# Generative AI for Testing of Autonomous Driving Systems: A Survey

arXiv: https://arxiv.org/abs/2508.19882 · HTML: https://mlsanity.com/paper/2508.19882/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 31 references that are in the map.
- First posted on arXiv 27 Aug 2025.

## Builds on (most cited first, 17 of 17)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Variational Inference: A Review for Statisticians](https://mlsanity.com/paper/1601.00670/) (2016)
- [Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models](https://mlsanity.com/paper/2304.01852/) (2023)
- [GitHub Copilot AI pair programmer: Asset or Liability?](https://mlsanity.com/paper/2206.15331/) (2022)
- [Bias Mitigation for Machine Learning Classifiers: A Comprehensive Survey](https://mlsanity.com/paper/2207.07068/) (2022)
- [Generative AI](https://mlsanity.com/paper/2309.07930/) (2023)
- [A Survey on Safety-Critical Driving Scenario Generation -- A Methodological Perspective](https://mlsanity.com/paper/2202.02215/) (2022)
- [Fairness Testing: A Comprehensive Survey and Analysis of Trends](https://mlsanity.com/paper/2207.10223/) (2022)
- [Mind the Gap! A Study on the Transferability of Virtual vs Physical-world Testing of Autonomous Driving Systems](https://mlsanity.com/paper/2112.11255/) (2021)
- [A Survey on Automated Driving System Testing: Landscapes and Trends](https://mlsanity.com/paper/2206.05961/) (2022)
- [Advancements in Generative AI: A Comprehensive Review of GANs, GPT, Autoencoders, Diffusion Model, and Transformers](https://mlsanity.com/paper/2311.10242/) (2023)
- [Exploring the Roles of Large Language Models in Reshaping Transportation Systems: A Survey, Framework, and Roadmap](https://mlsanity.com/paper/2503.21411/) (2025)
- [Testing of Autonomous Driving Systems: Where Are We and Where Should We Go?](https://mlsanity.com/paper/2106.12233/) (2021)
- [Successful Combination of Database Search and Snowballing for Identification of Primary Studies in Systematic Literature Studies](https://mlsanity.com/paper/2307.02612/) (2023)
- [A Deep Learning Framework for Generation and Analysis of Driving Scenario Trajectories](https://mlsanity.com/paper/2007.14524/) (2020)
- [Industry Practices for Challenging Autonomous Driving Systems with Critical Scenarios](https://mlsanity.com/paper/2305.14146/) (2023)
- [Lessons Learned from Accident of Autonomous Vehicle Testing: An Edge Learning-aided Offloading Framework](https://mlsanity.com/paper/2006.15382/) (2020)

## Built on by (most cited first, 3 of 3)

- [PlannerForge: LLM Agents for Scenario-Based Testing of Motion Planners in Autonomous Driving](https://mlsanity.com/paper/2609.08965/) (2026)
- [Multi-Large Language Model Orchestrated Severity Assessment of Clinical Records (MOSAIC)](https://mlsanity.com/paper/2607.05032/) (2026)
- [WM-Cov: Test Adequacy for Interactive World-Model-Style Autonomous Driving Simulation](https://mlsanity.com/paper/2608.00298/) (2026)

## Abstract (arXiv)

Autonomous driving systems (ADS) have been an active area of research, with the potential to deliver significant benefits to society. However, before large-scale deployment on public roads, extensive testing is necessary to validate their functionality and safety under diverse driving conditions. Therefore, different testing approaches are required, and achieving effective and efficient testing of ADS remains an open challenge. Recently, generative AI has emerged as a powerful tool across many domains, and it is increasingly being applied to ADS testing due to its ability to interpret context, reason about complex tasks, and generate diverse outputs. To gain a deeper understanding of its role in ADS testing, we systematically analyzed 91 relevant studies and synthesized their findings into six major application categories, primarily centered on scenario-based testing of ADS. We also reviewed their effectiveness and compiled a wide range of datasets, simulators, ADS, metrics, and benchmarks used for evaluation, while identifying 27 limitations. This survey provides an overview and practical insights into the use of generative AI for testing ADS, highlights existing challenges, and outlines directions for future research in this rapidly evolving field.