Although planning is a crucial component of the autonomous driving stack, researchers have
yet to develop robust
planning algorithms that are capable of safely handling the diverse range of possible
driving scenarios. Learning-based
planners suffer from overfitting and poor long-tail performance. On the other hand,
planners generalize well, but might fail to handle scenarios that require complex driving
To address these limitations, we investigate the possibility of leveraging the common-sense
reasoning capabilities of Large Language Models (LLMs) such as GPT4 and Llama2 to generate
plans for self-driving vehicles. In particular, we develop a novel hybrid planner
that leverages a conventional rule-based planner in conjunction with an LLM-based planner.
Guided by commonsense reasoning abilities of LLMs, our approach navigates complex scenarios
which existing planners struggle with, produces
well-reasoned outputs while also remaining grounded through working alongside the rule-based
approach. Through extensive
evaluation on the nuPlan benchmark, we achieve state-of-the-art performance, outperforming
all existing pure learning- and rule-based methods across most metrics.