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Detecting driver distraction.

机译：检测驾驶员分心。

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摘要

The increasing use of in-vehicle information systems (IVISs), such as navigation devices and MP3 players, can jeopardize safety by introducing distraction into driving. One way to address this problem is to develop distraction mitigation systems, which adapt IVIS functions according to driver state. In such a system, correctly identifying driver distraction is critical, which is the focus of this dissertation. Visual and cognitive distractions are two major types of distraction that interfere with driving most compared with other types. Visual and cognitive distraction can occur individually or in combination. The research gaps in detecting driver distraction are that the interactions of visual and cognitive distractions have not been well studied and that no accurate algorithm/strategy has been developed to detect visual, cognitive, or combined distraction.;To bridge these gaps, the dissertation fulfilled three specific aims. The first aim demonstrated the layered algorithm developed based on data mining methods could improve the detection of cognitive distraction from my previous studies. The second aim developed estimation algorithms for visual distraction and demonstrated a strong relationship of the estimated distraction with the increased risk of real crashes using the naturalistic data. The third objective examined the interaction of visual and cognitive distractions and developed an effective strategy to identify combined distraction. Together these aims suggest that driver distraction can be detected from performance indicators using appropriate quantitative methods. Data mining techniques represent a promising category of methods to construct such detection algorithms. When combined in a sequential way, visual distraction dominates the effects of distraction while cognitive distraction reduces the overall impairments of distraction on driver performance. Therefore, it is not necessary to detect cognitive distraction if visual distraction is present. These approaches to detecting distraction can be also generalized to estimate other performance impairments, such as driver fatigue.
机译：诸如导航设备和MP3播放器之类的车载信息系统（IVIS）的越来越多的使用，可能会将注意力分散到驾驶中，从而危害安全性。解决此问题的一种方法是开发减轻干扰的系统，该系统可根据驾驶员的状态适应IVIS功能。在这样的系统中，正确识别驾驶员的注意力至关重要，这是本文的重点。与其他类型的干扰相比，视觉和认知干扰是干扰驾驶的两种主要类型。视觉和认知分心可以单独发生或组合发生。在检测驾驶员注意力分散方面的研究差距是，对视觉和认知分散的相互作用尚未进行深入研究，并且尚未开发出用于检测视觉，认知或组合性分散注意力的精确算法/策略。三个具体目标。第一个目标证明了基于数据挖掘方法开发的分层算法可以改善我以前的研究中对认知干扰的检测。第二个目标是开发用于视觉分散的估计算法，并使用自然数据证明估计分散的注意力与实际坠机风险增加的强烈关系。第三个目标检查了视觉和认知干扰的相互作用，并开发了一种有效的策略来识别混合干扰。这些目标共同表明，可以使用适当的定量方法从性能指标中检测驾驶员的注意力。数据挖掘技术代表了构建此类检测算法的一种有前途的方法。当以顺序方式组合时，视觉分神在分心的效果中占主导地位，而认知分心减少了分心对驾驶员性能的总体损害。因此，如果存在视觉干扰，则不必检测认知干扰。这些检测分心的方法也可以被概括为估计其他性能损害，例如驾驶员疲劳。

著录项

  • 作者

    Liang, Yulan.;

  • 作者单位

    The University of Iowa.;

  • 授予单位 The University of Iowa.;
  • 学科 Engineering Industrial.
  • 学位 Ph.D.
  • 年度 2009
  • 页码 150 p.
  • 总页数 150
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 一般工业技术;
  • 关键词

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