Journal of Computer Applications ›› 2021, Vol. 41 ›› Issue (8): 2330-2337.DOI: 10.11772/j.issn.1001-9081.2020101691

Special Issue: 前沿与综合应用

• Frontier and comprehensive applications • Previous Articles     Next Articles

Research progress on driver distracted driving detection

QIN Binbin, PENG Liangkang, LU Xiangming, QIAN Jiangbo   

  1. Faculty of Electrical Engineering and Computer Science, Ningbo University, Ningbo Zhejiang 315211, China
  • Received:2020-11-02 Revised:2021-01-22 Online:2021-08-10 Published:2021-01-27
  • Supported by:
    This work is partially supported by the Natural Science Foundation of Zhejiang Province (LZ20F020001, LY20F020009), the Natural Science Foundation of Ningbo City (2019A610085).


秦斌斌, 彭良康, 卢向明, 钱江波   

  1. 宁波大学 信息科学与工程学院, 浙江 宁波 315211
  • 通讯作者: 钱江波
  • 作者简介:秦斌斌(1995-),男,山西吕梁人,硕士研究生,CCF会员,主要研究方向:大数据、深度学习;彭良康(1997-),男,河南信阳人,硕士研究生,CCF会员,主要研究方向:大数据、深度学习;卢向明(1996-),男,河南邓州人,硕士研究生,CCF会员,主要研究方向:大数据、深度学习;钱江波(1974-),男,浙江宁波人,教授,博士,CCF高级会员,主要研究方向:大数据、深度学习、计算机视觉。
  • 基金资助:

Abstract: With the rapid development of the vehicle industry and world economy, the number of private cars continues to increase, which results in more and more traffic accidents, and traffic safety problem has become a global hotpot. The research of driver distracted driving detection is mainly divided into two types:traditional Computer Vision (CV) algorithms and deep learning algorithms. In the driver distraction detection based on traditional CV algorithm, image features are extracted by the feature operators such as Scale-Invariant Feature Transform (SIFT) and Histogram of Oriented Gradient (HOG), then Support Vector Machine (SVM) is combined to build model and classify the images. However, the traditional CV algorithms have disadvantages of high requirements for the environment, narrow application range, large amount of parameters and high computational complexity. In recent years, deep learning has shown excellent performance such as fast speed and high precision in extracting data features. Therefore, the researchers began to introduce deep learning into driver distracted driving detection. The methods based on deep learning can realize the end-to-end distracted driving detection network with high accuracy. The research status of the traditional CV algorithms and deep learning algorithms in driver distracted driving detection was introduced. Firstly, the situations of the traditional CV algorithms used in the image field and the research of driver distracted driving detection were elaborated. Secondly, the research of driver distracted driving based on deep learning was introduced. Thirdly, the accuracies and model parameters of different driver distracted driving detection methods were compared and analyzed. Finally, the existing research was summarized and three problems that driver distracted driving detection need to solve in the future were put forward:the driver's distraction state and the distraction degree division standards need to be further improved, three aspects of person-car-road need to be considered comprehensively, and how to reduce neural network parameters more effectively.

Key words: distracted driving, Convolutional Neural Network (CNN), deep learning, driver detection, machine learning

摘要: 随着车辆工业和世界经济的快速发展,私家汽车数量不断增加,导致交通事故越来越多,且交通安全问题已经成为全球关注的焦点问题。司机分心驾驶检测的研究主要分为传统计算机视觉(CV)算法和深度学习算法两种。基于传统CV算法的司机分心检测通过尺度不变特征转换(SIFT)、方向梯度直方图(HOG)等特征算子提取图像特征,然后结合支持向量机(SVM)建立模型并对图像进行分类。然而传统CV算法具有对环境的要求高、运用范围较窄、参数多、计算量大的缺点。近年来深度学习在提取数据特征方面表现出速度快、精度高等优异的性能,因此研究人员开始将深度学习引入到司机分心驾驶检测中。基于深度学习的方法可以实现端到端的司机分心驾驶检测网络,而且取得了很高的准确度。介绍了传统CV算法和深度学习算法在司机分心驾驶检测的研究现状,首先,阐释了传统CV算法用于图像领域和司机分心驾驶检测研究的情况;接着,介绍了基于深度学习的司机分心驾驶研究;而后,从准确度、模型参数量等方面对不同司机分心驾驶检测方法进行比较分析;最后,对现有的研究进行了总结并提出了未来司机分心驾驶检测需要解决的三个问题:驾驶过程中司机分心状态以及分心程度划分规范需进一步完善,需要综合考虑人-车-路三者以及如何才能更有效地减少神经网络参数。

关键词: 分心驾驶, 卷积神经网络, 深度学习, 司机检测, 机器学习

CLC Number: