基于EOG的安全辅助驾驶系统算法设计与实现

吕钊; 吴小培; 张超; 卫兵

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

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基于EOG的安全辅助驾驶系统算法设计与实现

学术论文 | 更新时间：2024-06-05

- 基于EOG的安全辅助驾驶系统算法设计与实现
- Design and implementation algorithm of safe driver assistant system based on EOG
- 通信学报 2016年37卷第7期页码：87-95
- 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：
  
  [ "吕钊（1979-），男，安徽宿州人，博士，安徽大学副教授、硕士生导师，主要研究方向为智能信息处理与人机交互技术。" ]
  [ "吴小培（1966-），男，安徽池州人，博士，安徽大学教授、博士生导师，主要研究方向为智能信息处理与人机交互技术。" ]
  [ "张超（1983-），男，江苏邳州人，博士，安徽大学讲师，主要研究方向为智能信息处理、视频/图像处理。" ]
  [ "卫兵（1984-），男，安徽六安人，安徽大学博士生，主要研究方向为智能信息处理、嵌入式技术等。" ]
- 基金信息：
  
  国家自然科学基金资助项目(61401002;61271352);安徽省自然科学基金资助项目;安徽高校省级自然科学研究重点基金资助项目(KJ2014A011)
- DOI：10.11959/j.issn.1000-436x.2016111
  中图分类号： R318
- 网络出版日期：2016-07，
  
  纸质出版日期：2016-07-25
- 稿件说明：
移动端阅览
吕钊, 吴小培, 张超, 等. 基于EOG的安全辅助驾驶系统算法设计与实现[J]. 通信学报, 2016,37(7):87-95.

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.
吕钊, 吴小培, 张超, 等. 基于EOG的安全辅助驾驶系统算法设计与实现[J]. 通信学报, 2016,37(7):87-95. DOI： 10.11959/j.issn.1000-436x.2016111.

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.

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Keywords

references

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