13 citations · 23 across the 16 of their papers we have counts for
16 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…
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…
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…
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…
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…
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…
A survey on robustness in trajectory prediction for autonomous vehicles
Jeroen Hagenus, Frederik Baymler Mathiesen, Julian F. Schumann +1
Autonomous vehicles rely on accurate trajectory prediction to inform decision-making processes related to navigation and collision avoidance. However, current trajectory prediction…
ROME: Robust Multi-Modal Density Estimator
Anna Mészáros, Julian F. Schumann, Javier Alonso-Mora +2
The estimation of probability density functions is a fundamental problem in science and engineering. However, common methods such as kernel density estimation (KDE) have been demon…
Smooth-Trajectron++: Augmenting the Trajectron++ behaviour prediction model with smooth attention
Frederik S. B. Westerhout, Julian F. Schumann, Arkady Zgonnikov
Understanding traffic participants' behaviour is crucial for predicting their future trajectories, aiding in developing safe and reliable planning systems for autonomous vehicles.…
Using Models Based on Cognitive Theory to Predict Human Behavior in Traffic: A Case Study
Julian F. Schumann, Aravinda Ramakrishnan Srinivasan, Jens Kober +2
The development of automated vehicles has the potential to revolutionize transportation, but they are currently unable to ensure a safe and time-efficient driving style. Reliable m…
The COMMOTIONS Urban Interactions Driving Simulator Study Dataset
Aravinda Ramakrishnan Srinivasan, Julian Schumann, Yueyang Wang +7
Accurate modelling of road user interaction has received lot of attention in recent years due to the advent of increasingly automated vehicles. To support such modelling, there is…
TrajFlow: Learning Distributions over Trajectories for Human Behavior Prediction
Anna Mészáros, Julian F. Schumann, Javier Alonso-Mora +2
Predicting the future behavior of human road users is an important aspect for the development of risk-aware autonomous vehicles. While many models have been developed towards this…
Benchmark for Models Predicting Human Behavior in Gap Acceptance Scenarios
Julian Frederik Schumann, Jens Kober, Arkady Zgonnikov
Autonomous vehicles currently suffer from a time-inefficient driving style caused by uncertainty about human behavior in traffic interactions. Accurate and reliable prediction mode…
A machine learning approach for fighting the curse of dimensionality in global optimization
Julian F. Schumann, Alejandro M. Aragón
Finding global optima in high-dimensional optimization problems is extremely challenging since the number of function evaluations required to sufficiently explore the search space…