Scaling Self-Play for End-to-End Driving

arXiv:2606.19641v1 Announce Type: new Abstract: 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 them prone to compounding errors when deployed in closed-loop and brittle