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Explainable Action Prediction through Self-Supervision on Scene Graphs

Authors :
Kochakarn, Pawit
De Martini, Daniele
Omeiza, Daniel
Kunze, Lars
Kochakarn, Pawit
De Martini, Daniele
Omeiza, Daniel
Kunze, Lars
Publication Year :
2023

Abstract

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 attention mechanisms that can create spatial and temporal heatmaps on the scene graphs. We evaluate our system on the ROAD dataset against a fully-supervised approach, showing the superiority of our training regime.<br />Comment: Accepted to the 2023 IEEE International Conference on Robotics and Automation (ICRA)

Details

Database :
OAIster
Publication Type :
Electronic Resource
Accession number :
edsoai.on1381600761
Document Type :
Electronic Resource