  Trajectory prediction is one of the essential tasks for autonomous vehicles.
Recent progress in machine learning gave birth to a series of advanced
trajectory prediction algorithms. Lately, the effectiveness of using graph
neural networks (GNNs) with vectorized representations for trajectory
prediction has been demonstrated by many researchers. Nonetheless, these
algorithms either pay little attention to models' generalizability across
various scenarios or simply assume training and test data follow similar
statistics. In fact, when test scenarios are unseen or Out-of-Distribution
(OOD), the resulting train-test domain shift usually leads to significant
degradation in prediction performance, which will impact downstream modules and
eventually lead to severe accidents. Therefore, it is of great importance to
thoroughly investigate the prediction models in terms of their
generalizability, which can not only help identify their weaknesses but also
provide insights on how to improve these models. This paper proposes a
generalizability analysis framework using feature attribution methods to help
interpret black-box models. For the case study, we provide an in-depth
generalizability analysis of one of the state-of-the-art graph-based trajectory
predictors that utilize vectorized representation. Results show significant
performance degradation due to domain shift, and feature attribution provides
insights to identify potential causes of these problems. Finally, we conclude
the common prediction challenges and how weighting biases induced by the
training process can deteriorate the accuracy.
