# A Survey on Automated Driving System Testing: Landscapes and Trends

arXiv: https://arxiv.org/abs/2206.05961 · HTML: https://mlsanity.com/paper/2206.05961/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jun 2022.

## Built on by (most cited first, 30 of 36)

- [Efficient Domain Augmentation for Autonomous Driving Testing Using Diffusion Models](https://mlsanity.com/paper/2409.13661/) (2024)
- [Two is Better Than One: Digital Siblings to Improve Autonomous Driving Testing](https://mlsanity.com/paper/2305.08060/) (2023)
- [Domain Knowledge Distillation from Large Language Model: An Empirical Study in the Autonomous Driving Domain](https://mlsanity.com/paper/2307.11769/) (2023)
- [A Scenario-Based Functional Testing Approach to Improving DNN Performance](https://mlsanity.com/paper/2307.07083/) (2023)
- [Boundary State Generation for Testing and Improvement of Autonomous Driving Systems](https://mlsanity.com/paper/2307.10590/) (2023)
- [Bridging the Gap between Real-world and Synthetic Images for Testing Autonomous Driving Systems](https://mlsanity.com/paper/2408.13950/) (2024)
- [Benchmarking Generative AI Models for Deep Learning Test Input Generation](https://mlsanity.com/paper/2412.17652/) (2024)
- [Benchmarking Image Perturbations for Testing Automated Driving Assistance Systems](https://mlsanity.com/paper/2501.12269/) (2025)
- [Generative AI for Testing of Autonomous Driving Systems: A Survey](https://mlsanity.com/paper/2508.19882/) (2025)
- [Reinforcement Learning for Online Testing of Autonomous Driving Systems: a Replication and Extension Study](https://mlsanity.com/paper/2403.13729/) (2024)
- [Generative transformations and patterns in LLM-native approaches for software verification and falsification](https://mlsanity.com/paper/2404.09384/) (2024)
- [Dance of the ADS: Orchestrating Failures through Historically-Informed Scenario Fuzzing](https://mlsanity.com/paper/2407.04359/) (2024)
- [LitSim: A Conflict-aware Policy for Long-term Interactive Traffic Simulation](https://mlsanity.com/paper/2403.04299/) (2024)
- [Preparing for Black Swans: The Antifragility Imperative for Machine Learning](https://mlsanity.com/paper/2405.11397/) (2024)
- [Generating Critical Scenarios for Testing Automated Driving Systems](https://mlsanity.com/paper/2412.02574/) (2024)
- [Evaluating Scenario-based Decision-making for Interactive Autonomous Driving Using Rational Criteria: A Survey](https://mlsanity.com/paper/2501.01886/) (2025)
- [Simulator Ensembles for Trustworthy Autonomous Driving Systems Testing](https://mlsanity.com/paper/2503.08936/) (2025)
- [ME3-BEV: Mamba-Enhanced Deep Reinforcement Learning for End-to-End Autonomous Driving with BEV-Perception](https://mlsanity.com/paper/2508.06074/) (2025)
- [A Multi-Modality Evaluation of the Reality Gap in Autonomous Driving Systems](https://mlsanity.com/paper/2509.22379/) (2025)
- [Advancing Autonomous Driving System Testing: Demands, Challenges, and Future Directions](https://mlsanity.com/paper/2512.11887/) (2025)
- [Quantifying Fidelity: A Decisive Feature Approach to Comparing Synthetic and Real Imagery](https://mlsanity.com/paper/2512.16468/) (2025)
- [DMAVA: Distributed Multi-Autonomous Vehicle Architecture Using Autoware](https://mlsanity.com/paper/2601.16336/) (2026)
- [Checklist to Define the Identification of TP, FP, and FN Object Detections in Automated Driving](https://mlsanity.com/paper/2308.07106/) (2023)
- [MultiDrive: A Co-Simulation Framework Bridging 2D and 3D Driving Simulation for AV Software Validation](https://mlsanity.com/paper/2505.13959/) (2025)
- [Search-Based Software Engineering and AI Foundation Models: Current Landscape and Future Roadmap](https://mlsanity.com/paper/2505.19625/) (2025)
- [TigAug: Data Augmentation for Testing Traffic Light Detection in Autonomous Driving Systems](https://mlsanity.com/paper/2507.05932/) (2025)
- [Improving AEBS Validation Through Objective Intervention Classification Leveraging the Prediction Divergence Principle](https://mlsanity.com/paper/2507.07872/) (2025)
- [Validating Generalist Robots with Situation Calculus and STL Falsification](https://mlsanity.com/paper/2601.03038/) (2026)
- [ROMAN: Reward-Orchestrated Multi-Head Attention Network for Autonomous Driving System Testing](https://mlsanity.com/paper/2602.05629/) (2026)
- [Declarative Scenario-based Testing with RoadLogic](https://mlsanity.com/paper/2603.09455/) (2026)

## Abstract (arXiv)

Automated Driving Systems (ADS) have made great achievements in recent years thanks to the efforts from both academia and industry. A typical ADS is composed of multiple modules, including sensing, perception, planning, and control, which brings together the latest advances in different domains. Despite these achievements, safety assurance of ADS is of great significance, since unsafe behavior of ADS can bring catastrophic consequences. Testing has been recognized as an important system validation approach that aims to expose unsafe system behavior; however, in the context of ADS, it is extremely challenging to devise effective testing techniques, due to the high complexity and multidisciplinarity of the systems. There has been great much literature that focuses on the testing of ADS, and a number of surveys have also emerged to summarize the technical advances. Most of the surveys focus on the system-level testing performed within software simulators, and they thereby ignore the distinct features of different modules. In this paper, we provide a comprehensive survey on the existing ADS testing literature, which takes into account both module-level and system-level testing. Specifically, we make the following contributions: (1) we survey the module-level testing techniques for ADS and highlight the technical differences affected by the features of different modules; (2) we also survey the system-level testing techniques, with focuses on the empirical studies that summarize the issues occurring in system development or deployment, the problems due to the collaborations between different modules, and the gap between ADS testing in simulators and the real world; (3) we identify the challenges and opportunities in ADS testing, which pave the path to the future research in this field.