基于大语言模型的场景感知规划用于自动驾驶
LLM-Driven Scenario-Aware Planning for Autonomous Driving
AI总结：
本文提出LAP，一种基于大语言模型的自适应规划方法，通过场景理解与联合优化实现自动驾驶中的高速与精确驾驶平衡。
摘要：
混合规划切换框架（HPSF）用于自动驾驶需要在高速驾驶效率与密集交通中的安全操控之间取得平衡。现有HPSF方法往往无法可靠地进行模式切换或在拥堵环境中维持高效驾驶，这是因为启发式场景识别和低频控制更新。为了解决这一限制，本文提出LAP，一种由大语言模型（LLM）驱动的自适应规划方法，能够在低复杂度场景中进行高速驾驶，在高复杂度场景中进行精确驾驶，通过狭窄间隙生成高质量的轨迹。这通过利用LLM进行场景理解，并将其推断整合到模式配置和运动规划的联合优化中来实现。联合优化通过树搜索模型预测控制和交替最小化来解决。我们通过Python在机器人操作系统（ROS）中实现LAP。高保真模拟结果表明，所提出的LAP在驾驶时间和成功率方面均优于其他基准。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
