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Motion planning algorithms for automated vehicles need to assess the intended behavior of other Traffic Participants (TPs), in order to predict the likely future trajectory of TPs and plan the motion consequently. Information resulting from several sources, like sensors, must be gathered and combined into a reliable estimate of the intended behavior of TPs. Such estimates must be sufficiently steady and quantify the inherent uncertainty around the assessment. We present a novel information fusion algorithm to combine information from different sources into a coherent and reliable estimate. To explicitly account for the uncertainty of estimates, we leverage the Belief Function Theory and evaluate and handle possible disagreements between estimates individually provided by the sources. The algorithm is flexible and can also handle sources that do not discern between some of the considered behaviors and are only capable of assessing the probability of unions or clusters of different behaviors. We discuss the strengths of the approach through simulations in SUMO, comparing it to the Interactive Multiple Model algorithm.
Benciolini et al. (Tue,) studied this question.