用自监督学习解决助人行为预测数据少的难题
Self-Supervised Learning-Based Multimodal Prediction on Prosocial Behavior Intentions
- 利用已有生理与行为数据做自监督预训练
- 在小规模标注数据上微调，显著提升预测效果
- 适合智能驾驶与人机交互方向的研究者
人类状态检测与行为预测得益于机器学习和多模态感知技术的进展。然而，在交通场景中预测助人行为意图（如路上帮助他人）仍属研究空白。当前主要受限于缺乏大规模标注数据，小规模数据难以有效训练深度模型。为此，本文提出一种基于自监督学习的方法，利用现有生理与行为数据集中的多模态信息进行预训练，并在较小的人工标注助人行为数据集上微调，显著提升模型性能。该方法缓解了数据稀缺问题，为助人行为预测提供了更有效的基准，对智能车辆系统与人机交互具有重要价值。
原文摘要 · Abstract (English)
Human state detection and behavior prediction have seen significant advancements with the rise of machine learning and multimodal sensing technologies. However, predicting prosocial behavior intentions in mobility scenarios, such as helping others on the road, is an underexplored area. Current research faces a major limitation. There are no large, labeled datasets available for prosocial behavior, and small-scale datasets make it difficult to train deep-learning models effectively. To overcome this, we propose a self-supervised learning approach that harnesses multi-modal data from existing physiological and behavioral datasets. By pre-training our model on diverse tasks and fine-tuning it with a smaller, manually labeled prosocial behavior dataset, we significantly enhance its performance. This method addresses the data scarcity issue, providing a more effective benchmark for prosocial behavior prediction, and offering valuable insights for improving intelligent vehicle systems and human-machine interaction.
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