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In this article, a general probabilistic approach to multisensorial environmental perception of advanced driver assistance systems (ADAS) is presented. This approach incorporates sensor data fusion with self-diagnosis capability and maneuver level intent estimation of detected objects. Thus, the quality of environmental perception is continuously monitored and the intents of the traffic participants are predicted. The resulting probabilities are uniform and consistent basis and reflect the reliability of the results. This knowledge is an important prerequisite for the development of future complex and robust driver assistance systems. The presented approach is based on Bayesian networks (BN), an intuitive and simultaneously powerful form of the probability theory. This approach was demonstrated by means of an integrated lateral assistance system within the German research initiative AKTIV.