6 papers
Uncertainty-Driven Self-Play for Robust Multi-Agent Driving Policies
Sajad Marvi, Julian Schmidt, Alessandro Canevaro +2
Learning robust driving policies that generalize beyond their training domain remains a central challenge in motion planning. We propose an uncertainty-driven self-play scheme, in…
Learning Appearance and Motion Cues for Panoptic Tracking
Juana Valeria Hurtado, Sajad Marvi, Rohit Mohan +1
Panoptic tracking enables pixel-level scene interpretation of videos by integrating instance tracking in panoptic segmentation. This provides robots with a spatio-temporal understa…
Evidential Uncertainty Estimation for Multi-Modal Trajectory Prediction
Sajad Marvi, Christoph Rist, Julian Schmidt +2
Accurate trajectory prediction is crucial for autonomous driving, yet uncertainty in agent behavior and perception noise makes it inherently challenging. While multi-modal trajecto…
Traffic and Safety Rule Compliance of Humans in Diverse Driving Situations
Michael Kurenkov, Sajad Marvi, Julian Schmidt +6
The increasing interest in autonomous driving systems has highlighted the need for an in-depth analysis of human driving behavior in diverse scenarios. Analyzing human data is cruc…
Uncertainty-aware LiDAR Panoptic Segmentation
Kshitij Sirohi, Sajad Marvi, Daniel Büscher +1
Modern autonomous systems often rely on LiDAR scanners, in particular for autonomous driving scenarios. In this context, reliable scene understanding is indispensable. Current lear…
Uncertainty-aware Panoptic Segmentation
Kshitij Sirohi, Sajad Marvi, Daniel Büscher +1
Reliable scene understanding is indispensable for modern autonomous systems. Current learning-based methods typically try to maximize their performance based on segmentation metric…