Hybrid planner switching framework (HPSF) for autonomous driving needs to reconcile high-speed driving efficiency with safe maneuvering in dense traffic. Existing HPSF methods often fail to make reliable mode transitions or sustain efficient driving in congested environments, owing to heuristic scene recognition and low-frequency control updates. To address the limitation, this paper proposes LAP, a large language model (LLM) driven, adaptive planning method, which switches between high-speed driving in low-complexity scenes and precise driving in high-complexity scenes, enabling high qualities of trajectory generation through confined gaps. This is achieved by leveraging LLM for scene understanding and integrating its inference into the joint optimization of mode configuration and motion planning. The joint optimization is solved using tree-search model predictive control and alternating minimization. We implement LAP by Python in Robot Operating System (ROS). High-fidelity simulation results show that the proposed LAP outperforms other benchmarks in terms of both driving time and success rate.

翻译：自动驾驶的混合规划器切换框架（HPSF）需要兼顾高速驾驶效率与密集交通中的安全操控。现有的HPSF方法由于采用启发式的场景识别和低频控制更新，往往难以实现可靠的模式切换或在拥堵环境中维持高效驾驶。为克服这一局限，本文提出LAP——一种由大语言模型（LLM）驱动的自适应规划方法，该方法能在低复杂度场景的高速驾驶与高复杂度场景的精确驾驶之间进行切换，从而通过狭窄间隙实现高质量的轨迹生成。这是通过利用LLM进行场景理解，并将其推理结果融入模式配置与运动规划的联合优化中实现的。该联合优化问题通过树搜索模型预测控制和交替最小化方法求解。我们在机器人操作系统（ROS）中使用Python实现了LAP。高保真仿真结果表明，所提出的LAP在驾驶时间和成功率方面均优于其他基准方法。