  We present a random finite set-based method for achieving comprehensive
situation awareness by each vehicle in a distributed vehicle network. Our
solution is designed for labeled multi-Bernoulli filters running in each
vehicle. It involves complementary fusion of sensor information locally running
through consensus iterations. We introduce a novel label merging algorithm to
eliminate double counting. We also extend the label space to incorporate sensor
identities. This helps to overcome label inconsistencies. We show that the
proposed algorithm is able to outperform the standard LMB filter using a
distributed complementary approach with limited fields of view.
