title: Driving Everywhere with Large Language Model Policy Adaptation
summary: 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.
paper_authors._id: 65c59a3c73a47fa85dbe5828
paper_authors.name: Boyi Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-02-09T10:26:06.865Z
paper_authors._id: 620dd3888528f797e88cb9b5
paper_authors.avatarUrl: /avatars/af04728788d78fe7d6375e19e32a535e.svg
paper_authors.fullname: Boyi Li
paper_authors.type: user
paper_authors.user: Boyiliee
paper_authors._id: 65c59a3c73a47fa85dbe5829
paper_authors.name: Yue Wang
paper_authors._id: 65c59a3c73a47fa85dbe582a
paper_authors.name: Jiageng Mao
paper_authors._id: 65c59a3c73a47fa85dbe582b
paper_authors.name: Boris Ivanovic
paper_authors._id: 65c59a3c73a47fa85dbe582c
paper_authors.name: Sushant Veer
paper_authors._id: 65c59a3c73a47fa85dbe582d
paper_authors.name: Karen Leung
paper_authors._id: 65c59a3c73a47fa85dbe582e
paper_authors.name: Marco Pavone
paper_title: Driving Everywhere with Large Language Model Policy Adaptation
paper_summary: 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.
paper_ai_keywords: large language models (LLMs) zero-shot generalizability traffic rules driver handbook in-the-wild unexpected situations autonomous vehicles (AVs) motion planning policies