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, rule-based planners generalize well, but
might fail to handle scenarios that require complex driving maneuvers.
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.