  Motion prediction of surrounding vehicles is one of the most important tasks
handled by a self-driving vehicle, and represents a critical step in the
autonomous system necessary to ensure safety for all the involved traffic
actors. Recently a number of researchers from both academic and industrial
communities have focused on this important problem, proposing ideas ranging
from engineered, rule-based methods to learned approaches, shown to perform
well at different prediction horizons. In particular, while for longer-term
trajectories the engineered methods outperform the competing approaches, the
learned methods have proven to be the best choice at short-term horizons. In
this work we describe how to overcome the discrepancy between these two
research directions, and propose a method that combines the disparate
approaches under a single unifying framework. The resulting algorithm fuses
learned, uncertainty-aware trajectories with lane-based paths in a principled
manner, resulting in improved prediction accuracy at both shorter- and
longer-term horizons. Experiments on real-world, large-scale data strongly
suggest benefits of the proposed unified method, which outperformed the
existing state-of-the-art. Moreover, following offline evaluation the proposed
method was successfully tested onboard a self-driving vehicle.
