Motion planning in complex scenarios is the core challenge in autonomous
driving. Conventional methods apply predefined rules or learn from driving data
to plan the future trajectory. Recent methods seek the knowledge preserved in
large language models (LLMs) and apply them in the driving scenarios. Despite
the promising results, it is still unclear whether the LLM learns the
underlying human logic to drive. In this paper, we propose an InstructDriver
method to transform LLM into a motion planner with explicit instruction tuning
to align its behavior with humans. We derive driving instruction data based on
human logic (e.g., do not cause collisions) and traffic rules (e.g., proceed
only when green lights). We then employ an interpretable InstructChain module
to further reason the final planning reflecting the instructions. Our
InstructDriver allows the injection of human rules and learning from driving
data, enabling both interpretability and data scalability. Different from
existing methods that experimented on closed-loop or simulated settings, we
adopt the real-world closed-loop motion planning nuPlan benchmark for better
evaluation. InstructDriver demonstrates the effectiveness of the LLM planner in
a real-world closed-loop setting. Our code is publicly available at
this https URL

在自动驾驶中，复杂场景下的动作规划是核心挑战。本文提出了一种将大型语言模型 (LLMs) 转化为具有明确指令调整能力的运动规划器的 InstructDriver 方法，以对齐其行为与人类相符。通过基于人类逻辑与交通规则来生成驾驶指令数据，并采用可解释的 InstructChain 模块进行最终规划推理，实现了注入人类规则与学习驾驶数据的目标，使其具备可解释性与数据可扩展性。与现有的在封闭环或模拟设置中进行实验的方法不同，我们采用真实世界的封闭环动作规划 nuPlan 基准进行更好的评估。InstructDriver 在真实世界的封闭环设置中展示了 LLM 规划器的有效性。我们的代码在此链接公开可获得。