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