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原文传递 Non-Intrusive Driver Distraction Monitoring Using Vehicle Vibration Sensing.

题名：	Non-Intrusive Driver Distraction Monitoring Using Vehicle Vibration Sensing.
作者：	Noh, H. Y.; Zhang, P.
关键词：	Drivers, Information processing, Sensors, Road traffic accidents, Distraction, Heart rate, Monitoring, Motion, Vibration, Distractions, Hybrid modeling environment, Phone usage detection
摘要：	Driver distraction is responsible for more than a quarter of the 1.3 million deaths and 50 million injuries from road traffic accidents. It is the leading cause of death for the young. With the advent of mobile devices and mobile entertainment, this trend is only projected to increase. To reduce the distraction, the vehicle must first understand the distraction level of a user. In the past, many single-point on-body sensors and camera systems have been proposed to measure in car driver status (such as sleep, etc.) but these approaches are often limited to certain environments or require intrusive sensors on drivers that are difficult to deploy in reality. In this project, the authors will use inertial sensors embedded in the vehicle seat for recognizing driver’s distraction states. These includes physical distractions (such as texting and tuning the radio) and cognitive distractions (including stress, fatigue, etc.). Advantages of the inertial sensor based driver monitoring system come from its simple and non-intrusive nature (i.e. no need for drivers to wear a device). The project team will build on the sensing platform developed with previous support from the University Transportation Center. The team will focus their effort on developing data processing methods to extract detailed heart rate rhythm variability (for stress detection, etc.), and distraction related movement of drivers (for phone usage detection, etc.). The main challenge resides in high noise due to moving vehicles and sensing location constraints. To address these challenges, the project team plans to utilize multiple sensor nodes, high-resolution and high frequency data with hybrid modeling approach to minimize uncertainties in signal processing and obtain reliable information through modeling of data as well as vehicle and human responses. The team will experiment in real-vehicles during driving conditions to ensure real-world applicability of their system.
报告类型：	科技报告