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.
@article{arxiv.2302.03477,
title = {Explainable Action Prediction through Self-Supervision on Scene Graphs},
author = {Pawit Kochakarn and Daniele De Martini and Daniel Omeiza and Lars Kunze},
journal= {arXiv preprint arXiv:2302.03477},
year = {2023}
}
Comments
Accepted to the 2023 IEEE International Conference on Robotics and Automation (ICRA)