Generative AI for Autonomous Driving: Frontiers and Opportunities

Yuping Wang; Shuo Xing; Cui Can; Renjie Li; Hongyuan Hua; Kexin Tian; Zhaobin Mo; Xiangbo Gao; Keshu Wu; Sulong Zhou; Hengxu You; Juntong Peng; Junge Zhang; Zehao Wang; Rui Song; Mingxuan Yan; Walter Zimmer; Xingcheng Zhou; Peiran Li; Zhaohan Lu; Chia-Ju Chen; Yue Huang; Ryan A. Rossi; Lichao Sun; Hongkai Yu; Zhiwen Fan; Frank Hao Yang; Yuhao Kang; Ross Greer; Chenxi Liu; Eun Hak Lee; Xuan Di; Xinyue Ye; Liu Ren; Alois Knoll; Xiaopeng Li; Shuiwang Ji; Masayoshi Tomizuka; Marco Pavone; Tianbao Yang; Jing Du; Ming-Hsuan Yang; Hua Wei; Ziran Wang; Yang Zhou; Jiachen Li; Zhengzhong Tu

arXiv · 2505.08854

Generative AI for Autonomous Driving: Frontiers and Opportunities

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Abstract

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 begin by distilling the principles and trade-offs of modern generative modeling, encompassing VAEs, GANs, Diffusion Models, and Large Language Models (LLMs). We then map their frontier applications in image, LiDAR, trajectory, occupancy, video generation as well as LLM-guided reasoning and decision making. We categorize practical applications, such as synthetic data workflows, end-to-end driving strategies, high-fidelity digital twin systems, smart transportation networks, and cross-domain transfer to embodied AI. We identify key obstacles and possibilities such as comprehensive generalization across rare cases, evaluation and safety checks, budget-limited implementation, regulatory compliance, ethical concerns, and environmental effects, while proposing research plans across theoretical assurances, trust metrics, transport integration, and socio-technical influence. 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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Yuping Wang, Shuo Xing, Cui Can, Renjie Li, Hongyuan Hua, Kexin Tian, Zhaobin Mo, Xiangbo Gao, Keshu Wu, Sulong Zhou, Hengxu You, Juntong Peng, Junge Zhang, Zehao Wang, Rui Song, Mingxuan Yan, Walter Zimmer, Xingcheng Zhou, Peiran Li, Zhaohan Lu, Chia-Ju Chen, Yue Huang, Ryan A. Rossi, Lichao Sun, Hongkai Yu, Zhiwen Fan, Frank Hao Yang, Yuhao Kang, Ross Greer, Chenxi Liu, Eun Hak Lee, Xuan Di, Xinyue Ye, Liu Ren, Alois Knoll, Xiaopeng Li, Shuiwang Ji, Masayoshi Tomizuka, Marco Pavone, Tianbao Yang, Jing Du, Ming-Hsuan Yang, Hua Wei, Ziran Wang, Yang Zhou, Jiachen Li, Zhengzhong Tu. 2025-05-13. Generative AI for Autonomous Driving: Frontiers and Opportunities. https://arxiv.org/abs/2505.08854

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