  Uncertainty pervades through the modern robotic autonomy stack, with nearly
every component (e.g., sensors, detection, classification, tracking, behavior
prediction) producing continuous or discrete probabilistic distributions.
Trajectory forecasting, in particular, is surrounded by uncertainty as its
inputs are produced by (noisy) upstream perception and its outputs are
predictions that are often probabilistic for use in downstream planning.
However, most trajectory forecasting methods do not account for upstream
uncertainty, instead taking only the most-likely values. As a result,
perceptual uncertainties are not propagated through forecasting and predictions
are frequently overconfident. To address this, we present a novel method for
incorporating perceptual state uncertainty in trajectory forecasting, a key
component of which is a new statistical distance-based loss function which
encourages predicting uncertainties that better match upstream perception. We
evaluate our approach both in illustrative simulations and on large-scale,
real-world data, demonstrating its efficacy in propagating perceptual state
uncertainty through prediction and producing more calibrated predictions.
