计算机科学 ›› 2022, Vol. 49 ›› Issue (11): 170-178.doi: 10.11896/jsjkx.211000040
张宇欣1,2, 陈益强2
ZHANG Yu-xin1,2, CHEN Yi-qiang2
摘要： 近年来,道路交通事故的发生逐年增加。驾驶员注意力不集中是造成交通事故的主要原因之一。该项工作利用多源数据来检测驾驶员是否注意力分散。由于每个数据源能为其余数据源提供一定的信息,即多源数据之间的关联性较强,因此对不同来源的数据进行同等处理或对多源特征进行简单的连接整合会导致特征耦合度高,不能保证挖掘任务的有效性。另外,注意力分散驾驶可能受到许多因素的影响,当已知类别的集合中不存在驾驶员注意力分散的类型时,常见的有监督方法可能会导致分类错误。对此,提出了一种基于多尺度特征融合的驾驶员注意力分散检测方法(Multi-Scale Feature Fusion Network,MSFFN)。首先,通过多个嵌入式子网络从多源数据中学习低维表示。然后,提出一种多尺度特征融合方法,从时空关联性的角度聚合这些特征表示,降低多源特征之间的耦合度。最后,设计基于卷积长短期记忆的编解码模型进行无监督检测。在训练阶段,模型仅对正常驾驶实例进行训练,确定正常数据的一类分类边界。在检测阶段,计算模型重构误差并将其作为每一个测试数据的评分,从而做出细粒度的检测决策。该方法在公开的驾驶员行为数据集上取得了很好的实验结果,优于现有方法。
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