  Trajectory prediction modules are key enablers for safe and efficient
planning of autonomous vehicles (AVs), particularly in highly interactive
traffic scenarios. Recently, learning-based trajectory predictors have
experienced considerable success in providing state-of-the-art performance due
to their ability to learn multimodal behaviors of other agents from data. In
this paper, we present an algorithm called multi-predictor fusion (MPF) that
augments the performance of learning-based predictors by imbuing them with
motion planners that are tasked with satisfying logic-based rules. MPF
probabilistically combines learning- and rule-based predictors by mixing
trajectories from both standalone predictors in accordance with a belief
distribution that reflects the online performance of each predictor. In our
results, we show that MPF outperforms the two standalone predictors on various
metrics and delivers the most consistent performance.
