Design and implementation algorithm of safe driver assistant system based on EOG

Zhao LYU; Xiao-pei WU; Chao ZHANG; Bing WEI

doi:10.11959/j.issn.1000-436x.2016111

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Design and implementation algorithm of safe driver assistant system based on EOG

Academic paper | 更新时间：2024-06-05

- Design and implementation algorithm of safe driver assistant system based on EOG
- Journal of Communications Vol. 37, Issue 7, Pages: 87-95(2016)
- 作者机构：
  
  1. 安徽大学信息保障技术协同创新中心，安徽合肥 230601
  2. 安徽大学计算机科学与技术学院，安徽合肥 230601
- 作者简介：
- 基金信息：
  
  The National Natural Science Foundation of China(61401002;61271352);The Natural Science Foundation of Anhui Province;Anhui Provincial Natural Science Research Project of Colleges and Universities(KJ2014A011)
- DOI：10.11959/j.issn.1000-436x.2016111
  CLC： R318
- Online First：2016-07，
  
  Published：25 July 2016
- 稿件说明：
移动端阅览
Zhao LYU, Xiao-pei WU, Chao ZHANG, et al. Design and implementation algorithm of safe driver assistant system based on EOG[J]. Journal of Communications, 2016, 37(7): 87-95.
DOI：

Zhao LYU, Xiao-pei WU, Chao ZHANG, et al. Design and implementation algorithm of safe driver assistant system based on EOG[J]. Journal of Communications, 2016, 37(7): 87-95. DOI： 10.11959/j.issn.1000-436x.2016111.

摘要

为保证驾驶安全，提高车辆控制系统的智能化水平，实现“手不离盘”操作，设计并实现了一种基于眼电图(EOG)的安全辅助驾驶系统。该系统利用安装在驾驶员眼睛周围的生物电极采集其在观测抬头显示器(HUD

head up display)上提示符时所产生的扫视信号，生成多种车载设备控制命令；对原始多导联EOG信号进行端点检测后，使用了独立分量分析(ICA

independent component analysis)方法进行空域滤波后提取眼动信号特征参数，并结合支持向量机实现了上、左与右扫视动作的识别。实验室环境下对所提算法进行了测试，15位受试者在疲劳与非疲劳状态下的在线平均正确率达到了98.43%与96.0%。实验结果表明，基于 ICA 多类扫视信号识别算法的安全辅助驾驶系统在眼动信号分析中呈现出了良好的分类性能。

Abstract

In order to ensure driving safety

improve the intelligent level of the vehicle control system and realize“keeping hands on the wheel”

a safe driver assistant system (SDAS) based on EOG was proposed. The proposed sys-tem utilized saccade signals which come from bio-electrodes installed around driver's eyes

to generate some control commands when the driver observes different signs located on the head up display (HUD). Furthermore

independent component analysis (ICA) algorithm was used to extract spatial feature parameters of activity-detected EOG signals

and combined with support vector machine (SVM) method to recognize the type of saccade signals

such as up-rolling

left-rolling and right-rolling. Experiments have been carried out in lab environment

and the average correct ratio on 15 sub-jects is 98.43% and 96.0% corresponding to fatigue condition and non-fatigue condition respectively. Experiential results re-veal that the SDAS based on the multi-class saccade signals recognition algorithm presents an excellent classification per-formance.

关键词

Keywords

references

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