The advent of foundation models has revolutionized the fields of natural language processing and computer vision, paving the way for their application in autonomous driving (AD). This survey presents a comprehensive review of more than 40 research papers, demonstrating the role of foundation models in enhancing AD. Large language models contribute to planning and simulation in AD, particularly through their proficiency in reasoning, code generation and translation. In parallel, vision foundation models are increasingly adapted for critical tasks such as 3D object detection and tracking, as well as creating realistic driving scenarios for simulation and testing. Multi-modal foundation models, integrating diverse inputs, exhibit exceptional visual understanding and spatial reasoning, crucial for end-to-end AD. This survey not only provides a structured taxonomy, categorizing foundation models based on their modalities and functionalities within the AD domain but also delves into the methods employed in current research. It identifies the gaps between existing foundation models and cutting-edge AD approaches, thereby charting future research directions and proposing a roadmap for bridging these gaps.

翻译：基础模型的出现彻底革新了自然语言处理和计算机视觉领域，为其在自动驾驶中的广泛应用铺平了道路。本综述全面回顾了超过40篇研究论文，阐述了基础模型在增强自动驾驶能力方面的作用。大型语言模型通过其在推理、代码生成和翻译方面的卓越能力，为自动驾驶的规划与模拟环节做出贡献。与此同时，视觉基础模型越来越多地被应用于三维物体检测与跟踪，以及创建逼真驾驶场景用于模拟和测试等关键任务。多模态基础模型整合多种输入，展现出卓越的视觉理解与空间推理能力，这对于端到端自动驾驶至关重要。本综述不仅提供了一种结构化的分类体系，基于基础模型在自动驾驶领域的模态与功能对其进行归类，还深入探讨了当前研究中采用的方法。它识别了现有基础模型与前沿自动驾驶方法之间的差距，从而指明了未来的研究方向，并提出了一份弥合这些差距的路线图。