collaborators

16 papers

cs.HC2026

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…

cs.HC2026

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…

cs.HC2026

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…

cs.LG2026

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…

cs.CV2026

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…

cs.RO2026

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…