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Daniel Omeiza

2 accepted papers

2024

RAG-Driver: Generalisable Driving Explanations with Retrieval-Augmented In-Context Multi-Modal Large Language Model Learning

RSS 2024poster

We need to trust robots that use often opaque AI methods. They need to explain themselves to us, and we need to trust their explanation. In this regard, explainability plays a critical role in trustworthy autonomous decision-making to foster transparency and acceptance among end users, especially in…

Cited by 83SourcePDFScholar
2023

Explainable Action Prediction through Self-Supervision on Scene Graphs

ICRA 2023poster

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 embed…

Cited by 13SourceScholar