A Review on Measuring Affect with Practical Sensors to Monitor Driver Behavior

A Review on Measuring Affect with Practical Sensors to Monitor Driver Behavior
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DOI:
10.3390/safety5040072
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发表时间:
2019-10
期刊:
影响因子:
1.9
通讯作者:
K. Welch;C. Harnett;Yi-Ching Lee
K. Welch;C. Harnett;Yi-Ching Lee
中科院分区:
--
文献类型:
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作者:
K. Welch;C. Harnett;Yi-Ching Lee

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使用传感器监测驾驶员产生的信号是一种帮助更好地了解情绪如何导致不安全驾驶习惯的方法。对能够解释有意和无意信号的直观机器的需求对我们的现代世界至关重要。然而,在复杂的人机工作环境中,由于兼容性问题、噪声或实际限制,许多传感器将无法工作。本综述重点介绍了实用的传感器,这些传感器有可能为车辆操作员(如驾驶员、列车操作员、飞行员、驾驶员)提供可靠的监测和有意义的反馈,并可与现有的工作基础设施集成。如果信号表明驾驶员变得愤怒或压力大,这种对影响敏感的智能车辆可能会发出警报,在需要时控制车辆,并与其他车辆合作构建压力地图以提高道路安全。针对这种车辆,本文提供了一个审查新兴的传感器技术的驾驶员监控。在我们的研究中,我们研究了用于情感检测的传感器。这种见解对于那些难以准确理解情感信息的人特别有帮助,比如自闭症人群。本文还包括传感器和反馈的材料,从人群中可能有特殊需要的司机。
Using sensors to monitor signals produced by drivers is a way to help better understand how emotions contribute to unsafe driving habits. The need for intuitive machines that can interpret intentional and unintentional signals is imperative for our modern world. However, in complex human–machine work environments, many sensors will not work due to compatibility issues, noise, or practical constraints. This review focuses on practical sensors that have the potential to provide reliable monitoring and meaningful feedback to vehicle operators—such as drivers, train operators, pilots, astronauts—as well as being feasible for implementation and integration with existing work infrastructure. Such an affect-sensitive intelligent vehicle might sound an alarm if signals indicate the driver has become angry or stressed, take control of the vehicle if needed, and collaborate with other vehicles to build a stress map that improves roadway safety. Toward such vehicles, this paper provides a review of emerging sensor technologies for driver monitoring. In our research, we look at sensors used in affect detection. This insight is especially helpful for anyone challenged with accurately understanding affective information, like the autistic population. This paper also includes material on sensors and feedback for drivers from populations that may have special needs.