  In modern autonomy stacks, prediction modules are paramount to planning
motions in the presence of other mobile agents. However, failures in prediction
modules can mislead the downstream planner into making unsafe decisions.
Indeed, the high uncertainty inherent to the task of trajectory forecasting
ensures that such mispredictions occur frequently. Motivated by the need to
improve safety of autonomous vehicles without compromising on their
performance, we develop a probabilistic run-time monitor that detects when a
"harmful" prediction failure occurs, i.e., a task-relevant failure detector. We
achieve this by propagating trajectory prediction errors to the planning cost
to reason about their impact on the AV. Furthermore, our detector comes
equipped with performance measures on the false-positive and the false-negative
rate and allows for data-free calibration. In our experiments we compared our
detector with various others and found that our detector has the highest area
under the receiver operator characteristic curve.
