342 citations · 379 across the 11 of their papers we have counts for
11 papers
A Transparency Paradox? Investigating the Impact of Explanation Specificity and Autonomous Vehicle Perceptual Inaccuracies on Passengers
Daniel Omeiza, Raunak Bhattacharyya, Marina Jirotka +2
Transparency in automated systems could be afforded through the provision of intelligible explanations. While transparency is desirable, might it lead to catastrophic outcomes (suc…
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…
Learning Run-time Safety Monitors for Machine Learning Components
Ozan Vardal, Richard Hawkins, Colin Paterson +4
For machine learning components used as part of autonomous systems (AS) in carrying out critical tasks it is crucial that assurance of the models can be maintained in the face of p…
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…
Textual Explanations for Automated Commentary Driving
Marc Alexander Kühn, Daniel Omeiza, Lars Kunze
The provision of natural language explanations for the predictions of deep-learning-based vehicle controllers is critical as it enhances transparency and easy audit. In this work,…
Explainable Action Prediction through Self-Supervision on Scene Graphs
Pawit Kochakarn, Daniele De Martini, Daniel Omeiza +1
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 s…
From Spoken Thoughts to Automated Driving Commentary: Predicting and Explaining Intelligent Vehicles' Actions
Daniel Omeiza, Sule Anjomshoae, Helena Webb +2
In commentary driving, drivers verbalise their observations, assessments and intentions. By speaking out their thoughts, both learning and expert drivers are able to create a bette…
A Fait Accompli? An Empirical Study into the Absence of Consent to Third-Party Tracking in Android Apps
Konrad Kollnig, Reuben Binns, Pierre Dewitte +5
Third-party tracking allows companies to collect users' behavioural data and track their activity across digital devices. This can put deep insights into users' private lives into…
Explanations in Autonomous Driving: A Survey
Daniel Omeiza, Helena Webb, Marina Jirotka +1
The automotive industry has witnessed an increasing level of development in the past decades; from manufacturing manually operated vehicles to manufacturing vehicles with a high le…