Despite real-time planners exhibiting remarkable performance in autonomous driving, the growing exploration of Large Language Models (LLMs) has opened avenues for enhancing the interpretability and controllability of motion planning. Nevertheless, LLM-based planners continue to encounter significant challenges, including elevated resource consumption and extended inference times, which pose substantial obstacles to practical deployment. In light of these challenges, we introduce AsyncDriver, a new asynchronous LLM-enhanced closed-loop framework designed to leverage scene-associated instruction features produced by LLM to guide real-time planners in making precise and controllable trajectory predictions. On one hand, our method highlights the prowess of LLMs in comprehending and reasoning with vectorized scene data and a series of routing instructions, demonstrating its effective assistance to real-time planners. On the other hand, the proposed framework decouples the inference processes of the LLM and real-time planners. By capitalizing on the asynchronous nature of their inference frequencies, our approach have successfully reduced the computational cost introduced by LLM, while maintaining comparable performance. Experiments show that our approach achieves superior closed-loop evaluation performance on nuPlan's challenging scenarios.

翻译：尽管实时规划器在自动驾驶中展现出卓越性能，但大语言模型（LLMs）的深入探索为运动规划的可解释性与可控性提升开辟了新途径。然而，基于LLM的规划器仍面临严峻挑战，包括高昂的资源消耗与冗长的推理时间，这对其实际部署构成了重大障碍。针对这些挑战，我们提出了AsyncDriver——一种新型异步LLM增强的闭环框架，旨在利用LLM生成的场景关联指令特征来引导实时规划器做出精确且可控的轨迹预测。一方面，我们的方法突显了LLM在理解矢量化场景数据与系列路径指令并进行推理方面的强大能力，证明其能有效辅助实时规划器。另一方面，所提框架解耦了LLM与实时规划器的推理过程。通过利用两者推理频率的异步特性，我们的方法在保持相当性能的同时，成功降低了LLM引入的计算开销。实验表明，我们的方法在nuPlan挑战性场景中实现了优异的闭环评估性能。