论文标题
学习使用弱标记的数据从汽车加速传感器中估算驾驶员的嗜睡
Learning to Estimate Driver Drowsiness from Car Acceleration Sensors using Weakly Labeled Data
论文作者
论文摘要
本文介绍了从汽车加速传感器信号中估算驾驶员嗜睡的学习任务。由于即使驾驶员本身也无法及时感知自己的嗜睡，除非他们使用繁重的侵入性传感器，否则为每个时间戳获得标记的培训数据并不是现实的目标。为了解决这个困难，我们将任务作为一个弱监督的学习。我们只需要为每次完整的旅行添加标签，而不是独立的每个时间戳。通过假设驾驶员嗜睡的某些方面随着时间的流逝而增加，我们制定了一种可以从如此弱标记的数据中学习的算法。我们得出一种可扩展的随机优化方法，作为实现算法的一种方式。实际驾驶数据集的数值实验证明了我们算法对基线方法的优势。
This paper addresses the learning task of estimating driver drowsiness from the signals of car acceleration sensors. Since even drivers themselves cannot perceive their own drowsiness in a timely manner unless they use burdensome invasive sensors, obtaining labeled training data for each timestamp is not a realistic goal. To deal with this difficulty, we formulate the task as a weakly supervised learning. We only need to add labels for each complete trip, not for every timestamp independently. By assuming that some aspects of driver drowsiness increase over time due to tiredness, we formulate an algorithm that can learn from such weakly labeled data. We derive a scalable stochastic optimization method as a way of implementing the algorithm. Numerical experiments on real driving datasets demonstrate the advantages of our algorithm against baseline methods.
