New RAPiD framework distills diffusion planners for faster autonomous driving

Researchers have developed RAPiD, a new framework designed to distill diffusion-based trajectory planners into faster, few-step models for real-time autonomous driving. This method uses reward-guided consistency distillation to maintain multi-modal behavior while significantly reducing latency. The RAPiD framework incorporates safety-aware training with an Implicit Q-Learning critic and demonstrates competitive performance on benchmarks like nuPlan and interPlan, achieving a 5.5x speedup in inference time. AI

IMPACT Accelerates real-time autonomous driving capabilities by significantly reducing inference latency for trajectory planning.

RANK_REASON The cluster contains a research paper detailing a new method for autonomous driving planning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

• DiffusionPlanner
• Implicit Q-Learning
• nuPlan
• Ruturaj Reddy

• paper
• product
• infra

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