Class-agnostic motion prediction methods aim to comprehend motion within open-world scenarios, holding significance for autonomous driving systems. However, training a high-performance model in a fully-supervised manner always requires substantial amounts of manually annotated data, which can be both expensive and time-consuming to obtain. To address this challenge, our study explores the potential of semi-supervised learning (SSL) for class-agnostic motion prediction. Our SSL framework adopts a consistency-based self-training paradigm, enabling the model to learn from unlabeled data by generating pseudo labels through test-time inference. To improve the quality of pseudo labels, we propose a novel motion selection and re-generation module. This module effectively selects reliable pseudo labels and re-generates unreliable ones. Furthermore, we propose two data augmentation strategies: temporal sampling and BEVMix. These strategies facilitate consistency regularization in SSL. Experiments conducted on nuScenes demonstrate that our SSL method can surpass the self-supervised approach by a large margin by utilizing only a tiny fraction of labeled data. Furthermore, our method exhibits comparable performance to weakly and some fully supervised methods. These results highlight the ability of our method to strike a favorable balance between annotation costs and performance. Code will be available at https://github.com/kwwcv/SSMP.

翻译：类无关运动预测方法旨在理解开放世界场景中的运动，对自动驾驶系统具有重要意义。然而，在全监督方式下训练高性能模型始终需要大量人工标注数据，这既昂贵又耗时。为应对这一挑战，本研究探索了半监督学习（SSL）在类无关运动预测中的潜力。我们的SSL框架采用基于一致性的自训练范式，通过测试时推理生成伪标签，使模型能够从无标注数据中学习。为提升伪标签质量，我们提出了一种新颖的运动选择与再生模块，该模块能有效选择可靠伪标签并重新生成不可靠伪标签。此外，我们提出两种数据增强策略：时间采样和BEVMix，以促进SSL中的一致性正则化。在nuScenes数据集上的实验表明，我们的SSL方法仅需利用极少量标注数据，即可大幅超越自监督方法。同时，本方法性能可媲美弱监督及部分全监督方法。这些结果凸显了本方法在标注成本与性能之间实现良好平衡的能力。代码将发布于https://github.com/kwwcv/SSMP。