Classification of Manual Versus Autonomous Driving based on Machine Learning of Eye Movement Patterns

Recent advances in autonomous driving systems raise new questions about how to enhance the communication and takeover control between the system and the driver. Eye tracking technologies have shown their feasibility to recognize whether the driver’s gaze is directed ‘on-road’ or ‘off-road’. However, this binary information alone is not sufficient to infer the driver’s engagement in the driving task. In the present work, we take the next step and investigate how driving modes (autopilot, navigation system, and printed map) associated with different levels of engagement can be categorized from drivers’ gaze patterns. Using gaze data recorded in these three driving tasks along with several state-of-the-art machine learning methods, we demonstrate that the driving modes are associated with different gaze patterns. We achieved an average accuracy of 90.1% for binary and 80.3% for multi-class driving mode classification. Our findings pave the way for enhancing driver monitoring systems in (semi-) autonomous cars.