基于脑电图的模拟驾驶中 BP 神经网络对新手和老手驾驶员的识别研究

Yingzhang Wu, Jie Zhang, Bangbei Tang, Gang Guo
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引用次数: 1

摘要

驾驶员在交通系统中扮演着重要角色。新手司机由于缺乏驾驶经验,驾驶风险意识不足,成为交通系统中的隐患。汽车驾驶辅助系统(ADAS)可以或多或少地帮助新手司机规避危险。为了进一步改进针对不同驾驶经验驾驶员的 ADAS 控制策略,有必要对新手驾驶员和老手驾驶员进行识别。本研究设计了一条 12 公里长的双向直行高速公路作为驾驶场景。记录前额区产生的脑电图(EEG)数据作为评估驾驶员危险感知的指标。我们的目的是通过从收集到的脑电图数据中提取的贝塔波来识别新手司机和老司机在面对危险情况时的表现。结果表明,通过 BP 神经网络,从前额区提取的脑电图特征(β 波的 PSD 值)可以有效识别新手司机和老手司机,并达到了近 88% 的较高准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research on EEG-based Novice and Experienced Drivers' Identification Using BP Neural Network during Simulated Driving
Drivers play an important role in the transportation system. Novice drivers have insufficient driving risk awareness due to lack of driving experience, which has become a potential hazard in the traffic system. The automotive driving assistance system (ADAS) can more or less help the novice driver to avoid danger. In order to further improve the ADAS control strategy for drivers with different driving experience, it is necessary to identify novice drivers and experienced drivers. In this study, a twelve-kilometer two-way straight highway was designed as the driving scenario. Electroencephalogram(EEG) data generated in the frontal region was recorded as an indicator to evaluate the driver's perception of danger. We aim to identify novice drivers and experienced drivers by using beta waves extracted from collected EEG data when facing dangerous situations. The results indicate that the EEG features (PSD value of beta wave) extracted from the frontal region can effectively recognize the novice driver and the experienced driver through the BP neural network, and achieve a relatively high accuracy at nearly 88%.
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