Physiological signal monitoring and driver behavior analysis have gained increasing attention in both fundamental research and applied research. This study involved the analysis of driving behavior using multimodal physiological data collected from 35 participants. The data included 59-channel EEG, single-channel ECG, 4-channel EMG, single-channel GSR, and eye movement data obtained via a six-degree-of-freedom driving simulator. We categorized driving behavior into five groups: smooth driving, acceleration, deceleration, lane changing, and turning. Through extensive experiments, we confirmed that both physiological and vehicle data met the requirements. Subsequently, we developed classification models, including linear discriminant analysis (LDA), MMPNet, and EEGNet, to demonstrate the correlation between physiological data and driving behaviors. Notably, we propose a multimodal physiological dataset for analyzing driving behavior(MPDB). The MPDB dataset’s scale, accuracy, and multimodality provide unprecedented opportunities for researchers in the autonomous driving field and beyond. With this dataset, we will contribute to the field of traffic psychology and behavior.

生理信号监测和驾驶员行为分析在基础研究和应用研究中越来越受到关注。这项研究涉及使用从 35 名参与者收集的多模态生理数据来分析驾驶行为。数据包括59通道脑电图、单通道心电图、4通道肌电图、单通道GSR以及通过六自由度驾驶模拟器获得的眼动数据。我们将驾驶行为分为五组：平稳驾驶、加速、减速、变道和转弯。通过大量的实验，我们确认生理数据和车辆数据都满足要求。随后，我们开发了分类模型，包括线性判别分析（LDA）、MMPNet 和 EEGNet，以证明生理数据和驾驶行为之间的相关性。值得注意的是，我们提出了一个用于分析驾驶行为的多模态生理数据集（MPDB）。 MPDB 数据集的规模、准确性和多模态为自动驾驶领域及其他领域的研究人员提供了前所未有的机会。通过这个数据集，我们将为交通心理学和行为领域做出贡献。