Hernández Luis G, Mozos Oscar Martinez, Ferrández José M, Antelis Javier M
Tecnologico de Monterrey, Escuela de Ingeniería y Ciencias, Zapopan, Mexico.
DETCP, Technical University of Cartagena, Cartagena, Spain.
Front Neuroinform. 2018 May 29;12:29. doi: 10.3389/fninf.2018.00029. eCollection 2018.
The anticipatory recognition of braking is essential to prevent traffic accidents. For instance, driving assistance systems can be useful to properly respond to emergency braking situations. Moreover, the response time to emergency braking situations can be affected and even increased by different driver's cognitive states caused by stress, fatigue, and extra workload. This work investigates the detection of emergency braking from driver's electroencephalographic (EEG) signals that precede the brake pedal actuation. Bioelectrical signals were recorded while participants were driving in a car simulator while avoiding potential collisions by performing emergency braking. In addition, participants were subjected to stress, workload, and fatigue. EEG signals were classified using support vector machines (SVM) and convolutional neural networks (CNN) in order to discriminate between braking intention and normal driving. Results showed significant recognition of emergency braking intention which was on average 71.1% for SVM and 71.8% CNN. In addition, the classification accuracy for the best participant was 80.1 and 88.1% for SVM and CNN, respectively. These results show the feasibility of incorporating recognizable driver's bioelectrical responses into advanced driver-assistance systems to carry out early detection of emergency braking situations which could be useful to reduce car accidents.
提前识别制动对于预防交通事故至关重要。例如，驾驶辅助系统有助于妥善应对紧急制动情况。此外，紧急制动情况的响应时间可能会受到压力、疲劳和额外工作量等不同驾驶员认知状态的影响，甚至增加。这项工作研究了在制动踏板启动之前从驾驶员脑电图（EEG）信号中检测紧急制动的情况。在参与者驾驶汽车模拟器时记录生物电信号，同时通过紧急制动避免潜在碰撞。此外，让参与者承受压力、工作量和疲劳。使用支持向量机（SVM）和卷积神经网络（CNN）对EEG信号进行分类，以便区分制动意图和正常驾驶。结果显示紧急制动意图的识别效果显著，SVM平均为71.1%，CNN为71.8%。此外，最佳参与者的分类准确率SVM为80.1%，CNN为88.1%。这些结果表明，将可识别的驾驶员生物电反应纳入先进的驾驶辅助系统以对紧急制动情况进行早期检测是可行的，这可能有助于减少汽车事故。
