There has been rapid growth in the mobile-phone industry in terms of technology and growing number of users with migration into the car environment. There is also a significant demand for smart phones capable of accessing email, listening to music, organizing daily activities, linking to social networking sites, while the user is on the move. The automotive industry has been significantly impacted by such mobile-phone usage. Driving a car is a complicated and skillful task requiring attention and focus. However many people perceive driving to be easy - second-to-habit or an extension of their natural skills. This complacency encourages drivers to multitask while driving. While many drivers manage this multitasking comfortably, it becomes a distraction and contributes to increased risk while driving for some. Since the effect of multitasking is variable on different drivers, it is important to understand its impact on individual drivers. This study focuses on assessing the impact of several secondary tasks such as speaking over a hand-held mobile phone, tuning the radio, selecting songs from an MP3/CD playlist, speaking to a co-passenger, on individual drivers. To assess the driver activity, we formulate stochastic models such as Gaussian Mixture Models (GMM) using only CAN-bus signals collected on-road in real world driving situations from the UTDrive corpus. We also categorize drivers based on their distraction level into (1) least impacted, (2) moderately impacted, and (3) most impacted drivers. As the automotive industry further advances in developing active safety systems, such driver centric adaptive systems will help in personalizing the vehicle by triggering active safety systems only when drivers are impacted or when they show tendencies of such impact. This will help motivate drivers to use active safety systems rather than disabling them because they are annoyed.