学习使用弱标记数据从汽车加速传感器估计驾驶员是否有睡意(cs.LG)---Donuts_choco

本文的学习任务为从汽车加速传感器的信号中估计驾驶员是否有睡意。由于即使是驾驶员自己也无法及时察觉到自己有睡意，除非他们使用繁琐的侵入性传感器，因此想要使每一个时间戳均获取到有标签的训练数据并不现实。为了应对这一困难，我们把任务制定成一个弱监督学习。我们只需要为每个完整行程添加标签，而不是为每个时间戳单独添加标签。假设由于疲劳而产生的某些影响因子（它们使驾驶员有睡意）随着时间的推移而增加，我们制定了一种算法，可以从这种弱标签数据中学习。我们推导出一种可扩展的随机优化方法，将其作为实现算法的一种方式。对实际驱动数据集进行数值实验，证明了我们算法相对于基线方法具有优势。

原文标题：Learning to Estimate Driver Drowsiness from Car Acceleration Sensors using Weakly Labeled Data

原文：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.

原文作者：Takayuki Katsuki, Kun Zhao, Takayuki Yoshizumi

原文地址：http://arxiv.org/abs/2005.05898