Recent advancements in Foundation Models (FMs), such as Large Language Models (LLMs), have significantly enhanced Autonomous Driving Systems (ADSs) by improving perception, reasoning, and decision-making in dynamic and uncertain environments. However, ADSs are highly complex cyber-physical systems that demand rigorous software engineering practices to ensure reliability and safety. Integrating FMs into ADSs introduces new challenges in system design and evaluation, requiring a systematic review to establish a clear research roadmap. To unlock these challenges, we present a structured roadmap for integrating FMs into autonomous driving, covering three key aspects: the infrastructure of FMs, their application in autonomous driving systems, and their current applications in practice. For each aspect, we review the current research progress, identify existing challenges, and highlight research gaps that need to be addressed by the community.

翻译：近年来，基础模型（如大型语言模型）的进展显著提升了自动驾驶系统在动态不确定环境中的感知、推理与决策能力。然而，自动驾驶系统是高度复杂的信息物理系统，需要严格的软件工程实践以确保其可靠性与安全性。将基础模型集成至自动驾驶系统中，为系统设计与评估带来了新的挑战，亟需通过系统性梳理以建立明确的研究路线图。为应对这些挑战，本文提出了一个将基础模型集成到自动驾驶领域的结构化路线图，涵盖三个关键方面：基础模型的基础设施、其在自动驾驶系统中的应用以及当前的实际应用案例。针对每个方面，我们回顾了现有研究进展，识别了当前面临的挑战，并指出了需要学界共同关注的研究空白。