Generative AI for Autonomous Driving: Frontiers and Opportunities
Yuping Wang, Shuo Xing, Can Cui, Renjie Li, Hong Hua, Kexin Tian, Zhaobin Mo, Xiangbo Gao 等 12 位
University of Michigan Mitchell Institute Nomor Research (Germany) Texas A&M University
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摘要与影响
Generative Artificial Intelligence (GenAI) constitutes a transformative technological wave that reconfigures industries through its unparalleled capabilities for content creation, reasoning, planning, and multimodal understanding. This revolutionary force offers the most promising path yet toward solving one of engineering’s grandest challenges: achieving reliable, fully autonomous driving, particularly the pursuit of Level 5 autonomy. This survey delivers a comprehensive and critical synthesis of the emerging role of GenAI across the autonomous driving stack. We delve into the frontier applications of GenAI in image, LiDAR, trajectory, occupancy, and video generation, as well as LLM-guided reasoning and decision-making. We categorize practical applications, such as end-to-end driving strategies and closed-loop simulations. We identify key obstacles and possibilities such as comprehensive generalization across rare cases, evaluation, safety, and onboard deployment. By unifying these threads, the survey provides a forward-looking reference for researchers, engineers, and policymakers navigating the convergence of generative AI and advanced autonomous mobility. An actively maintained repository of cited works is available at https://github.com/taco-group/GenAI4AD.
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工程Transportation and Mobility Innovations
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