We aim to advance towards the design of trustworthy motion prediction systems, based on some of the requirements for the design of Trustworthy Artificial Intelligence. The focus is on evaluation criteria, robustness, and interpretability of outputs.
We proposed 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. We provided detailed explainability analysis, which is a first step towards more transparent and reliable motion prediction systems, important from the perspective of the user, developers and regulatory agencies.
We identified the gaps in current evaluation methodologies and proposed a more comprehensive and holistic evaluation framework for multi-modal motion prediction autonomus vehicle system.
We identified the gaps in current evaluation methodologies and proposed a more comprehensive and holistic evaluation framework for multi-modal motion prediction autonomus vehicle system.
The main area of interest of the project is research on explainable and interpretable motion prediction approaches of road users in highly interactive scenarios using NuScenes dataset.