Diffusion models have demonstrated strong capabilities for modeling human-like driving behaviors in autonomous driving, but their iterative sampling process induces substantial latency, and operating directly on raw trajectory points forces the model to spend capacity on low-level kinematics, rather than high-level multi-modal semantics. To address these limitations, we propose LAtent Planner (LAP), a framework that plans in a VAE-learned latent space that disentangles high-level intents from low-level kinematics, enabling our planner to capture rich, multi-modal driving strategies. We further introduce a fine-grained feature distillation mechanism to guide a better interaction and fusion between the high-level semantic planning space and the vectorized scene context. Notably, LAP can produce high-quality plans in one single denoising step, substantially reducing computational overhead. Through extensive evaluations on the large-scale nuPlan benchmark, LAP achieves state-of-the-art closed-loop performance among learning-based planning methods, while demonstrating an inference speed-up of at most 10 times over previous SOTA approaches. Code will be released at: https://github.com/jhz1192/Latent-Planner.

翻译：扩散模型在自动驾驶中已展现出模拟类人驾驶行为的强大能力，但其迭代采样过程会引入显著延迟，且直接在原始轨迹点上操作迫使模型将计算资源消耗于低层运动学而非高层多模态语义。为应对这些局限，我们提出潜在规划器（LAP），该框架在VAE学习的潜在空间中进行规划，该空间将高层意图与低层运动学解耦，使我们的规划器能够捕捉丰富多样的驾驶策略。我们进一步引入细粒度特征蒸馏机制，以引导高层语义规划空间与矢量化场景上下文之间实现更优的交互与融合。值得注意的是，LAP仅需单步去噪即可生成高质量规划方案，显著降低了计算开销。通过在大型nuPlan基准上的广泛评估，LAP在学习型规划方法中实现了最先进的闭环性能，同时推理速度较先前SOTA方法最高提升10倍。代码将发布于：https://github.com/jhz1192/Latent-Planner。