127 citations · 288 across the 53 of their papers we have counts for
9 papers · 1 filter
Clustered Randomized Smoothing for Stochastic Prediction Functions
Eduardo Figueiredo, Frederik Mathiesen, Julian Schumann +3
Modern stochastic predictors can model rich, multi-modal outcome distributions. However, this expressive power comes with challenges in ensuring robust predictions a critical r…
Emotion in an active inference model of human driving
Julian F. Schumann, Johan Engström, Ran Wei +3
Active inference has emerged as a principled framework for modeling adaptive behavior by balancing goal-directed action with uncertainty reduction. It has been successfully applied…
Knowing When to Move: Evidence Accumulation Models of Human Behavior in Traffic
Floor Bontje, Felix van Waveren, Leendert van Maanen +3
Evidence accumulation models provide a formal framework for studying decision making as a dynamic process unfolding over time. While these models have been extensively developed an…
General-purpose LLMs as Models of Human Driver Behavior: The Case of Simplified Merging
Samir H. A. Mohammad, Wouter Mooi, Arkady Zgonnikov
Human behavior models are essential as behavior references and for simulating human agents in virtual safety assessment of automated vehicles (AVs), yet current models face a trade…
Resolving space-sharing conflicts in road user interactions through uncertainty reduction: An active inference-based computational model
Julian F. Schumann, Johan Engström, Ran Wei +3
Understanding how road users resolve space-sharing conflicts is important both for traffic safety and the safe deployment of autonomous vehicles. While existing models have capture…
Linking Behaviour and Perception to Evaluate Meaningful Human Control over Partially Automated Driving
Ashwin George, Lucas Elbert Suryana, Lorenzo Flipse +5
Partial driving automation creates a tension: drivers remain legally responsible while being less active in control. Meaningful human control (MHC), a normative framework that can…
Evaluating randomized smoothing as a defense against adversarial attacks in trajectory prediction
Julian F. Schumann, Eduardo Figueiredo, Frederik Baymler Mathiesen +3
Accurate and robust trajectory prediction is essential for safe and efficient autonomous driving, yet recent work has shown that even state-of-the-art prediction models are highly…
CARE Drive A Framework for Evaluating Reason-Responsiveness of Vision Language Models in Automated Driving
Lucas Elbert Suryana, Farah Bierenga, Sanne van Buuren +6
Foundation models, including vision language models, are increasingly used in automated driving to interpret scenes, recommend actions, and generate natural language explanations.…
Using Feasible Action-Space Reduction by Groups to fill Causal Responsibility Gaps in Spatial Interactions
Ashwin George, Vassil Guenov, Arkady Zgonnikov +2
Heralding the advent of autonomous vehicles and mobile robots that interact with humans, responsibility in spatial interaction is burgeoning as a research topic. Even though metric…