  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.
