使用大型語言模型進行政策適應性的駕駛。

摘要

將駕駛行為調整至新環境、習俗和法律是自動駕駛中一個長期存在的問題，這阻礙了自動駕駛車輛（AVs）的廣泛部署。在本文中，我們提出了LLaDA，一個簡單但強大的工具，使人類駕駛員和自動駕駛車輛都能通過調整其任務和運動計劃來適應新地點的交通規則而實現無處不在的駕駛。LLaDA通過利用大型語言模型（LLMs）在解釋當地駕駛手冊中的交通規則時的令人印象深刻的零樣本泛化能力來實現這一目標。通過廣泛的用戶研究，我們展示了LLaDA的指導在消除野外意外情況方面的實用性。我們還展示了LLaDA在真實世界數據集中適應AV運動規劃策略的能力；LLaDA在所有指標上均優於基線規劃方法。請查看我們的網站以獲取更多詳細信息：https://boyiliee.github.io/llada。

English

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.

使用大型語言模型進行政策適應性的駕駛。

Driving Everywhere with Large Language Model Policy Adaptation

摘要

Support