Saved in:
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.08854 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910941957849088 |
|---|---|
| author | Wang, Yuping Xing, Shuo Can, Cui Li, Renjie Hua, Hongyuan Tian, Kexin Mo, Zhaobin Gao, Xiangbo Wu, Keshu Zhou, Sulong You, Hengxu Peng, Juntong Zhang, Junge Wang, Zehao Song, Rui Yan, Mingxuan Zimmer, Walter Zhou, Xingcheng Li, Peiran Lu, Zhaohan Chen, Chia-Ju Huang, Yue Rossi, Ryan A. Sun, Lichao Yu, Hongkai Fan, Zhiwen Yang, Frank Hao Kang, Yuhao Greer, Ross Liu, Chenxi Lee, Eun Hak Di, Xuan Ye, Xinyue Ren, Liu Knoll, Alois Li, Xiaopeng Ji, Shuiwang Tomizuka, Masayoshi Pavone, Marco Yang, Tianbao Du, Jing Yang, Ming-Hsuan Wei, Hua Wang, Ziran Zhou, Yang Li, Jiachen Tu, Zhengzhong |
| author_facet | Wang, Yuping Xing, Shuo Can, Cui Li, Renjie Hua, Hongyuan Tian, Kexin Mo, Zhaobin Gao, Xiangbo Wu, Keshu Zhou, Sulong You, Hengxu Peng, Juntong Zhang, Junge Wang, Zehao Song, Rui Yan, Mingxuan Zimmer, Walter Zhou, Xingcheng Li, Peiran Lu, Zhaohan Chen, Chia-Ju Huang, Yue Rossi, Ryan A. Sun, Lichao Yu, Hongkai Fan, Zhiwen Yang, Frank Hao Kang, Yuhao Greer, Ross Liu, Chenxi Lee, Eun Hak Di, Xuan Ye, Xinyue Ren, Liu Knoll, Alois Li, Xiaopeng Ji, Shuiwang Tomizuka, Masayoshi Pavone, Marco Yang, Tianbao Du, Jing Yang, Ming-Hsuan Wei, Hua Wang, Ziran Zhou, Yang Li, Jiachen Tu, Zhengzhong |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_08854 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Generative AI for Autonomous Driving: Frontiers and Opportunities Wang, Yuping Xing, Shuo Can, Cui Li, Renjie Hua, Hongyuan Tian, Kexin Mo, Zhaobin Gao, Xiangbo Wu, Keshu Zhou, Sulong You, Hengxu Peng, Juntong Zhang, Junge Wang, Zehao Song, Rui Yan, Mingxuan Zimmer, Walter Zhou, Xingcheng Li, Peiran Lu, Zhaohan Chen, Chia-Ju Huang, Yue Rossi, Ryan A. Sun, Lichao Yu, Hongkai Fan, Zhiwen Yang, Frank Hao Kang, Yuhao Greer, Ross Liu, Chenxi Lee, Eun Hak Di, Xuan Ye, Xinyue Ren, Liu Knoll, Alois Li, Xiaopeng Ji, Shuiwang Tomizuka, Masayoshi Pavone, Marco Yang, Tianbao Du, Jing Yang, Ming-Hsuan Wei, Hua Wang, Ziran Zhou, Yang Li, Jiachen Tu, Zhengzhong Computer Vision and Pattern Recognition Artificial Intelligence Robotics 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. |
| title | Generative AI for Autonomous Driving: Frontiers and Opportunities |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Robotics |
| url | https://arxiv.org/abs/2505.08854 |