Li Yifan, Liu Bo, Zhang Wenli
Faculty of Information Science and Technology, Beijing University of Technology, Beijing 100124, China.
Sensors (Basel). 2024 Dec 31;25(1):174. doi: 10.3390/s25010174.
With the increasing complexity of urban roads and rising traffic flow, traffic safety has become a critical societal concern. Current research primarily addresses drivers' attention, reaction speed, and perceptual abilities, but comprehensive assessments of cognitive abilities in complex traffic environments are lacking. This study, grounded in cognitive science and neuropsychology, identifies and quantitatively evaluates ten cognitive components related to driving decision-making, execution, and psychological states by analyzing video footage of drivers' actions. Physiological data (e.g., Electrocardiogram (ECG), Electrodermal Activity (EDA)) and non-physiological data (e.g., Eye Tracking (ET)) are collected from simulated driving scenarios. A dual-branch Transformer network model is developed to extract temporal features from multimodal data, integrating these features through a weight adjustment strategy to predict driving-related cognitive abilities. Experiments on a multimodal driving dataset from the Computational Physiology Laboratory at the University of Houston, USA, yield an Accuracy (ACC) of 0.9908 and an F1-score of 0.9832, confirming the model's effectiveness. This method effectively combines scale measurements and driving behavior under secondary tasks to assess cognitive abilities, providing a novel approach for driving risk assessment and traffic safety strategy development.
随着城市道路复杂性的增加和交通流量的上升，交通安全已成为社会关注的关键问题。当前的研究主要关注驾驶员的注意力、反应速度和感知能力，但缺乏对复杂交通环境中认知能力的全面评估。本研究基于认知科学和神经心理学，通过分析驾驶员行为的视频片段，识别并定量评估与驾驶决策、执行和心理状态相关的十个认知成分。从模拟驾驶场景中收集生理数据（如心电图（ECG）、皮肤电活动（EDA））和非生理数据（如眼动追踪（ET））。开发了一种双分支Transformer网络模型，从多模态数据中提取时间特征，通过权重调整策略整合这些特征，以预测与驾驶相关的认知能力。在美国休斯顿大学计算生理学实验室的多模态驾驶数据集上进行的实验，准确率（ACC）达到0.9908，F1分数达到0.9832，证实了该模型的有效性。该方法有效地结合了次要任务下的量表测量和驾驶行为来评估认知能力，为驾驶风险评估和交通安全策略制定提供了一种新方法。
