Driving Intents Amplify Planning-Oriented Reinforcement Learning
Continuous-action policies trained on a single demonstrated trajectory per scene suffer from mode collapse: samples cluster around the demonstrated maneuver and the policy cannot represent semantically distinct alternatives. Under preference-based evaluation, this caps best-of-N performance -- even oracle selection cannot recover what the sampling distribution does not contain. We introduce DIAL, a two-stage Driving-Intent-Amplified reinforcement Learning framework for preference-aligned continuous-action driving policies. In the first stage, DIAL conditions the flow-matching action head on a discrete intent label with classifier-free guidance (CFG), which expands the sampling distributio...
| Authors | Hengtong Lu, Victor Shea-Jay Huang, Chengmin Yang et al. |
|---|---|
| Primary category | cs.RO |
| Published | 12 May 2026 |
| Last updated at source | 14 May 2026 |
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