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