基于LLM的多规划器调度的开放式指令实现用于自动驾驶车辆
Open-Ended Instruction Realization with LLM-Enabled Multi-Planner Scheduling in Autonomous Vehicles
AI总结：
本文提出一种基于大语言模型的指令实现框架，通过实时反馈调度多个基于模型预测控制的运动规划器，提高任务完成率并降低LLM查询成本，同时确保安全性和可追溯性。
摘要：
大多数人机交互（HMI）研究忽视了自动驾驶（AD）中乘客的 maneuvering 需求。自然语言提供直观的接口，但将乘客的开放式指令转化为控制信号，而不牺牲可解释性和可追溯性，仍是一个挑战。本文提出了一种指令实现框架，利用大语言模型（LLM）来解析指令，生成可执行脚本，根据实时反馈调度多个基于模型预测控制（MPC）的运动规划器，并将计划的轨迹转换为控制信号。这种以调度为中心的设计在不同时间尺度上将语义推理与车辆控制解耦，建立了从高层指令到低层动作的透明、可追溯的决策链。由于缺乏高保真度的评估工具，本文引入了一个闭环设置下的开放式指令实现基准。全面的实验表明，该框架在指令实现基线上显著提高了任务完成率，降低了LLM查询成本，实现了与专用AD方法相当的安全性和合规性，并表现出对LLM推理延迟的显著容忍性。
英文摘要：
Most Human-Machine Interaction (HMI) research overlooks the maneuvering needs of passengers in autonomous driving (AD). Natural language offers an intuitive interface, yet translating passenger open-ended instructions into control signals, without sacrificing interpretability and traceability, remains a challenge. This study proposes an instruction-realization framework that leverages a large language model (LLM) to interpret instructions, generates executable scripts that schedule multiple model predictive control (MPC)-based motion planners based on real-time feedback, and converts planned trajectories into control signals. This scheduling-centric design decouples semantic reasoning from vehicle control at different timescales, establishing a transparent, traceable decision-making chain from high-level instructions to low-level actions. Due to the absence of high-fidelity evaluation tools, this study introduces a benchmark for open-ended instruction realization in a closed-loop setting. Comprehensive experiments reveal that the framework significantly improves task-completion rates over instruction-realization baselines, reduces LLM query costs, achieves safety and compliance on par with specialized AD approaches, and exhibits considerable tolerance to LLM inference latency. For more qualitative illustrations and a clearer understanding.
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。
