  The prediction of human trajectories is important for planning in autonomous
systems that act in the real world, e.g. automated driving or mobile robots.
Human trajectory prediction is a noisy process, and no prediction does
precisely match any future trajectory. It is therefore approached as a
stochastic problem, where the goal is to minimise the error between the true
and the predicted trajectory. In this work, we explore the application of
metamorphic testing for human trajectory prediction. Metamorphic testing is
designed to handle unclear or missing test oracles. It is well-designed for
human trajectory prediction, where there is no clear criterion of correct or
incorrect human behaviour. Metamorphic relations rely on transformations over
source test cases and exploit invariants. A setting well-designed for human
trajectory prediction where there are many symmetries of expected human
behaviour under variations of the input, e.g. mirroring and rescaling of the
input data. We discuss how metamorphic testing can be applied to stochastic
human trajectory prediction and introduce the Wasserstein Violation Criterion
to statistically assess whether a follow-up test case violates a
label-preserving metamorphic relation.
