In the past years, driver analyzing has become a field of increasing interest. Within this topic, camera based as well as camera free systems are in the scope of researchers all over the world with the overall goal to detect, for example, critical driver states like drowsiness or distraction. Unfortunately, there are yet no comprehensive models for understanding the driver and his states in the automotive context. Therefore, we present a user model tailored to automotive needs. This model allows us to understand the driver in the automotive environment and to set up a general architecture from which we can decide on necessary input information for detecting a certain driver state.

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It is evident that a lot of accidents occur because of drowsiness or inattentiveness of the driver. The logical consequence is that we have to find methods to better analyze the driver. A lot of research has been spent on camera-based systems which focus on the driver's eye gaze or his head movement. But there are few systems that provide camera-free driver analyzing. This is the main goal of the work presented here which is structured in three phases, with the operational goal of having a working driver analyzer implemented in a car. The main question is: is it possible to make statements concerning the driver and his state by using vehicle data from the CAN Bus only? This paper describes the current state of driver analyzing, our overall system architecture, as well as future work. At the moment, we focus on detecting the driving style of a person.