MAPLE performs closed-loop multi-agent training of VLA driving models entirely in latent space using supervised fine-tuning followed by RL with safety, progress, and diversity rewards, reaching SOTA on Bench2Drive.
Diffrefiner: Coarse to fine trajectory planning via diffusion refinement with semantic interaction for end to end autonomous driving
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.RO 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
MAPLE: Latent Multi-Agent Play for End-to-End Autonomous Driving
MAPLE performs closed-loop multi-agent training of VLA driving models entirely in latent space using supervised fine-tuning followed by RL with safety, progress, and diversity rewards, reaching SOTA on Bench2Drive.