  Despite the significant research efforts on trajectory prediction for
automated driving, limited work exists on assessing the prediction reliability.
To address this limitation we propose an approach that covers two sources of
error, namely novel situations with out-of-distribution (OOD) detection and the
complexity in in-distribution (ID) situations with uncertainty estimation. We
introduce two modules next to an encoder-decoder network for trajectory
prediction. Firstly, a Gaussian mixture model learns the probability density
function of the ID encoder features during training, and then it is used to
detect the OOD samples in regions of the feature space with low likelihood.
Secondly, an error regression network is applied to the encoder, which learns
to estimate the trajectory prediction error in supervised training. During
inference, the estimated prediction error is used as the uncertainty. In our
experiments, the combination of both modules outperforms the prior work in OOD
detection and uncertainty estimation, on the Shifts robust trajectory
prediction dataset by $2.8 \%$ and $10.1 \%$, respectively. The code is
publicly available.
