OpenDriver: An Open-Road Driver State Detection Dataset
Delong Liu, Shichao Li, Tianyi Shi, Zhu Meng, Guanyu Chen, Yadong, Huang, Jin Dong, Zhicheng Zhao

TL;DR
OpenDriver is a large-scale, multimodal dataset for driver state detection, including ECG and motion data from 81 drivers over 4,600 hours, enabling research on signal quality, biometric ID, and physiological analysis in real driving conditions.
Contribution
The paper introduces OpenDriver, a comprehensive open-road driver physiological dataset with benchmarks for signal quality, biometric identification, and physiological analysis tasks.
Findings
Developed a large-scale dataset with 3,278 trips and 4,600 hours of data.
Created benchmarks for ECG quality assessment, biometric ID, and driver physiology analysis.
Applied data augmentation and contrastive learning techniques for improved analysis.
Abstract
Among numerous studies for driver state detection, wearable physiological measurements offer a practical method for real-time monitoring. However, 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, OpenDriver, for driver state detection is developed. The OpenDriver encompasses a total of 3,278 driving trips, with a signal collection duration spanning approximately 4,600 hours. Two modalities of driving signals are enrolled in OpenDriver: electrocardiogram (ECG) signals and six-axis motion data of the steering wheel from a motion measurement unit (IMU), which were recorded from 81 drivers and their vehicles. Furthermore, three challenging tasks are involved in our work, namely…
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Taxonomy
TopicsSleep and Work-Related Fatigue · Non-Invasive Vital Sign Monitoring · Heart Rate Variability and Autonomic Control
