The use of driver models within advanced driver assistance systems (ADAS) allows anticipating the driving behavior of the vehicle and all traffic participants in the close vicinity. This valuable information could considerably improve the performance as well as the acceptance of ADAS. Consequently complex driver models need to be integrated in embedded systems. This work, first of all, aims to summarize important driver models described in literature. Based upon this a suitable approach to implement a driver model on an embedded system is derived. The model used, focuses on the longitudinal driving and lane change behavior of drivers. The system architecture is derived and optimized for real-time execution. The driver model is analyzed in detailed simulations. Test drives in a small scale naturalistic driving study are used to validate the driver model. This paper defines a standard driver model to be implemented as part of the DESERVE platform within the Artemis project “DESERVE”. As embedded automotive hardware the dSpace MicroAutoBox II is used. The paper summarizes approaches and examples to use the generated prediction data in ADAS like ACC.
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