  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
https://github.com/bonbon-rj/InstructDriver.
