  When predicting trajectories of road agents, motion predictors usually
approximate the future distribution by a limited number of samples. This
constraint requires the predictors to generate samples that best support the
task given task specifications. However, existing predictors are often
optimized and evaluated via task-agnostic measures without accounting for the
use of predictions in downstream tasks, and thus could result in sub-optimal
task performance.
  In this paper, we propose a task-informed motion prediction model that better
supports the tasks through its predictions, by jointly reasoning about
prediction accuracy and the utility of the downstream tasks, which is commonly
used to evaluate the task performance. The task utility function does not
require the full task information, but rather a specification of the utility of
the task, resulting in predictors that serve a wide range of downstream tasks.
We demonstrate our approach on two use cases of common decision making tasks
and their utility functions, in the context of autonomous driving and parallel
autonomy. Experiment results show that our predictor produces accurate
predictions that improve the task performance by a large margin in both tasks
when compared to task-agnostic baselines on the Waymo Open Motion dataset.
