面向自动驾驶系统的XLM：全面综述
XLM for Autonomous Driving Systems: A Comprehensive Review
AI总结：
本综述全面梳理了XLM（视觉大模型与多模态大语言模型）在自动驾驶系统中的应用潜力，回顾相关架构、工具与框架，阐述现有部署方法，并指出部署挑战与未来研究方向。
摘要：
大语言模型（LLMs）已在各类信息处理任务中展现出卓越能力，这些任务涵盖数据提取、文献综述、内容生成、预测建模、决策制定以及系统控制等多个领域。此外，作为下一代语言模型的视觉大模型（VLMs）和多模态大语言模型（MLLMs），也被称为XLM，能够凭借语言理解能力融合多种数据模态，从而推动自动驾驶系统（ADS）等各类信息化系统的发展。实际上，通过将语言交互与全景图像、激光雷达或雷达数据等多模态传感输入相结合，系统可以做出精准的驾驶决策。在此背景下，本综述论文全面梳理了XLM在实现自动驾驶方面的潜力。具体而言，我们回顾了ADS与XLM领域的相关文献，涉及二者的架构、工具与框架；随后详细阐述了将XLM部署于自动驾驶解决方案的现有方法；最后，我们指出了XLM在ADS中部署面临的相关挑战，并指明了旨在推动XLM应用于未来ADS框架的未来研究方向。
英文摘要：
Large Language Models (LLMs) have showcased remarkable proficiency in various information-processing tasks. These tasks span from extracting data and summarizing literature to generating content, predictive modeling, decision-making, and system controls. Moreover, Vision Large Models (VLMs) and Multimodal LLMs (MLLMs), which represent the next generation of language models, a.k.a., XLMs, can combine and integrate many data modalities with the strength of language understanding, thus advancing several information-based systems, such as Autonomous Driving Systems (ADS). Indeed, by combining language communication with multimodal sensory inputs, e.g., panoramic images and LiDAR or radar data, accurate driving actions can be taken. In this context, we provide in this survey paper a comprehensive overview of the potential of XLMs towards achieving autonomous driving. Specifically, we review the relevant literature on ADS and XLMs, including their architectures, tools, and frameworks. Then, we detail the proposed approaches to deploy XLMs for autonomous driving solutions. Finally, we provide the related challenges to XLM deployment for ADS and point to future research directions aiming to enable XLM adoption in future ADS frameworks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
