Model of the Driver's Physiological State in Conditionally Automated Driving

Road accidents are still one of the main causes of death in the world, despite all the technological advances made on cars since its invention. In particular, the driver's state is often the cause of these road accidents. To assist the driver, the level of automation of cars has increased in recent years, including advanced driver assistance systems. The next level of automation should be conditionally automated driving, where the driver is no longer responsible for the main driving task. In theory, this should reduce the number of accidents. But the fact that the driver can engage in non-driving related tasks could be very dangerous if the car suddenly requires him or her to take over control. In addition, long periods of automated driving can also reduce drivers' alertness and ability to take over control in critical situations, up to causing an accident.

In this regard, this thesis aims at proposing an approach to assess the driver's state continuously in the specific context of conditionally automated driving. This would allow to know if he or she is able to take over control when the car asks him or her. To achieve that goal, machine learning techniques and physiological signals were used to assess the driver's state. In particular, the prediction of four predictive risk factors was done as they are critical at this level of automation: fatigue, mental workload, affective state and situation awareness.

Four contributions were made to address several research questions: the collection of a physiological dataset in the specific context of conditionally automated driving, the creation of a machine learning pipeline to predict the selected risk factors from the collected

data, the design and implementation of a model to assess the driver's state continuously, and a system to measure breathing non-intrusively.