用空间关系与语义先验提升在线高精地图构建精度
RelMap: Enhancing Online Map Construction with Class-Aware Spatial Relation and Semantic Priors
- 引入类别感知的空间关系编码，显式建模地图元素间相对位置
- 通过专家混合机制根据类别概率路由特征，优化实例解码
- 兼容单帧与时序感知模型，在nuScenes和Argoverse 2上达顶尖性能
在线高精地图构建对自动驾驶系统规模化至关重要。尽管基于Transformer的方法已成为主流，但现有方法普遍忽略地图元素间的内在空间依赖与语义关联，限制了其准确性和泛化能力。为此，我们提出RelMap，一种端到端框架，显式建模空间关系与语义先验以增强在线高精地图构建。具体而言，引入类别感知的空间关系先验，利用可学习的类别感知关系编码器显式编码地图元素间的相对位置依赖；同时设计基于专家混合（Mixture-of-Experts）的语义先验，依据预测类别概率将特征路由至类别专属专家，优化实例特征解码。RelMap兼容单帧与时序感知骨干网络，在nuScenes和Argoverse 2数据集上均达到当前最优性能。
原文摘要 · Abstract (English)
Online high-definition (HD) map construction is crucial for scaling autonomous driving systems. While Transformer-based methods have become prevalent in online HD map construction, most existing approaches overlook the inherent spatial dependencies and semantic relationships among map elements, which constrains their accuracy and generalization capabilities. To address this, we propose RelMap, an end-to-end framework that explicitly models both spatial relations and semantic priors to enhance online HD map construction. Specifically, 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 design a Mixture-of-Experts-based Semantic Prior, which routes features to class-specific experts based on predicted class probabilities, refining instance feature decoding. RelMap 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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