College of Intelligence Science and Technology, National University of Defense Technology, Changsha, 410073, Hunan, China.
Biomed Eng Online. 2023 Jul 1;22(1):65. doi: 10.1186/s12938-023-01129-4.
Current research related to electroencephalogram (EEG)-based driver's emergency braking intention detection focuses on recognizing emergency braking from normal driving, with little attention to differentiating emergency braking from normal braking. Moreover, the classification algorithms used are mainly traditional machine learning methods, and the inputs to the algorithms are manually extracted features.
To this end, a novel EEG-based driver's emergency braking intention detection strategy is proposed in this paper. The experiment was conducted on a simulated driving platform with three different scenarios: normal driving, normal braking and emergency braking. We compared and analyzed the EEG feature maps of the two braking modes, and explored the use of traditional methods, Riemannian geometry-based methods, and deep learning-based methods to predict the emergency braking intention, all using the raw EEG signals rather than manually extracted features as input.
We recruited 10 subjects for the experiment and used the area under the receiver operating characteristic curve (AUC) and F1 score as evaluation metrics. The results showed that both the Riemannian geometry-based method and the deep learning-based method outperform the traditional method. At 200 ms before the start of real braking, the AUC and F1 score of the deep learning-based EEGNet algorithm were 0.94 and 0.65 for emergency braking vs. normal driving, and 0.91 and 0.85 for emergency braking vs. normal braking, respectively. The EEG feature maps also showed a significant difference between emergency braking and normal braking. Overall, based on EEG signals, it was feasible to detect emergency braking from normal driving and normal braking.
The study provides a user-centered framework for human-vehicle co-driving. If the driver's intention to brake in an emergency can be accurately identified, the vehicle's automatic braking system can be activated hundreds of milliseconds earlier than the driver's real braking action, potentially avoiding some serious collisions.
当前基于脑电图（EEG）的驾驶员紧急制动意图检测相关研究主要集中在识别正常驾驶与紧急制动，而很少关注紧急制动与正常制动的区分。此外，所使用的分类算法主要是传统的机器学习方法，算法的输入是手动提取的特征。
为此，本文提出了一种基于脑电图的驾驶员紧急制动意图检测策略。实验在模拟驾驶平台上进行，有三种不同场景：正常驾驶、正常制动和紧急制动。我们比较和分析了两种制动模式的脑电图特征图，并探索了使用传统方法、基于黎曼几何的方法和基于深度学习的方法来预测紧急制动意图，所有这些方法都使用原始脑电图信号作为输入，而不是手动提取的特征。
我们招募了 10 名受试者进行实验，并使用接收者操作特征曲线下的面积（AUC）和 F1 分数作为评估指标。结果表明，基于黎曼几何的方法和基于深度学习的方法都优于传统方法。在实际制动开始前 200ms，基于深度学习的 EEGNet 算法对紧急制动与正常驾驶的 AUC 和 F1 分数分别为 0.94 和 0.65，对紧急制动与正常制动的 AUC 和 F1 分数分别为 0.91 和 0.85。脑电图特征图也显示了紧急制动与正常制动之间的显著差异。总的来说，基于脑电图信号，从正常驾驶和正常制动中检测紧急制动是可行的。
该研究为人机共驾提供了以用户为中心的框架。如果能准确识别驾驶员的紧急制动意图，车辆的自动制动系统可以在驾驶员实际制动动作前提前数百毫秒启动，从而可能避免一些严重的碰撞。
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