Driving Everywhere with Large Language Model Policy Adaptation
Authors: name: Boyi Li, name: Yue Wang, name: Jiageng Mao, name: Boris Ivanovic, name: Sushant Veer, name: Karen Leung, name: Marco Pavone

Adapting driving behavior to new environments, customs, and laws is a
long-standing problem in autonomous driving, precluding the widespread
deployment of autonomous vehicles (AVs). In this paper, we present LLaDA, a
simple yet powerful tool that enables human drivers and autonomous vehicles
alike to drive everywhere by adapting their tasks and motion plans to traffic
rules in new locations. LLaDA achieves this by leveraging the impressive
zero-shot generalizability of large language models (LLMs) in interpreting the
traffic rules in the local driver handbook. Through an extensive user study, we
show that LLaDA's instructions are useful in disambiguating in-the-wild
unexpected situations. We also demonstrate LLaDA's ability to adapt AV motion
planning policies in real-world datasets; LLaDA outperforms baseline planning
approaches on all our metrics. Please check our website for more details:
https://boyiliee.github.io/llada.