用奖励引导的蒸馏方法，让扩散模型驾驶规划器提速5.5倍且保持安全
RAPiD: Reward-Guided Consistency Distillation of Diffusion Planners for Real-Time Autonomous Driving
- 用冻结的扩散教师模型生成确定性去噪步骤，结合低噪声锚点训练少步一致性学生
- 在nuPlan上推理延迟从100.91毫秒降至18.41毫秒，速度提升5.5倍
- 适合实时自动驾驶系统，尤其在交互式长尾场景中表现优异
基于扩散的轨迹规划器能建模多模式驾驶行为，但其迭代去噪过程导致实时闭环部署时存在延迟瓶颈。本文提出RAPiD，一种奖励引导的一致性蒸馏框架，将预训练的DiffusionPlanner压缩为少步一致性学生模型，同时保留多模式轨迹生成能力。学生模型通过冻结的扩散教师模型提供的确定性去噪步骤进行训练，并结合低噪声数据锚点，使生成轨迹保持与专家示范一致。为实现安全感知蒸馏，我们在平衡混合的真值日志回放与扩散规划器轨迹上训练隐式Q学习判别器，使用改进的PDM风格奖励进行轨迹级监督，超越传统模仿学习。部署时，2步学生生成K条轨迹，训练好的判别器根据状态进行最优选择。在nuPlan上，RAPiD在非反应式闭环分割中性能与扩散教师相当，在反应式分割中仍具竞争力，整体推理延迟从100.91毫秒降至18.41毫秒，提速5.5倍。在interPlan上，其综合得分高于所有学习型方法，展示出在交互式长尾场景中的良好泛化能力。结果表明，奖励引导的一致性蒸馏可将预训练扩散规划器转化为少步闭环规划器，显著降低推理开销，同时保持面向安全的轨迹选择能力。
原文摘要 · Abstract (English)
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 consistency distillation framework that distills a pretrained DiffusionPlanner into a few-step consistency student while retaining multi-modal trajectory generation. The student is trained using deterministic teacher denoising steps from the frozen diffusion planner, together with a low-noise data anchor that keeps generated trajectories grounded in expert demonstrations. To make distillation safety-aware, we train an Implicit Q-Learning critic on a balanced mixture of ground-truth log-replay and DiffusionPlanner rollout trajectories, each scored using a modified PDM-style reward, providing trajectory-level supervision beyond conventional imitation learning. During deployment, the 2-step student generates K trajectories, and the trained critic performs best-of-K trajectory selection conditioned on the latent state. On nuPlan, RAPiD maintains comparable performance to the diffusion teacher on non-reactive closed-loop splits and remains competitive on reactive splits, while reducing complete-pipeline inference latency from 100.91 ms to 18.41 ms, corresponding to a 5.5x speedup. On interPlan, RAPiD achieves the highest aggregate score among learning-based methods, demonstrating competitive generalization in interactive long-tail scenarios. These results show that reward-guided consistency distillation can convert a pretrained diffusion planner into a few-step closed-loop planner that substantially reduces inference cost while preserving safety-oriented trajectory selection. The official website of this work is: https://github.com/ruturajreddy/RAPiD
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