13 citations · 23 across the 16 of their papers we have counts for
4 papers · 1 filter
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…