自动驾驶中基于大语言模型的多规划器调度的开放式指令实现
Open-Ended Instruction Realization with LLM-Enabled Multi-Planner Scheduling in Autonomous Vehicles
对做自动驾驶视觉感知与规划的研究者，这篇提供了一个把开放语言指令经 LLM 解析为可执行脚本、再调度多个 MPC 规划器的分层接口，可借鉴其「语义推理与底层控制解耦、跨时间尺度」的设计，以及闭环指令实现基准来评测语言条件规划。
原文依据 generates executable scripts that schedule multiple model predictive control (MPC)-based motion planners based on real-time feedback
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论大多数人机交互（HMI）研究忽视了自动驾驶（AD）中乘客的操纵需求。自然语言提供了直观的接口，但在不牺牲可解释性与可追溯性的前提下，将乘客的开放式指令转化为控制信号仍是一项挑战。本研究提出一种指令实现框架，利用大语言模型（LLM）解释指令，生成可执行脚本，依据实时反馈调度多个基于模型预测控制（MPC）的运动规划器，并将规划出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，建立起从高层指令到低层动作的透明、可追溯的决策链。由于缺乏高保真评测工具，本研究引入了一个闭环设定下开放式指令实现的基准。大量实验表明，该框架相比指令实现基线显著提升了任务完成率，降低了 LLM 查询成本，达到了与专用自动驾驶方法相当的安全性与合规性，并对 LLM 推理延迟表现出相当大的容忍度。如需更多定性图示与更清晰的理解，请参阅补充材料。
英文原文摘要 1401 字符
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, please refer to Supplementary Material.