  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.
