In recent years, advanced driver assistance systems (ADAS) have found their way into many commercially available vehicles. Research has shown that many drivers still lack knowledge regarding these systems, resulting in over-reliance or underutilization on either extreme. This thesis highlights the need to allow users of these systems an easy way of becoming familiar with the distinctive characteristics of these systems without operating a vehicle at speed. Many traditional methods of learning, like reading manuals or test driving compromise either on lack of feedback fidelity or ease of use, causing many people to avoid using the systems altogether or incorrectly. In order to tackle this obstacle, an in-car driving simulator game is created aiming to guide the learning of ADAS features. To test its effectiveness, drivers’ change in mental model score is compared before and after the learning intervention in the simulator game. The mental model score is calculated using metrics from signal detection theory: sensitivity(d’) and bias (c), which are measures for objective understanding and bias towards distrusting and over-confidence. Participants showed a small significant increase in understanding of the system after the intervention.