127 citations · 288 across the 53 of their papers we have counts for
53 papers
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…
Controllable Generative Trajectory Prediction via Weak Preference Alignment
Yongxi Cao, Julian F. Schumann, Jens Kober +2
Deep generative models such as conditional variational autoencoders (CVAEs) have shown great promise for predicting trajectories of surrounding agents in autonomous vehicle plannin…
How Much Is Too Much? Adaptive, Context-Aware Risk Detection in Naturalistic Driving
Amir Hossein Kalantari, Eleonora Papadimitriou, Arkady Zgonnikov +1
Reliable risk identification based on driver behavior data underpins real-time safety feedback, fleet risk management, and evaluation of driver-assist systems. While naturalistic d…
STEP: Structured Training and Evaluation Platform for benchmarking trajectory prediction models
Julian F. Schumann, Anna Mészáros, Jens Kober +1
While trajectory prediction plays a critical role in enabling safe and effective path-planning in automated vehicles, standardized practices for evaluating such models remain under…
Towards Human-Centric Evaluation of Interaction-Aware Automated Vehicle Controllers: A Framework and Case Study
Federico Scarì, Olger Siebinga, Arkady Zgonnikov
As automated vehicles (AVs) increasingly integrate into mixed-traffic environments, evaluating their interaction with human-driven vehicles (HDVs) becomes critical. In most researc…
A Framework for Ethical Decision-Making in Automated Vehicles through Human Reasons-based Supervision
Lucas Elbert Suryana, Saeed Rahmani, Simeon Craig Calvert +2
Ethical dilemmas are a common challenge in everyday driving, requiring human drivers to balance competing priorities such as safety, efficiency, and rule compliance. However, much…
Principles and Reasons Behind Automated Vehicle Decisions in Ethically Ambiguous Everyday Scenarios
Lucas Elbert Suryana, Simeon Calvert, Arkady Zgonnikov +1
Automated vehicles (AVs) increasingly encounter ethically ambiguous situations in everyday driving--scenarios involving conflicting human interests and lacking clearly optimal cour…
A Framework for Human-Reason-Aligned Trajectory Evaluation in Automated Vehicles
Lucas Elbert Suryana, Saeed Rahmani, Simeon Craig Calvert +2
One major challenge for the adoption and acceptance of automated vehicles (AVs) is ensuring that they can make sound decisions in everyday situations that involve ethical tension.…
Active inference as a unified model of collision avoidance behavior in human drivers
Julian F. Schumann, Johan Engström, Leif Johnson +4
Collision avoidance -- involving a rapid threat detection and quick execution of the appropriate evasive maneuver -- is a critical aspect of driving. However, existing models of hu…
Realistic Adversarial Attacks for Robustness Evaluation of Trajectory Prediction Models via Future State Perturbation
Julian F. Schumann, Jeroen Hagenus, Frederik Baymler Mathiesen +1
Trajectory prediction is a key element of autonomous vehicle systems, enabling them to anticipate and react to the movements of other road users. Evaluating the robustness of predi…
Feasible Action Space Reduction for Quantifying Causal Responsibility in Continuous Spatial Interactions
Ashwin George, Luciano Cavalcante Siebert, David A. Abbink +1
Understanding the causal influence of one agent on another agent is crucial for safely deploying artificially intelligent systems such as automated vehicles and mobile robots into…
Understanding Driver Cognition and Decision-Making Behaviors in High-Risk Scenarios: A Drift Diffusion Perspective
Heye Huang, Zheng Li, Hao Cheng +4
Ensuring safe interactions between autonomous vehicles (AVs) and human drivers in mixed traffic systems remains a major challenge, particularly in complex, high-risk scenarios. Thi…
Hybrid Human-Machine Perception via Adaptive LiDAR for Advanced Driver Assistance Systems
Federico Scarì, Nitin Jonathan Myers, Chen Quan +1
Accurate environmental perception is critical for advanced driver assistance systems (ADAS). Light detection and ranging (LiDAR) systems play a crucial role in ADAS; they can relia…
In the driver's mind: modeling the dynamics of human overtaking decisions in interactions with oncoming automated vehicles
Samir H. A. Mohammad, Haneen Farah, Arkady Zgonnikov
Understanding human behavior in overtaking scenarios is crucial for enhancing road safety in mixed traffic with automated vehicles (AVs). Computational models of behavior play a pi…
Are you sure? Modelling Drivers' Confidence Judgments in Left-Turn Gap Acceptance Decisions
Floor Bontje, Arkady Zgonnikov
When a person makes a decision, it is automatically accompanied by a subjective probability judgment of the decision being correct, in other words, a confidence judgment. A better…
Safe Spot: Perceived safety of dominant and submissive appearances of quadruped robots in human-robot interactions
Nanami Hashimoto, Emma Hagens, Arkady Zgonnikov +1
Unprecedented possibilities of quadruped robots have driven much research on the technical aspects of these robots. However, the social perception and acceptability of quadruped ro…