arXiv:2608.12600v1 Announce Type: new Abstract: A critical challenge in deploying online HD map construction systems to real-world scenarios is the scarcity of labeled training data, which limits model generalization in
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在线高精地图（HD Map）构建系统在部署到真实世界场景时面临一个关键瓶颈：标注训练数据的稀缺。这一问题直接限制了模型在多样化道路环境中的泛化表现。针对这一挑战，一篇新发表于 arXiv 的论文提出了名为 PseudoMapLabeler 的方法，核心思路是利用半监督学习框架，通过置信度感知的方式自动生成伪标签，从而减少对人工标注的依赖。来源
从摘要披露的信息来看，该方法的关键创新在于「置信度感知」（Confidence-Aware）这一设计。与传统的伪标签生成方式不同，PseudoMapLabeler 并非对所有未标注样本一视同仁地赋予伪标签，而是根据模型预测的置信度水平进行筛选或加权，以降低错误伪标签对训练过程的负面影响。原文摘要仅提供了方法定位与动机概述，具体的技术架构、置信度度量方式以及实验验证细节，原文未提供。
这一研究方向与同期 arXiv 上多篇关注「可靠性」与「置信度」的论文形成了呼应。例如，一篇关于性别歧视检测的研究同样采用了标注者一致性与词元级置信度评分来提升系统可靠性；另一篇关于大语言模型自我反思的研究则通过对照消融实验，考察不确定性路由在推理增益中的实际作用。这些工作共同反映出当前机器学习社区对「模型输出可信度」问题的持续关注，而 PseudoMapLabeler 将这一思路引入自动驾驶在线建图领域，具有一定的场景针对性。来源 来源
arXiv:2608.12600v1 Announce Type: new Abstract: A critical challenge in deploying online HD map construction systems to real-world scenarios is the scarcity of labeled training data, which limits model generalization in diverse environments. To address this limitation, we propose a teacher-student semi-supervised learning (SSL) framework that generates high-quality pseudo-labels from unlabeled data through confidence-aware map refinement. Our approach first trains a teacher model on limited labeled data, then leverages Beta-distribution-based confidence maps to assess the reliability of predicted map elements across temporal observations. Unlike conventional filtering methods that discard entire elements, we introduce a spatial clipping technique that selectively preserves high-confidence regions while removing unreliable segments. The refined map elements serve as map priors that improve the teacher model's prediction accuracy on unlabeled data in a second pass. These enhanced predictions become pseudo-labels for training a student model from scratch, followed by fine-tuning on the original labeled data. Experimental results on the nuScenes dataset demonstrate that our teacher-student framework with refined pseudo-labels improves performance by +6.1 mAP under a low-label regime compared to training on labeled data alone, offering a practical solution to the labeled data scarcity problem in online HD map construction.