In modern society, road safety relies heavily on the psychological and physiological state of drivers. Negative factors such as fatigue, drowsiness, and stress can impair drivers' reaction time and decision making abilities, leading to an increased incidence of traffic accidents. Among the numerous studies for impaired driving detection, wearable physiological measurement is a real-time approach to monitoring a driver's state. However, currently, there are few driver physiological datasets in open road scenarios and the existing datasets suffer from issues such as poor signal quality, small sample sizes, and short data collection periods. Therefore, in this paper, a large-scale multimodal driving dataset for driver impairment detection and biometric data recognition is designed and described. The dataset contains two modalities of driving signals: six-axis inertial signals and electrocardiogram (ECG) signals, which were recorded while over one hundred drivers were following the same route through open roads during several months. Both the ECG signal sensor and the six-axis inertial signal sensor are installed on a specially designed steering wheel cover, allowing for data collection without disturbing the driver. Additionally, electrodermal activity (EDA) signals were also recorded during the driving process and will be integrated into the presented dataset soon. Future work can build upon this dataset to advance the field of driver impairment detection. New methods can be explored for integrating other types of biometric signals, such as eye tracking, to further enhance the understanding of driver states. The insights gained from this dataset can also inform the development of new driver assistance systems, promoting safer driving practices and reducing the risk of traffic accidents. The OpenDriver dataset will be publicly available soon.

翻译：在现代社会，道路交通安全高度依赖于驾驶员的心理与生理状态。疲劳、困倦、压力等负面因素会损害驾驶员的反应时间及决策能力，导致交通事故发生率上升。在众多关于驾驶员状态异常检测的研究中，可穿戴生理测量是一种实时监测驾驶员状态的方法。然而，目前开放道路场景下的驾驶员生理数据集较少，且现有数据集存在信号质量差、样本量小、采集周期短等问题。为此，本文设计并描述了一个用于驾驶员异常状态检测与生物特征识别的大规模多模态驾驶数据集。该数据集包含两种驾驶信号模态：六轴惯性信号与心电图（ECG）信号，这些信号在数月内由超过一百名驾驶员沿相同路线在开放道路上行驶时同步采集。ECG信号传感器与六轴惯性信号传感器均安装于特制的方向盘套上，可在不干扰驾驶员的情况下进行数据采集。此外，驾驶过程中的皮肤电活动（EDA）信号也已记录，并将在近期整合至本数据集中。未来研究可基于该数据集推动驾驶员异常状态检测领域的发展，探索集成如眼动追踪等其他类型生物特征信号的新方法，以进一步加深对驾驶员状态的理解。从该数据集中获得的洞见还可为新型驾驶员辅助系统的开发提供参考，从而促进更安全的驾驶行为，降低交通事故风险。OpenDriver数据集将于近期公开发布。