  Detecting other agents and forecasting their behavior is an integral part of
the modern robotic autonomy stack, especially in safety-critical scenarios
entailing human-robot interaction such as autonomous driving. Due to the
importance of these components, there has been a significant amount of interest
and research in perception and trajectory forecasting, resulting in a wide
variety of approaches. Common to most works, however, is the use of the same
few accuracy-based evaluation metrics, e.g., intersection-over-union,
displacement error, log-likelihood, etc. While these metrics are informative,
they are task-agnostic and outputs that are evaluated as equal can lead to
vastly different outcomes in downstream planning and decision making. In this
work, we take a step back and critically assess current evaluation metrics,
proposing task-aware metrics as a better measure of performance in systems
where they are deployed. Experiments on an illustrative simulation as well as
real-world autonomous driving data validate that our proposed task-aware
metrics are able to account for outcome asymmetry and provide a better estimate
of a model's closed-loop performance.
