UA2C: Uncertainty-Aware Adaptive Action Chunking for Offline-to-Online Decision-Making in Mixed Traffic

Hongki Kim, Sangeun Park, Minhae Kwon

Keywords: Autonomous Driving System, Reinforcement Learning, Flow-Matching, Action Chunking

Abstract:

Autonomous driving requires sequential decision-making under partial observations and heterogeneous interactions with human-driven vehicles.
While action chunking provides a useful temporal abstraction by predicting multi-step future actions, fixed-length chunk execution can be unreliable when later actions are conditioned on stale observations.
We propose $\textbf{UA2C}$, an uncertainty-aware adaptive action chunking framework for offline-to-online reinforcement learning (RL) in mixed traffic.
UA2C first learns a flow-matching chunk policy from offline driving data and then refines the policy through online interaction.
To account for behaviorally diverse surrounding vehicles, UA2C incorporates a driving-style inference module that augments the policy with local behavior context.
During execution, UA2C estimates uncertainty from sampled action chunks and executes only a reliable prefix before replanning.
Experiments show that UA2C improves offline reward and control smoothness over a one-step baseline, and further improves online performance over fixed chunk execution.

Submission Number: 135