342 citations · 578 across the 39 of their papers we have counts for
39 papers
Beyond Visual Quality: A Study of Test-Time Planning with World Action Models
Jianhao Yuan, Yu Yuan, Benjamin Ramtoula +5
World action models generate actions together with visual predictions of their consequences. These paired outputs create the potential for planning by sampling multiple actions fro…
A revised framework for the assessment of psychological safety in autonomous vehicles
Yandika Sirgabsou, Benjamin Hardin, François Leblanc +5
Despite recent technological progress in the development of autonomous vehicles (AVs), their societal acceptability remains a subject of debate as recent research findings point to…
Engineering Psychological Safety in Autonomous Vehicles: A Systems-Theoretic Framework for Psychological Safety in Autonomous Vehicles and its Validation in Real-World Scenarios
Yandika Sirgabsou, Benjamin Hardin, François Leblanc +5
Despite rapid technological advances, the societal acceptability of autonomous vehicles (AVs) remains limited by psychological barriers that extend beyond traditional concerns of p…
What Can Eye Gaze Teach Us About Real-World Cycling? Insights From the Oxford RobotCycle Project
Benjamin Hardin, Efimia Panagiotaki, Daniele De Martini +1
Although much is known about the physical danger of cycling situations, less is understood about the perceived danger of cycling. Furthermore, perception of danger may be filtered…
Quantile-Coupled Flow Matching for Distributional Reinforcement Learning
Michael Groom, Victor-Alexandru Darvariu, Lars Kunze +2
Unlike standard expected-return Reinforcement Learning (RL), Distributional RL (DRL) models the full return distribution, making it better-suited for uncertainty-aware and risk-sen…
Risk-Aware Reinforcement Learning for Mobile Manipulation
Michael Groom, James Wilson, Nick Hawes +1
For robots to successfully transition from lab settings to everyday environments, they must begin to reason about the risks associated with their actions and make informed, risk-aw…
Sociotechnical Challenges of Machine Learning in Healthcare and Social Welfare
Tyler Reinmund, Lars Kunze, Marina Jirotka
Sociotechnical challenges of machine learning in healthcare and social welfare are mismatches between how a machine learning tool functions and the structure of care practices. Whi…
LikePhys: Evaluating Intuitive Physics Understanding in Video Diffusion Models via Likelihood Preference
Jianhao Yuan, Fabio Pizzati, Francesco Pinto +5
Intuitive physics understanding in video diffusion models plays an essential role in building general-purpose physically plausible world simulators, yet accurately evaluating such…
Ensemble of Pre-Trained Models for Long-Tailed Trajectory Prediction
Divya Thuremella, Yi Yang, Simon Wanna +2
This work explores the application of ensemble modeling to the multidimensional regression problem of trajectory prediction for vehicles in urban environments. As newer and bigger…
MinkOcc: Towards real-time label-efficient semantic occupancy prediction
Samuel Sze, Daniele De Martini, Lars Kunze
Developing 3D semantic occupancy prediction models often relies on dense 3D annotations for supervised learning, a process that is both labor and resource-intensive, underscoring t…
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…
A risk model and analysis method for the psychological safety of human and autonomous vehicles interaction
Yandika Sirgabsou, Benjamin Hardin, François Leblanc +5
The rapid advancement of artificial intelligence and autonomous driving technologies has significantly propelled the development of autonomous vehicles (AVs). However, psychologica…
GraphSCENE: On-Demand Critical Scenario Generation for Autonomous Vehicles in Simulation
Efimia Panagiotaki, Georgi Pramatarov, Lars Kunze +1
Testing and validating Autonomous Vehicle (AV) performance in safety-critical and diverse scenarios is crucial before real-world deployment. However, manually creating such scenari…
NAR-*ICP: Neural Execution of Classical ICP-based Pointcloud Registration Algorithms
Efimia Panagiotaki, Daniele De Martini, Lars Kunze +2
This study explores the intersection of neural networks and classical robotics algorithms through the Neural Algorithmic Reasoning (NAR) blueprint, enabling the training of neural…
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…
From Feature Importance to Natural Language Explanations Using LLMs with RAG
Sule Tekkesinoglu, Lars Kunze
As machine learning becomes increasingly integral to autonomous decision-making processes involving human interaction, the necessity of comprehending the model's outputs through co…
Risk-aware Trajectory Prediction by Incorporating Spatio-temporal Traffic Interaction Analysis
Divya Thuremella, Lewis Ince, Lars Kunze
To operate in open-ended environments where humans interact in complex, diverse ways, autonomous robots must learn to predict their behaviour, especially when that behavior is pote…
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…
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…
RobotCycle: Assessing Cycling Safety in Urban Environments
Efimia Panagiotaki, Tyler Reinmund, Stephan Mouton +9
This paper introduces RobotCycle, a novel ongoing project that leverages Autonomous Vehicle (AV) research to investigate how road infrastructure influences cyclist behaviour and sa…
Advancing Explainable Autonomous Vehicle Systems: A Comprehensive Review and Research Roadmap
Sule Tekkesinoglu, Azra Habibovic, Lars Kunze
Given the uncertainty surrounding how existing explainability methods for autonomous vehicles (AVs) meet the diverse needs of stakeholders, a thorough investigation is imperative t…
COBRA-PPM: A Causal Bayesian Reasoning Architecture Using Probabilistic Programming for Robot Manipulation Under Uncertainty
Ricardo Cannizzaro, Michael Groom, Jonathan Routley +2
Manipulation tasks require robots to reason about cause and effect when interacting with objects. Yet, many data-driven approaches lack causal semantics and thus only consider corr…
Real-time 3D semantic occupancy prediction for autonomous vehicles using memory-efficient sparse convolution
Samuel Sze, Lars Kunze
In autonomous vehicles, understanding the surrounding 3D environment of the ego vehicle in real-time is essential. A compact way to represent scenes while encoding geometric distan…