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. 1. Introduction Driver analysis (DA) has been an active field of research for years. For example,  published an article about driver monitoring already in 2005. Among others, DA can be divided in the following subtopics: driver monitoring, driving style analysis, and merging vehicle data to derive conclusions concerning the driver (The word driver means both, female as well as male drivers. This is also relevant for words like “his” or “him” which reflect also both, female as well as male persons.) and his environment. For our research work, we focus on the following aspects. (i)How can the state of the driver be detected without using a camera or realtime biosensor data like a electrocardiogram (ecd)? (ii)How can we support the driver, depending on his actual driving situation, based on the results of the driver state detection? Driver monitoring is usually performed by cameras installed in the car for detecting the driver's behavior or state, mostly by using infrared cameras ([2, 3], or ). There are also first results for noncamera based research on driver analysis: By analyzing analog speed graphs, Rygula  makes conclusions about the driving style, speed profile and, depending on driving time and course, aggressiveness of the driver. Therefore, he evaluated ten analog speed graphs for two drivers by comparing their speed profile, their profile referring to the distance, or referring to route and direction. Rygula states that “Even a brake of 45 minutes reduce aggressivity of driving style” ([4, page 79]). A different approach is the research on context recognition in vehicles and the development of a driver model. Ferscha and Riener  describe this process of in-car context recognition and
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