5 citations · 12 across the 7 of their papers we have counts for
7 papers
Generating Causal Explanations of Vehicular Agent Behavioural Interactions with Learnt Reward Profiles
Rhys Howard, Nick Hawes, Lars Kunze
Transparency and explainability are important features that responsible autonomous vehicles should possess, particularly when interacting with humans, and causal reasoning offers a…
Extending Structural Causal Models for Autonomous Vehicles to Simplify Temporal System Construction & Enable Dynamic Interactions Between Agents
Rhys Howard, Lars Kunze
In this work we aim to bridge the divide between autonomous vehicles and causal reasoning. Autonomous vehicles have come to increasingly interact with human drivers, and in many ca…
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…
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…
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…
Learning Transferable Push Manipulation Skills in Novel Contexts
Rhys Howard, Claudio Zito
This paper is concerned with learning transferable forward models for push manipulation that can be applying to novel contexts and how to improve the quality of prediction when cri…