用大模型动态切换自动驾驶规划模式，提升复杂路况下的安全与效率。
LLM-Driven Scenario-Aware Planning for Autonomous Driving
- 通过大模型理解场景复杂度，自动切换高速与精准驾驶模式
- 在高密度交通中实现98.7%成功率，比基准方法快12.3%
- 适合需要高鲁棒性的自动驾驶系统研发者参考
针对自动驾驶中混合规划器切换框架（HPSF）在密集交通中难以可靠切换模式且控制更新频率低的问题，本文提出一种基于大语言模型（LLM）的自适应规划方法（LAP）。该方法根据场景复杂度，在低复杂度场景下采用高速行驶模式，在高复杂度场景下切换至精确驾驶模式，通过受限间隙生成高质量轨迹。LAP利用LLM进行场景理解，并将推理结果融入模式配置与运动规划的联合优化中，采用树搜索模型预测控制与交替最小化求解。在ROS中用Python实现，高保真仿真结果显示，LAP在行驶时间与成功率达到98.7%的同时，相较其他基准方法提速12.3%。
原文摘要 · Abstract (English)
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.
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