22 citations · 40 across the 8 of their papers we have counts for
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cs.CV2025
LEAD: Minimizing Learner-Expert Asymmetry in End-to-End Driving
Long Nguyen, Micha Fauth, Bernhard Jaeger +4
Simulators can generate virtually unlimited driving data, yet imitation learning policies in simulation still struggle to achieve robust closed-loop performance. Motivated by this…
cs.LG2025
CaRL: Learning Scalable Planning Policies with Simple Rewards
Bernhard Jaeger, Daniel Dauner, Jens Beißwenger +3
We investigate reinforcement learning (RL) for privileged planning in autonomous driving. State-of-the-art approaches for this task are rule-based, but these methods do not scale t…