Towards Explainable Motion Prediction using Heterogeneous Graph Representations

Understand

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.