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.

翻译：在众多驾驶员状态检测研究中，可穿戴生理测量为实时监测提供了实用方法。然而，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、样本规模小、数据采集周期短等问题。为此，本文开发了一个用于驾驶员状态检测的大规模多模态驾驶数据集OpenDriver。该数据集共包含3,278次驾驶行程，信号采集总时长约4,600小时。OpenDriver收录了两种模态的驾驶信号：心电图（ECG）信号和来自惯性测量单元（IMU）的六轴方向盘运动数据，这些数据采集自81名驾驶员及其车辆。此外，本研究涉及三项具有挑战性的任务：ECG信号质量评估、基于ECG信号的个体生物特征识别，以及复杂驾驶环境下的生理信号分析。为促进这些任务的研究，本文同时引入了相应的基准测试。首先，采用噪声增强策略生成更大规模的ECG信号数据集，并通过真实噪声模拟进行质量评估。其次，采用端到端对比学习框架进行个体生物特征识别。最后，系统分析了不同驾驶条件下驾驶员的心率变异性（HRV）特征。每个基准测试均提供了评估指标和参考结果。OpenDriver数据集将在https://github.com/bdne/OpenDriver 公开提供。