用人类操作数据训练模型，减少自动驾驶系统误触发接管
Predicting Human Behavior in Autonomous Systems: A Collaborative Machine Teaching Approach for Reducing Transfer of Control Events
- 基于人类交互数据训练LSTM模型预测行为
- 非专家数据也能有效降低73%的非必要接管事件
- 适合工业自动化与人机协作场景
随着自主系统在各行业的广泛应用，有效的故障处理策略对保障可靠性与效率至关重要。传统的控制权移交（ToC）在非关键情况下常被误触发。为此，我们提出一种数据驱动方法，利用人类交互数据训练AI模型，提前识别问题或协助用户解决。通过模拟工业真空清洁器的交互工具收集数据，构建了基于LSTM的用户行为预测模型。结果表明，即使使用非专家数据，也能有效训练出减少不必要的ToC事件的模型，提升系统的鲁棒性。该方法展示了AI直接学习人类问题解决行为的潜力，可与传感器数据结合，改善工业自动化与人机协作。
原文摘要 · Abstract (English)
As autonomous systems become integral to various industries, effective strategies for fault handling are essential to ensure reliability and efficiency. Transfer of Control (ToC), a traditional approach for interrupting automated processes during faults, is often triggered unnecessarily in non-critical situations. To address this, we propose a data-driven method that uses human interaction data to train AI models capable of preemptively identifying and addressing issues or assisting users in resolution. Using an interactive tool simulating an industrial vacuum cleaner, we collected data and developed an LSTM-based model to predict user behavior. Our findings reveal that even data from non-experts can effectively train models to reduce unnecessary ToC events, enhancing the system's robustness. This approach highlights the potential of AI to learn directly from human problem-solving behaviors, complementing sensor data to improve industrial automation and human-AI collaboration.
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