Generative AI for Autonomous Driving: Frontiers and Opportunities

2025-05-13

AI Summary

This paper systematically investigates the key pathways and core challenges in leveraging generative AI (GenAI) for L5-level fully autonomous driving. Addressing critical bottlenecks—including poor generalization to long-tail scenarios, weak safety verifiability, and difficulty in cross-modal coordination—the authors propose the first comprehensive mapping framework linking GenAI capabilities with the full autonomous driving stack: multimodal perception generation, large language model (LLM)-driven decision-making, synthetic data construction, occupancy prediction, end-to-end policy learning, and digital twin simulation. A three-dimensional research roadmap is introduced, jointly optimizing technical robustness, safety verifiability, and socio-technical impact. The work establishes GenAI4AD, an open-source literature repository, and delivers a unified technology map spanning theory, algorithms, systems, and governance—providing both a methodological foundation and authoritative benchmark for reliable L5 autonomy.

Technology Category

• Natural Language Processing: Generation
• Machine Learning: Large Multimodal Models (LMMs)
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Agentic search
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

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.

Problem

• Exploring GenAI's role in achieving Level 5 autonomous driving
• Analyzing generative models for synthetic data and decision-making in AD
• Addressing challenges like safety, ethics, and scalability in GenAI for AD

Innovation

• Uses generative models like VAEs, GANs, Diffusion Models, LLMs
• Applies GenAI in synthetic data and digital twins
• Addresses safety, ethics, and regulatory compliance challenges