School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China; Engineering Research Center of Transportation Information and Safety, Ministry of Education, Wuhan, 430063, China.
School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan 430063, China.
Accid Anal Prev. 2025 Jan;209:107835. doi: 10.1016/j.aap.2024.107835. Epub 2024 Nov 12.
Driving stress is a critical factor leading to road traffic accidents. Despite numerous studies that have been conducted on driving stress recognition, most of them only focus on accuracy improvement without taking model interpretability into account. In this study, an explainable driving stress recognition framework was presented to quantify stress based on electroencephalography (EEG) and behavior data. Based on the extraction of key EEG and behavior features and feature selection, low, medium, and high levels of driving stress were identified using seven machine learning algorithms. The recognition results when only using EEG or behavior features were compared with the result when fusing EEG together with behavior features. Then, the dependency effects between brain activity, driving behavior, and stress were analyzed using the SHapley Additive exPlanation (SHAP) method, and fuzzy rules were obtained by decision tree method. Results indicated that after feature selection, the accuracy of the combined EEG and behavior feature set improved by 8.56% and 26.51% compared to the single EEG and behavior feature sets respectively, and the accuracy rate of 84.93% was achieved. Furthermore, the variations in driver behavior and physiology under stress were identified by the visualization results of SHAP and the quantitative analysis method of decision tree. The changes of different brain regions in the same frequency band showed higher synchronicity under driving stress stimulation. The changes caused by increased stress can be explained by lower speed, smaller maximum lateral lane deviation, smaller accelerator pedal depth and larger brake depth, along with the power changes of the θ and β-band of the brain.
驾驶压力是导致道路交通事故的关键因素。尽管已经有许多研究致力于驾驶压力识别，但大多数研究仅关注准确性的提高，而没有考虑模型的可解释性。在这项研究中，提出了一种基于脑电（EEG）和行为数据的可解释驾驶压力识别框架，以量化压力。基于关键 EEG 和行为特征的提取以及特征选择，使用七种机器学习算法识别低、中、高三个级别的驾驶压力。比较了仅使用 EEG 或行为特征的识别结果与融合 EEG 和行为特征的结果。然后，使用 SHapley Additive exPlanation (SHAP) 方法分析大脑活动、驾驶行为和压力之间的依赖关系，并通过决策树方法获得模糊规则。结果表明，经过特征选择后，与单独的 EEG 和行为特征集相比，融合 EEG 和行为特征集的准确率分别提高了 8.56%和 26.51%，达到了 84.93%的准确率。此外，通过 SHAP 的可视化结果和决策树的定量分析方法，识别了压力下驾驶员行为和生理的变化。在驾驶压力刺激下，相同频带中不同脑区的变化表现出更高的同步性。压力增加引起的变化可以通过速度降低、最大横向车道偏离减小、油门踏板深度减小和刹车深度增大以及大脑θ和β波段的功率变化来解释。
