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cs.RO2026
CANMOT: Class-Aware Noise Modeling for Multi-Object Tracking in Autonomous Driving
Timo Osterburg, Stefan Schütte, Torsten Bertram
Kalman filter (KF)-based multi-object tracking (MOT) remains a strong baseline for autonomous driving due to its strong performance, computational efficiency and interpretability.…
cs.RO2025
HiLO: High-Level Object Fusion for Autonomous Driving using Transformers
Timo Osterburg, Franz Albers, Christopher Diehl +2
The fusion of sensor data is essential for a robust perception of the environment in autonomous driving. Learning-based fusion approaches mainly use feature-level fusion to achieve…
cs.RO2025
LoRD: Adapting Differentiable Driving Policies to Distribution Shifts
Christopher Diehl, Peter Karkus, Sushant Veer +2
Distribution shifts between operational domains can severely affect the performance of learned models in self-driving vehicles (SDVs). While this is a well-established problem, pri…