342 citations · 578 across the 39 of their papers we have counts for
8 papers · 1 filter
LROC-PANGU-GAN: Closing the Simulation Gap in Learning Crater Segmentation with Planetary Simulators
Jaewon La, Jaime Phadke, Matt Hutton +5
It is critical for probes landing on foreign planetary bodies to be able to robustly identify and avoid hazards - as, for example, steep cliffs or deep craters can pose significant…
Towards Probabilistic Causal Discovery, Inference & Explanations for Autonomous Drones in Mine Surveying Tasks
Ricardo Cannizzaro, Rhys Howard, Paulina Lewinska +1
Causal modelling offers great potential to provide autonomous agents the ability to understand the data-generation process that governs their interactions with the world. Such mode…
Generating and Explaining Corner Cases Using Learnt Probabilistic Lane Graphs
Enrik Maci, Rhys Howard, Lars Kunze
Validating the safety of Autonomous Vehicles (AVs) operating in open-ended, dynamic environments is challenging as vehicles will eventually encounter safety-critical situations for…
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…
Simulation-Based Counterfactual Causal Discovery on Real World Driver Behaviour
Rhys Howard, Lars Kunze
Being able to reason about how one's behaviour can affect the behaviour of others is a core skill required of intelligent driving agents. Despite this, the state of the art struggl…
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…
Evaluating Temporal Observation-Based Causal Discovery Techniques Applied to Road Driver Behaviour
Rhys Howard, Lars Kunze
Autonomous robots are required to reason about the behaviour of dynamic agents in their environment. The creation of models to describe these relationships is typically accomplishe…