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New RAPiD framework distills diffusion planners for faster autonomous driving

By PulseAugur Editorial · [1 sources] ·

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 →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New RAPiD framework distills diffusion planners for faster autonomous driving

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

  1. arXiv cs.AI TIER_1 English(EN) · Ruturaj Reddy, Hrishav Bakul Barua, Junn Yong Loo, Thanh Thi Nguyen, Ganesh Krishnasamy ·

    RAPiD: Reward-Guided Consistency Distillation of Diffusion Planners for Real-Time Autonomous Driving

    arXiv:2602.07339v2 Announce Type: replace Abstract: Diffusion-based trajectory planners can model multi-modal driving behavior, but their iterative denoising process introduces a latency bottleneck for real-time closed-loop deployment. We present RAPiD, a reward-guided consistenc…

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