Driving Everywhere with Large Language Model Policy Adaptation
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.
65c59a3c73a47fa85dbe5828
Boyi Li
admin_assigned
2024-02-09T10:26:06.865Z
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Boyi Li
user
Boyiliee
65c59a3c73a47fa85dbe5829
Yue Wang
65c59a3c73a47fa85dbe582a
Jiageng Mao
65c59a3c73a47fa85dbe582b
Boris Ivanovic
65c59a3c73a47fa85dbe582c
Sushant Veer
65c59a3c73a47fa85dbe582d
Karen Leung
65c59a3c73a47fa85dbe582e
Marco Pavone
Driving Everywhere with Large Language Model Policy Adaptation
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.
LLaDA, utilizing the zero-shot capabilities of large language models, adapts driving behavior and motion plans to local traffic rules, enhancing both human and autonomous vehicle performance.
zero-shot generalizability large language models traffic rules user study motion planning policies real-world datasets