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：自动驾驶车辆依赖地图信息来理解周围环境。然而，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在于在线高精地图构建，其仅在训练时需要地图标注。为进一步减少对海量训练标注的需求，自监督训练提供了一种替代方案。本研究致力于通过强制重叠鸟瞰图特征网格之间的地理空间一致性作为对比损失函数的一部分，来改进矢量化在线高精地图构建模型中的潜在鸟瞰图特征网格表征。为确保对比对具有地理空间重叠，我们引入了一种方法来分析给定数据集中不同轨迹间的重叠区域，并根据可调的多轨迹要求生成辅助数据集划分。我们使用减少的单轨迹标注数据集对同一模型进行监督训练，并依据我们的多轨迹要求在更广泛的未标注数据集上进行自监督训练，从而有效实现了一种半监督方法。我们的方法在各项指标上均优于监督基线，无论是在下游任务矢量化地图感知性能的定量评估方面，还是在鸟瞰图特征空间主成分分析可视化中分割效果的定性评估方面。