9 citations · 11 across the 12 of their papers we have counts for
4 papers · 1 filter
What Probing Reveals about Autonomous Driving: Linking Internal Prediction Errors to Ego Planning
Hyeonchang Jeon, Kyungbeom Kim, Eugene Vinitsky +1
Large-scale datasets and fast simulators have enabled improvements in driving policies that appear safe and robust, yet strong performance in nominal scenarios can still mask flawe…
Scaling Self-Play for End-to-End Driving
Luke Rowe, Roger Girgis, Rodrigue de Schaetzen +6
End-to-end autonomous driving models are typically trained on offline human-demonstration datasets that provide limited state coverage and often no closed-loop feedback, making the…
Beyond Self-Play and Scale: A Behavior Benchmark for Generalization in Autonomous Driving
Aron Distelzweig, Faris Janjoš, Andreas Look +7
Recent Autonomous Driving (AD) works such as GigaFlow and PufferDrive have unlocked Reinforcement Learning (RL) at scale as a training strategy for driving policies. Yet such polic…
Human-compatible driving partners through data-regularized self-play reinforcement learning
Daphne Cornelisse, Eugene Vinitsky
A central challenge for autonomous vehicles is coordinating with humans. Therefore, incorporating realistic human agents is essential for scalable training and evaluation of autono…