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）的日益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的规划器仍面临重大挑战，包括较高的资源消耗与较长的推理时间，这对实际部署构成了显著障碍。针对这些挑战，我们提出了AsyncDriver——一种新型的异步LLM增强闭环框架，旨在利用LLM生成的场景关联指令特征来指导实时规划器进行精确且可控的轨迹预测。一方面，我们的方法突显了LLM在理解与推理矢量化场景数据及系列路径指令方面的强大能力，证明了其对实时规划器的有效辅助作用。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引入的计算成本。实验表明，我们的方法在nuPlan的挑战性场景中实现了优异的闭环评估性能。