Driver behaviour, including visual strategies, can be modified in autonomous driving situations compared to manual driving. The first axis of this PhD work focuses on the adaptation of visual strategies in autonomous driving. To this end, two experiments were conducted in different road contexts (rural roads with bends and motorway), with drivers having to continuously or partially supervise the driving scene.The results show that the strategies evolve in all cases towards a disengagement of the gaze from the immediate road to move either upstream for more anticipation (continuous supervision on rural roads) or towards irrelevant areas (partial supervision on motorways). The second line of work deals with the link between visual strategies and the out-of-the-loop phenomenon, i.e. the gradual disenga-gement of the driver from supervision as he drives in autonomous mode. The results show that it is possible to predict the state of the driver in the loop from his spontaneous visual strategies.These conclusions are based on statistical modelling work. The resulting models highlight the importance of considering indicators of gaze dynamics to predict the driver's state.These conclusions are based on statistical modelling work. The resulting models highlight the importance of considering indicators of gaze dynamics to predict the driver's state. These results can be used to develop algorithms for detecting driver disengagement in future autonomous vehicles.