Aims Lane departures while driving a car may be caused directly by errors in steering behaviour and indirectly as a consequence of driver distraction. Designing advanced driving assistance systems (ADAS) is a way to avoid these situations. One of the key problems is to monitor and predict driver behaviour in order to make the ADAS intervene in a timely and efficient way. Our approach to achieve this goal is to incorporate a driver model in the design process. The model represents the visual and motor determinants of steering control. It has been used as the foundation of two types of ADAS: 1. a haptic shared control system that exerts forces on the steering wheel in such a way that the automation blends into the driver’s sensorimotor control loop, providing continuous support to lane keeping, and 2. a method of distraction estimation based on the comparison between steering behaviour and the model prediction. Methods Based on current knowledge of human sensorimotor functions, the model represents the visual anticipation of the road curvature fed by the angular deviation of a far point, the visual compensation of lateral position error fed by the angular deviation of a near point, and a neuromuscular system that transforms the output of the visual subsystem into a steering wheel torque. For haptic shared control, the approach consisted in designing a control law that optimizes performance and cooperation criteria, taking into account the predictions of the driver model. A comparison was performed between the driver behaviour when using this system and when using a shared control law that did not incorporate a driver model. For distraction estimation, an identification of the driver model’s parameters was conducted in five different distraction conditions: no distraction and cognitive, visual, motor and visuomotor distractions. The torque prediction error and the parameters values were analysed. Results The results concerning haptic shared control suggest that cooperation between the driver and the automation can be improved by the inclusion of the driver model in the control law. In particular, when the level of haptic authority was set at a high level, the driver acted 27% more in coherence with the system. For the second application, the results show that the model prediction error is commensurate to a modification of steering behaviour caused by distraction. The parametric analysis suggests that an online identification of the model may be used to some extent to discriminate between different types of distraction. Conclusions Adopting a model-based approach to design ADAS is promising. The current model is limited to the representation of sensorimotor processes in humans. As such, it is adapted to be used in the context of real-time control embedded systems. In the future, it could be extended by incorporating a tactical analysis of the driving context.