4 papers · 1 filter
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…
Poutine: Vision-Language-Trajectory Pre-Training and Reinforcement Learning Post-Training Enable Robust End-to-End Autonomous Driving
Luke Rowe, Rodrigue de Schaetzen, Roger Girgis +2
Maintaining good driving behavior in out-of-distribution scenarios remains a critical challenge in autonomous driving. A promising direction is to leverage the generalist knowledge…
Scenario Dreamer: Vectorized Latent Diffusion for Generating Driving Simulation Environments
Luke Rowe, Roger Girgis, Anthony Gosselin +3
We introduce Scenario Dreamer, a fully data-driven generative simulator for autonomous vehicle planning that generates both the initial traffic scene - comprising a lane graph and…
CtRL-Sim: Reactive and Controllable Driving Agents with Offline Reinforcement Learning
Luke Rowe, Roger Girgis, Anthony Gosselin +5
Evaluating autonomous vehicle stacks (AVs) in simulation typically involves replaying driving logs from real-world recorded traffic. However, agents replayed from offline data are…