  Autonomous driving consists of a multitude of interacting modules, where each
module must contend with errors from the others. Typically, the motion
prediction module depends upon a robust tracking system to capture each agent's
past movement. In this work, we systematically explore the importance of the
tracking module for the motion prediction task and ultimately conclude that the
overall motion prediction performance is highly sensitive to the tracking
module's imperfections. We explicitly compare models that use tracking
information to models that do not across multiple scenarios and conditions. We
find that the tracking information plays an essential role and improves motion
prediction performance in noise-free conditions. However, in the presence of
tracking noise, it can potentially affect the overall performance if not
studied thoroughly. We thus argue practitioners should be mindful of noise when
developing and testing motion/tracking modules, or that they should consider
tracking free alternatives.
