Explainable Action Prediction through Self-Supervision on Scene Graphs
Pawit Kochakarn,Daniele De Martini,Daniel Omeiza,Lars Kunze,Pawit Kochakarn,Daniele De Martini,Daniel Omeiza,Lars Kunze
This work explores scene graphs as a distilled representation of high-level information for autonomous driving, applied to future driver-action prediction. Given the scarcity and strong imbalance of data samples, we propose a self-supervision pipeline to infer representative and well-separated embeddings. Key aspects are interpretability and explainability; as such, we embed in our architecture at...
