OpenDriver: An Open-Road Driver State Detection Dataset

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 ECG signal quality assessment, individual biometric identification based on ECG signals, and physiological signal analysis in complex driving environments. To facilitate research in these tasks, corresponding benchmarks have also been introduced. First, a noisy augmentation strategy is applied to generate a larger-scale ECG signal dataset with realistic noise simulation for quality assessment. Second, an end-to-end contrastive learning framework is employed for individual biometric identification. Finally, a comprehensive analysis of drivers' HRV features under different driving conditions is conducted. Each benchmark provides evaluation metrics and reference results. The OpenDriver dataset will be publicly available at https://github.com/bdne/OpenDriver.