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Generative AI for Autonomous Driving: Frontiers and Opportunities

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arxiv 2505.08854 v1 pith:3PEFELRI submitted 2025-05-13 cs.CV cs.AIcs.RO

Yuping Wang , Shuo Xing , Cui Can , Renjie Li , Hongyuan Hua , Kexin Tian , Zhaobin Mo , Xiangbo Gao
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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
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classification cs.CVcs.AIcs.RO
keywords autonomousdrivinggenerativeacrossapplicationscomprehensivegenaimodels
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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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Cited by 9 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. WorldLens: Full-Spectrum Evaluations of Driving World Models in Real World

    cs.CV 2025-12 conditional novelty 6.0 of 10

    A five-aspect, 24-metric benchmark, a 26K human-annotated dataset, and an AI evaluator show that today's driving world models cannot simultaneously look real, respect geometry, and behave safely.

  2. A New Perspective On AI Safety Through Control Theory Methodologies

    cs.AI 2025-06 conditional novelty 6.0 of 10

    This paper outlines a new conceptual paradigm, data control, which transfers control-theoretic system analysis and properties to AI systems to support generic AI safety assurance.

  3. AirV2X: Unified Air-Ground Vehicle-to-Everything Collaboration

    cs.CV 2025-06 conditional novelty 6.0 of 10

    AirV2X-Perception is a 6.73-hour simulated dataset and benchmark for collaborative perception with up to 5 vehicles, 5 roadside units, and 5 drones.

  4. Generative AI for Testing of Autonomous Driving Systems: A Survey

    cs.SE 2025-08 conditional novelty 5.0 of 10

    A systematic survey that organizes 91 studies of generative AI for autonomous driving testing into six scenario-based tasks and catalogs 27 limitations.

  5. Vision-Language Assistant for Emotional Reactions to Risky Driving

    cs.CV 2026-07 reject novelty 4.0 of 10

    KYA pipes YOLOv8-detected cut-in risks into persona-prompted LLMs to generate emotional spoken reactions; in a 108-person study users preferred humorous/analytical styles and ChatGPT-4o won the most votes, though the ...

  6. Edge-Based Multimodal Sensor Data Fusion with Vision Language Models (VLMs) for Real-time Autonomous Vehicle Accident Avoidance

    cs.AI 2025-08 reject novelty 4.0 of 10

    A lightweight vision-language model on an edge device fuses roadside hazard alerts with onboard camera views to adjust trajectories, and the authors report a 77% simulated collision reduction over a vision-only baseline.

  7. Demystifying the Visual Quality Paradox in Multimodal Large Language Models

    cs.CV 2025-06 reject novelty 4.0 of 10

    Multimodal LLM accuracy can improve on visually degraded images, and a lightweight test-time tuning module that modulates input quality yields small accuracy gains on some benchmarks.

  8. Research Challenges and Progress in the End-to-End V2X Cooperative Autonomous Driving Competition

    cs.RO 2025-07 conditional novelty 3.0 of 10

    This paper summarizes the CVPR 2025 V2X cooperative driving challenge, its winning solutions, and the open research problems it reveals.

  9. Automated Vehicles Should be Connected with Natural Language

    cs.MA 2025-06 conditional novelty 3.0 of 10

    A vision paper recommending natural language as the universal communication medium for connected and automated vehicles.

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