Multi-criteria similarity operator based on the Belief Theory: Management of similarity, dissimilarity, conflict and ambiguities

Abstract

For several years, researches dedicated to autonomous driving are growing and a great number of applications and algorithms have been developed for embedded ADAS. In a part of these very critical applications, the tracking of the road's obstacles is one of the key elements. The tracking is built around a stage of measurement to track association. It consists in assessing the similarity between two sets of multi-dimensional state vectors characterizing the track and the measurement. For this application, but not only, we propose in this work a multi-criteria similarity operator based on Belief Theory allowing to take into account uncertainties and imperfections on the data. Partial data availability, conflict and ambiguities between state vectors are managed. The output of this operator provides not only the similarity but also the dissimilarity level, the amount of conflict and ambiguity. This operator is applied to relevant use-cases to highlight its benefits over the standard similarity operators.