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.

翻译：适应新环境、习俗和法律的驾驶行为是自动驾驶领域长期存在的问题，阻碍了自动驾驶车辆（AVs）的广泛部署。本文提出LLaDA——一种简洁而强大的工具，能够通过将人类驾驶员和自动驾驶车辆的任务与运动规划适配到新地点的交通规则中，实现全域驾驶。LLaDA通过利用大语言模型（LLMs）在解读当地驾驶员手册中交通规则时展现出的卓越零样本泛化能力来实现这一目标。通过广泛的用户研究，我们证明LLaDA的指令有助于澄清野外遇到的突发状况。同时，我们展示了LLaDA在真实世界数据集中适配自动驾驶运动规划策略的能力：在所有评估指标上，LLaDA均优于基线规划方法。更多详情请访问我们的网站：https://boyiliee.github.io/llada。