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.

通过组合头部姿势特征和眼动数据，该研究综合分析了驾驶员准备就绪评估的有效性，采用机器学习技术和双向 LSTM 架构模拟了驾驶员准备就绪状态，实现了在 DMD 数据集上的优异评估性能。

基于眼动和头部姿势数据评估有条件自动驾驶车辆中驾驶员的准备性

Evaluating Driver Readiness in Conditionally Automated Vehicles from  Eye-Tracking Data and Head Pose

This paper introduces our approach to the EmotioNet Challenge 2020. We pose
the AU recognition problem as a multi-task learning problem, where the
non-rigid facial muscle motion (mainly the first 17 AUs) and the rigid head
motion (the last 6 AUs) are modeled separately. The co-occurrence of the
expression features and the head pose features are explored. We observe that
different AUs converge at various speed. By choosing the optimal checkpoint for
each AU, the recognition results are improved. We are able to obtain a final
score of 0.746 in validation set and 0.7306 in the test set of the challenge.

该研究论文讨论了 EmotioNet Challenge 2020 上的 AU 识别问题，并将其视为一项多任务学习问题，通过分别对非刚性面部肌肉运动（主要是前 17 个 AUs）和刚性头部运动（最后 6 个 AUs）进行建模。研究观察了表情特征和头部姿态特征的共现关系。通过选择最佳的检查点来进行 AU 识别，提高了识别结果，最终在验证集和测试集中获得了分别为 0.746 和 0.7306 的得分。