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As automated driving technology advances, the role of the driver to resumecontrol of the vehicle in conditionally automated vehicles becomes increasinglycritical. In the SAE Level 3 or partly automated vehicles, the driver needs tobe available and ready to intervene when necessary. This makes it essential toevaluate their readiness accurately. This article presents a comprehensiveanalysis of driver readiness assessment by combining head pose features andeye-tracking data. The study explores the effectiveness of predictive models inevaluating driver readiness, addressing the challenges of dataset limitationsand limited ground truth labels. Machine learning techniques, including LSTMarchitectures, are utilised to model driver readiness based on theSpatio-temporal status of the driver's head pose and eye gaze. The experimentsin this article revealed that a Bidirectional LSTM architecture, combining bothfeature sets, achieves a mean absolute error of 0.363 on the DMD dataset,demonstrating superior performance in assessing driver readiness. The modulararchitecture of the proposed model also allows the integration of additionaldriver-specific features, such as steering wheel activity, enhancing itsadaptability and real-world applicability.

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