驾驶员脑力负荷的SVM识别模型

SVM recognition model of driver’s mental workload

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.

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.