基于机器学习系统的实时V2V通信,用于检测驾驶员的困倦状态

A. Awad, S. Mohan
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引用次数: 2

摘要

本文应用机器学习来检测驾驶员是否昏昏欲睡并提醒驾驶员。司机的困倦会导致交通事故,造成严重的身体伤害,包括死亡,以及重大的经济损失。由于睡眠不足导致的驾驶员疲劳导致了今天道路上的重大事故。据美国国家公路交通安全管理局(U.S. NHTSA)的数据,2010年,美国发生了近2400万辆汽车交通事故,导致3.3万多人死亡,390多万人受伤。很大比例的交通事故可归咎于疲劳驾驶。因此,必须设计和实施一种有效的技术,以便在驾驶员感到困倦时立即检测困倦,并提醒和唤醒驾驶员,从而防止事故的发生。作者应用机器学习来检测司机的闭眼和打哈欠,以优化系统。本文还实现了DSRC来连接车辆,并在道路上创建一个自组织的车辆网络。当系统检测到司机昏昏欲睡时,附近其他车辆的司机就会收到警报。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Real-Time V2V Communication With a Machine Learning-Based System for Detecting Drowsiness of Drivers
This article applies machine learning to detect whether a driver is drowsy and alert the driver. The drowsiness of a driver can lead to accidents resulting in severe physical injuries, including deaths, and significant economic losses. Driver fatigue resulting from sleep deprivation causes major accidents on today's roads. In 2010, nearly 24 million vehicles were involved in traffic accidents in the U.S., which resulted in more than 33,000 deaths and over 3.9 million injuries, according to the U.S. NHTSA. A significant percentage of traffic accidents can be attributed to drowsy driving. It is therefore imperative that an efficient technique is designed and implemented to detect drowsiness as soon as the driver feels drowsy and to alert and wake up the driver and thereby preventing accidents. The authors apply machine learning to detect eye closures along with yawning of a driver to optimize the system. This paper also implements DSRC to connect vehicles and create an ad hoc vehicular network on the road. When the system detects that a driver is drowsy, drivers of other nearby vehicles are alerted.
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