As automated driving technology advances, the role of the driver to resume control of the vehicle in conditionally automated vehicles becomes increasingly critical. In the SAE Level 3 or partly automated vehicles, the driver needs to be available and ready to intervene when necessary. This makes it essential to evaluate their readiness accurately. This article presents a comprehensive analysis of driver readiness assessment by combining head pose features and eye-tracking data. The study explores the effectiveness of predictive models in evaluating driver readiness, addressing the challenges of dataset limitations and limited ground truth labels. Machine learning techniques, including LSTM architectures, are utilised to model driver readiness based on the Spatio-temporal status of the driver's head pose and eye gaze. The experiments in this article revealed that a Bidirectional LSTM architecture, combining both feature sets, achieves a mean absolute error of 0.363 on the DMD dataset, demonstrating superior performance in assessing driver readiness. The modular architecture of the proposed model also allows the integration of additional driver-specific features, such as steering wheel activity, enhancing its adaptability and real-world applicability.

翻译：随着自动驾驶技术的进步，在条件自动驾驶车辆中，驾驶员重新接管车辆控制权的角色变得愈发关键。在SAE L3级或部分自动驾驶车辆中，驾驶员需保持可用状态并随时准备在必要时进行干预，因此准确评估其就绪状态至关重要。本文通过结合头部姿态特征与眼动追踪数据，对驾驶员就绪状态评估进行了全面分析。研究探讨了预测模型在评估驾驶员就绪状态中的有效性，并解决了数据集有限及真实标注不足等挑战。采用包括LSTM架构在内的机器学习技术，基于驾驶员头部姿态与眼动注视的时空状态对就绪状态进行建模。实验结果表明，结合两种特征集的双向LSTM架构在DMD数据集上取得了0.363的平均绝对误差，展现了其在评估驾驶员就绪状态中的优越性能。该模型的模块化架构还允许集成方向盘活动等额外驾驶员特定特征，从而增强了其适应性与实际应用能力。