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 contrastive pairs, we introduce an approach to analyze the overlap between traversals within a given dataset and generate subsidiary dataset splits following adjustable multi-traversal requirements. We train the same model supervised using a reduced set of single-traversal labeled data and self-supervised on a broader unlabeled set of data following our multi-traversal requirements, effectively implementing a semi-supervised approach. Our approach outperforms the supervised baseline across the board, both quantitatively in terms of the downstream tasks vectorized map perception performance and qualitatively in terms of segmentation in the principal component analysis (PCA) visualization of the BEV feature space.

翻译：自动驾驶汽车依赖地图信息理解周围环境，但离线高精地图的创建与维护成本高昂。更具可扩展性的替代方案是在线高精地图构建，该方法仅在训练阶段需要地图标注。为减少大规模训练标签的标注需求，自监督训练提供了另一种途径。本研究聚焦于改进矢量化在线高精地图构建模型中的潜在鸟瞰特征网格表示，通过在对比损失函数中强制对齐重叠鸟瞰特征网格间的地理空间一致性。为实现对比样本对的地理空间重叠，我们提出一种方法：分析给定数据集内轨迹间的重叠区域，并根据可调节的多轨迹需求生成子数据集。我们使用缩减后的单轨迹标注数据对同一模型进行监督训练，同时基于多轨迹需求对更广泛的未标注数据实施自监督训练，有效实现了半监督学习框架。本方法在各方面均优于监督基线：定量指标上，下游矢量地图感知任务性能全面提升；定性分析中，鸟瞰特征空间的主成分分析可视化结果展现出更优的语义分割效果。