A Unified Bayesian Approach for Object and Situation Assessment

Object and situation assessment are crucial components of many advanced driver assistance systems (ADASs). Current object assessment algorithms usually provide a probabilistic representation of relevant entities in the vehicle´s environment. Similarly, probabilistic approaches for situation assessment have been proposed. However, the interface between both stages is still an unsolved issue. In this paper, a direct link between Bayes filters and Bayesian networks is proposed which is based on adaptive likelihood nodes. The method is illustrated on the example of a system which automatically determines lane change maneuver recommendations. The presented results using both simulated and real data show that the proposed approach provides a unified approach for handling uncertainties in ADAS applications.