Foundation Models for Autonomous Driving System: An Initial Roadmap

• URL: http://arxiv.org/abs/2504.00911v1
• Date: Tue, 01 Apr 2025 15:45:31 GMT
• Title: Foundation Models for Autonomous Driving System: An Initial Roadmap
• Authors: Xiongfei Wu, Mingfei Cheng, Qiang Hu, Jianlang Chen, Yuheng Huang, Manabu Okada, Michio Hayashi, Tomoyuki Tsuchiya, Xiaofei Xie, Lei Ma,
• Abstract summary: Recent advancements in Foundation Models (FMs) have significantly enhanced Autonomous Driving Systems (ADSs)<n>ADSs are highly complex cyber-physical systems that demand rigorous software engineering practices to ensure reliability and safety.<n>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.
• Score: 17.198146951189635
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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