  Tracking multiple objects through time is an important part of an intelligent
transportation system. Random finite set (RFS)-based filters are one of the
emerging techniques for tracking multiple objects. In multi-object tracking
(MOT), a common assumption is that each object is moving independent of its
surroundings. But in many real-world applications, target objects interact with
one another and the environment. Such interactions, when considered for
tracking, are usually modeled by an interactive motion model which is
application specific. In this paper, we present a novel approach to incorporate
target interactions within the prediction step of an RFS-based multi-target
filter, i.e. labeled multi-Bernoulli (LMB) filter. The method has been
developed for two practical applications of tracking a coordinated swarm and
vehicles. The method has been tested for a complex vehicle tracking dataset and
compared with the LMB filter through the OSPA and OSPA$^{(2)}$ metrics. The
results demonstrate that the proposed interaction-aware method depicts
considerable performance enhancement over the LMB filter in terms of the
selected metrics.
