Ma,
• Abstract summary: We propose an end-to-end framework that enhances online map construction by incorporating spatial relations and semantic priors.<n>Our method is compatible with both single-frame and temporal perception backbones, achieving state-of-the-art performance on both the nuScenes and Argoverse 2 datasets.
• Score: 13.26774106477855
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Online high-definition (HD) map construction plays an increasingly important role in scaling autonomous driving systems. Transformer-based methods have become prevalent in online HD map construction; however, existing approaches often neglect the inherent spatial and semantic relationships among map elements, which limits their accuracy and generalization. To address this, we propose RelMap, an end-to-end framework that enhances online map construction by incorporating spatial relations and semantic priors. We introduce a Class-aware Spatial Relation Prior, which explicitly encodes relative positional dependencies between map elements using a learnable class-aware relation encoder. Additionally, we propose a Mixture-of-Experts (MoE)-based Semantic Prior, which routes features to class-specific experts based on predicted class probabilities, refining instance feature decoding. Our method is compatible with both single-frame and temporal perception backbones, achieving state-of-the-art performance on both the nuScenes and Argoverse 2 datasets.

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