Driver distraction is an important risk factor for road traffic injuries, and has been the focus of a number of empirical studies aiming to raise awareness about the risks of distracted driving and to promote countermeasures. While some of the recorded road incidents in these studies have their roots in distracting events (such as mobile phone usage) a large proportion of recorded road incidents can be attributed to more elusive driver inattention factors not linked to specific trigger events. These distraction categories are especially challenging and currently not in focus of current research as they are difficult to detect and address by suitable prognostic measures, in order to improve road safety. To contribute to this issue, this paper presents research into monitoring drivers' mental states in real-time, using objective measurements. We propose an iterative research methodology where specific mental states are elicited, user response captured experimentally, and interaction models built using advanced machine learning techniques. Behavioral measures such as speech, eye activity or posture, and physiological measures such as galvanic skin response or heart rate provide input features for the models. This driver-centric approach addresses the complex issue of driver inattention, and can help improve road safety through active monitoring of road users, customized decision support in the vehicle, and objective training feedback. Low-fidelity simulators we have built allowed us to roll out some preliminary tasks prompting encouraging feedback from subjects during informal testing.