Generative AI for Autonomous Driving: Frontiers and Opportunities
cs.CV, cs.AI, cs.RO
Submitted: 2025-05-13
Updated: 2026-10-06
Comments: Accepted to ACM Computer Survey
Code: https://github.com/taco-group/GenAI4AD
License: http://creativecommons.org/licenses/by/4.0/
The gist: Generative Artificial Intelligence (GenAI) constitutes a transformative technological wave that reconfigures industries through its unparalleled capabilities for content creation, reasoning,
Terminology
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.
Sources
- nuScenes: A multimodal dataset for autonomous driving
- Specification and Validation of Autonomous Driving Systems: A Multilevel Semantic Framework
- Automatic Generation of Scenarios for System-level Simulation-based Verification of Autonomous Driving Systems
- Moving Forward: A Review of Autonomous Driving Software and Hardware Systems
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
- EMMA: End-to-End Multimodal Model for Autonomous Driving
- YouTube-8M: A Large-Scale Video Classification Benchmark
- Mcity Data Collection for Automated Vehicles Study
- A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT
- LLaMA: Open and Efficient Foundation Language Models
- Code Llama: Open Foundation Models for Code
- Scenario Dreamer: Vectorized Latent Diffusion for Generating Driving Simulation Environments
- DriveVLM: The Convergence of Autonomous Driving and Large Vision-Language Models
- A Survey of World Models for Autonomous Driving
- Exploring the Interplay Between Video Generation and World Models in Autonomous Driving: A Survey
- From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge
- Generative AI in Transportation Planning: A Survey
- Vision-Language-Action (VLA) Models: Concepts, Progress, Applications and Challenges
- A Survey for Foundation Models in Autonomous Driving
- LLM4Drive: A Survey of Large Language Models for Autonomous Driving
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