| 引用本文: | 郭孜政,潘雨帆,潘毅润,张骏,刘萍,谭永刚.驾驶员脑力负荷的SVM识别模型[J].哈尔滨工业大学学报,2016,48(3):154.DOI:10.11918/j.issn.0367-6234.2016.03.026 |
| GUO Zizheng,PAN Yufan,PAN Yirun,ZHANG Jun,LIU Ping,TAN Yonggang.SVM recognition model of driver’s mental workload[J].Journal of Harbin Institute of Technology,2016,48(3):154.DOI:10.11918/j.issn.0367-6234.2016.03.026 |
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| 摘要: |
| 车载信息系统的使用，道路交通控制信息的复杂，增加了驾驶员脑力负荷量.为对驾驶员脑力负荷进行有效识别，为自动辅助驾驶系统以及交通信息的整合优化设计提供依据，以驾驶员脑电信号δ（0.5~4 Hz），θ（4~8 Hz），α（8~13 Hz），β（13~30 Hz）频谱幅值为输入特征，结合SVM模型构建了驾驶员脑力负荷识别模型.在此基础上，基于驾驶模拟器实验数据，对该模型予以试算.结果表明，模型识别正确率可达93.8%~96.5%.该模型对驾驶员脑力负荷识别具有较高准确性，可用于驾驶员脑力负荷识别.
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| 关键词: 驾驶员 脑力负荷 识别 脑电 支持向量机 |
| DOI：10.11918/j.issn.0367-6234.2016.03.026 |
| 分类号:U491.2 |
| 文献标识码:A |
| 基金项目:国家自然科学基金(51108390)； 国家自然科学基金委铁道联合基金资助(U1234206). |
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| SVM recognition model of driver’s mental workload |
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GUO Zizheng1,PAN Yufan1,PAN Yirun1,ZHANG Jun1,LIU Ping2,TAN Yonggang1
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(1.School of Transportation and Logistics,Southwest Jiaotong University,610031 Chengdu,China; 2. Accident Prevention Office,Bureau of Public Security of Chengdu Municipality,610031 Chengdu,China)
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| Abstract: |
| The use of the vehicle information system and the complex road traffic control information make the mental workload of drivers increased. In order to recognize driving mental workload efficiently, provide the basis of automatic auxiliary driving and integrate the traffic information, the method use the EEG signal δ(0.5-4 Hz), θ(4-8 Hz), α(8-13 Hz), β (13-30 Hz) as the input features and SVM model to establish the recognition model for state of driving mental workload. Meanwhile, combine with examples based on EEG data from the simulator to test the model, the result shows that the average recognition accuracy rate was between 93. 8% and 96.5%. The modle shows good accurancies for driver's mental workload recognition and can be used in actual driving.
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| Key words: driver mental workload recognition EEG SVM |
