使用大型语言模型进行政策调整驱动全方位发展

摘要

将驾驶行为调整至新环境、习俗和法律是自动驾驶长期存在的问题，这一问题妨碍了自动驾驶车辆（AVs）的广泛部署。在本文中，我们提出了LLaDA，这是一个简单而强大的工具，使人类驾驶员和自动驾驶车辆能够通过调整其任务和运动计划来适应新位置的交通规则而在任何地方驾驶。LLaDA通过利用大型语言模型（LLMs）在解释当地驾驶手册中的交通规则方面的惊人零样本泛化能力来实现这一目标。通过广泛的用户研究，我们展示了LLaDA的指导在消除野外意外情况中的歧义方面是有用的。我们还展示了LLaDA在真实世界数据集中调整AV运动规划策略的能力；LLaDA在所有指标上均优于基准规划方法。请查看我们的网站以获取更多详细信息：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.

使用大型语言模型进行政策调整驱动全方位发展

Driving Everywhere with Large Language Model Policy Adaptation

摘要

Support