  Motion prediction systems aim to capture the future behavior of traffic
scenarios enabling autonomous vehicles to perform safe and efficient planning.
The evolution of these scenarios is highly uncertain and depends on the
interactions of agents with static and dynamic objects in the scene. GNN-based
approaches have recently gained attention as they are well suited to naturally
model these interactions. However, one of the main challenges that remains
unexplored is how to address the complexity and opacity of these models in
order to deal with the transparency requirements for autonomous driving
systems, which includes aspects such as interpretability and explainability. In
this work, we aim to improve the explainability of motion prediction systems by
using different approaches. First, we propose a new Explainable Heterogeneous
Graph-based Policy (XHGP) model based on an heterograph representation of the
traffic scene and lane-graph traversals, which learns interaction behaviors
using object-level and type-level attention. This learned attention provides
information about the most important agents and interactions in the scene.
Second, we explore this same idea with the explanations provided by
GNNExplainer. Third, we apply counterfactual reasoning to provide explanations
of selected individual scenarios by exploring the sensitivity of the trained
model to changes made to the input data, i.e., masking some elements of the
scene, modifying trajectories, and adding or removing dynamic agents. The
explainability analysis provided in this paper is a first step towards more
transparent and reliable motion prediction systems, important from the
perspective of the user, developers and regulatory agencies. The code to
reproduce this work is publicly available at
https://github.com/sancarlim/Explainable-MP/tree/v1.1.
