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、习俗及法规的驾驶行为调整是自动驾驶领域长期存在的难题，阻碍了自动驾驶车辆（AVs）的广泛部署。本文提出LLaDA——一个简洁而强大的工具，能够通过将人类驾驶员与自动驾驶车辆的任务及运动规划适配至新地点的交通规则，协助其实现全域驾驶。LLaDA利用大型语言模型（LLMs）在解读本地驾驶员手册中交通规则时展现的非凡零样本泛化能力达成该目标。通过大规模用户研究，我们验证了LLaDA的指令在澄清野外突发状况中的有效性。同时，我们在真实世界数据集中证明了LLaDA对自动驾驶运动规划策略的适配能力；在全部评估指标上，LLaDA均优于基线规划方法。更多详情请访问网站：https://boyiliee.github.io/llada。