A model-based approach to probabilistic situation assessment for driver assistance systems

Until today, driver assistance systems deduce actions from very limited information, which consider the driving situation. Only few aspects of complex interactions between the driver, the vehicle and the environment are regarded, a holistic driving situation is not assessed. In this work, we present a feasible approach to model driving situations using a knowledge base. The knowledge is described by first-order logic in a formal language. Propelled by environmental information, a probabilistic network is automatically constructed from the formal definitions, to overcome the rigid nature of traditional networks. The network is continuously updated by sensor information and probabilistic inference of the situation is performed. The method proposed is eligible for driving situation assessment, which is demonstrated in examples.