驾驶员疲劳驾驶检测研究综述
A research survey of driver drowsiness driving detection
杨巨成 1魏峰 2林亮 1贾庆祥 1刘建征1
作者信息
- 1. 天津科技大学人工智能学院,天津 300457
- 2. 天津科技大学机械工程学院,天津 300457;天津科技大学人工智能学院,天津 300457
- 折叠
摘要
司机疲劳驾驶检测对于交通安全至关重要,有效的疲劳识别技术可以降低因疲劳引起的交通事故.对司机疲劳驾驶检测方法进行系统综述.介绍司机疲劳的概念及其检测的必要性,阐述疲劳驾驶行为特征并进行分类.详细总结目前广泛使用的几种疲劳驾驶公开数据集,通过归纳分析各数据集特点,对比其适用性和局限性,为后续研究提供宝贵资源.综合分析基于面部特征、生理信号特征、车辆特征以及多特征融合的司机疲劳驾驶检测方法,对比各类方法的优劣.总结司机疲劳驾驶检测领域面临的问题与挑战,对未来的发展方向进行展望.
Abstract
The detection of driver drowsiness was crucial for traffic safety,as effective drowsiness recognition technology could sig-nificantly reduce traffic accidents caused by drowsiness.A systematic review of driver drowsiness detection methods was conducted.The concept of driver fatigue and the necessity of its detection were introduced.The behavioral characteristics of drowsiness driving were elaborated upon and categorized.A detailed summary of several widely used public datasets for drowsiness driving was provid-ed.By analyzing and comparing the characteristics,applicability,and limitations of each dataset,valuable resources were offered for subsequent research.Driver drowsiness detection methods based on facial features,physiological signal features,vehicle characteris-tics,and multi-feature fusion were comprehensively analyzed,with the strengths and weaknesses of each approach being compared.The challenges and issues faced in the field of driver drowsiness detection were summarized,and perspectives on future directions of development were offered.
关键词
疲劳驾驶/交通安全/多特征融合/驾驶行为/疲劳检测Key words
drowsiness driving/traffic safety/multi-feature fusion/driving behavior/drowsiness detection引用本文复制引用
基金项目
天津市研究生科研创新资助项目(2022SKYZ370)
出版年
2024
NETL
NSTL
万方数据