用指令微调大模型实现更符合人类驾驶习惯的自动规划
Align2Act: Instruction-Tuned Models for Human-Aligned Autonomous Driving
- 将大模型转化为分步推理的可解释规划器，结合人类驾驶逻辑与交通规则
- 在nuPlan数据集上达到85.17分开环得分和66.96分闭环反应式得分
- 首次在真实世界闭环场景验证了大模型规划的人类相似性与安全性
复杂场景下的运动规划是自动驾驶的核心挑战。传统方法依赖预设规则或从驾驶数据中学习生成轨迹，近期研究尝试利用大语言模型（LLM）进行决策，但其是否真正捕捉人类驾驶逻辑尚不明确。本文提出Align2Act框架，将指令微调的LLM转化为与人类行为对齐的可解释规划器。基于人类推理模式（如预判危险、在路口让行）和交通规则（如红灯停车、保持车道边界），构建结构化驾驶指令。通过Align2ActChain模块引导分步推理，生成可解释的理由和安全轨迹。在nuPlan数据集一百万场景上，使用LoRA微调LLaMA-2-7B，开环得分为85.17，闭环比对非反应式为70.31，反应式为66.96。相比以往聚焦合成数据或开环设置的工作，本方法在真实世界nuPlan闭环基准上显著提升规划质量与人类相似性。消融实验表明，结构化推理显著优于基线LLM规划器。
原文摘要 · Abstract (English)
Motion planning in complex scenarios is a core challenge in autonomous driving. Conventional methods apply predefined rules or learn from driving data to generate trajectories, while recent approaches leverage large language models (LLMs) for decision-making. However, it remains unclear whether LLMs truly capture human driving logic. We propose Align2Act, a motion planning framework that transforms instruction-tuned LLMs into interpretable planners aligned with human behavior. We derive structured driving instructions based on human reasoning patterns (e.g., anticipate hazards, yield at intersections) and traffic rules (e.g., stop at red lights, maintain lane boundaries). Our Align2ActChain module guides step-by-step reasoning to produce both an interpretable rationale and a safe trajectory. By fine-tuning LLaMA-2-7B with LoRA on one million scenarios from the nuPlan dataset, our method achieves an open-loop score of 85.17 and closed-loop scores of 70.31 (non-reactive) and 66.96 (reactive) on Test14-random. Unlike prior work focused on synthetic or open-loop settings, we demonstrate improved planning quality and human-likeness on the real-world nuPlan closed-loop benchmark. Ablation studies confirm that structured reasoning significantly improves performance over baseline LLM planners.
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