arXiv 1408.55732014-08-24cs.HC

使用时间序列分析测量认知干扰对驾驶性能的影响

Measuring the impact of cognitive distractions on driving performance using time series analysis

Matias Garcia-Constantino, Paolo Missier, Phil Blytheand Amy Weihong Guo

备注：: IEEE ITS conference, 2014

摘要：

使用当前的传感技术，通过车辆传感器和驾驶员生理传感器（心率、呼吸率、皮肤温度等）的组合，可以获得大量驾驶会话数据。我们的假设是，应该可以利用驾驶过程中多个传感器产生的时间序列的组合，以便（i）学习正常驾驶行为的模型，以及（ii）使用这些模型实时检测与规范的重要和潜在危险的偏差，从而能够生成适当的警报。至关重要的是，我们认为，这些模型和干预措施应该而且可以为每个驾驶员量身定制。作为实现这一目标的第一步，在本文中，我们提出了基于简单时间序列分析评估认知注意力分散对驾驶员影响的技术。我们已经在一个丰富的驾驶会议数据集上测试了我们的方法，该数据集在一个专业模拟器中进行，涉及一组志愿者司机。每个会话包括不同类型的认知干扰，并导致来自各种车载传感器以及驾驶员佩戴的传感器的多个时间序列。至关重要的是，每个司机还记录了一个没有分心的初始会话。在我们的模型中，这样的初始会话提供了基线时间序列，使得有可能定量评估驾驶员在分心条件下的表现。

英文摘要：

Using current sensing technology, a wealth of data on driving sessions is potentially available through a combination of vehicle sensors and drivers' physiology sensors (heart rate, breathing rate, skin temperature, etc.). Our hypothesis is that it should be possible to exploit the combination of time series produced by such multiple sensors during a driving session, in order to (i) learn models of normal driving behaviour, and (ii) use such models to detect important and potentially dangerous deviations from the norm in real-time, and thus enable the generation of appropriate alerts. Crucially, we believe that such models and interventions should and can be personalised and tailor-made for each individual driver. As an initial step towards this goal, in this paper we present techniques for assessing the impact of cognitive distraction on drivers, based on simple time series analysis. We have tested our method on a rich dataset of driving sessions, carried out in a professional simulator, involving a panel of volunteer drivers. Each session included a different type of cognitive distraction, and resulted in multiple time series from a variety of on-board sensors as well as sensors worn by the driver. Crucially, each driver also recorded an initial session with no distractions. In our model, such initial session provides the baseline times series that make it possible to quantitatively assess driver performance under distraction conditions.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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英文摘要：

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