15 citations · 16 across the 5 of their papers we have counts for
4 papers · 1 filter
S-RAF: A Simulation-Based Robustness Assessment Framework for Responsible Autonomous Driving
Daniel Omeiza, Pratik Somaiya, Jo-Ann Pattinson +4
As artificial intelligence (AI) technology advances, ensuring the robustness and safety of AI-driven systems has become paramount. However, varying perceptions of robustness among…
RAG-Driver: Generalisable Driving Explanations with Retrieval-Augmented In-Context Learning in Multi-Modal Large Language Model
Jianhao Yuan, Shuyang Sun, Daniel Omeiza +4
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 crit…
CC-SGG: Corner Case Scenario Generation using Learned Scene Graphs
George Drayson, Efimia Panagiotaki, Daniel Omeiza +1
Corner case scenarios are an essential tool for testing and validating the safety of autonomous vehicles (AVs). As these scenarios are often insufficiently present in naturalistic…
Effects of Explanation Specificity on Passengers in Autonomous Driving
Daniel Omeiza, Raunak Bhattacharyya, Nick Hawes +2
The nature of explanations provided by an explainable AI algorithm has been a topic of interest in the explainable AI and human-computer interaction community. In this paper, we in…