# Real-World Perturbation Testing of Autonomous Driving Systems

arXiv: https://arxiv.org/abs/2607.04953 · HTML: https://mlsanity.com/paper/2607.04953/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Adversarial scenarios and safety testing, one of 9 directions in End-to-end autonomous driving.
- Builds on 33 of its 74 references that are in the map.
- First posted on arXiv 6 Jul 2026.

## Placed in

- End-to-end autonomous driving / [Adversarial scenarios and safety testing](https://mlsanity.com/driving/adversarial-scenarios-and-safety-testing/)

## Builds on (most cited first, 30 of 33)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://mlsanity.com/paper/1903.12261/) (2019)
- [RandAugment: Practical automated data augmentation with a reduced search space](https://mlsanity.com/paper/1909.13719/) (2019)
- [End to End Learning for Self-Driving Cars](https://mlsanity.com/paper/1604.07316/) (2016)
- [AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty](https://mlsanity.com/paper/1912.02781/) (2019)
- [A Survey of Deep Learning Techniques for Autonomous Driving](https://mlsanity.com/paper/1910.07738/) (2019)
- [A Survey of Autonomous Driving: Common Practices and Emerging Technologies](https://mlsanity.com/paper/1906.05113/) (2019)
- [Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming](https://mlsanity.com/paper/1907.07484/) (2019)
- [DeepXplore: Automated Whitebox Testing of Deep Learning Systems](https://mlsanity.com/paper/1705.06640/) (2017)
- [DeepTest: Automated Testing of Deep-Neural-Network-driven Autonomous Cars](https://mlsanity.com/paper/1708.08559/) (2017)
- [Generalisation in humans and deep neural networks](https://mlsanity.com/paper/1808.08750/) (2018)
- [MNIST-C: A Robustness Benchmark for Computer Vision](https://mlsanity.com/paper/1906.02337/) (2019)
- [Understanding How Image Quality Affects Deep Neural Networks](https://mlsanity.com/paper/1604.04004/) (2016)
- [Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Driving](https://mlsanity.com/paper/2303.11040/) (2023)
- [Taxonomy of Real Faults in Deep Learning Systems](https://mlsanity.com/paper/1910.11015/) (2019)
- [DeepBillboard: Systematic Physical-World Testing of Autonomous Driving Systems](https://mlsanity.com/paper/1812.10812/) (2018)
- [A Survey on Automated Driving System Testing: Landscapes and Trends](https://mlsanity.com/paper/2206.05961/) (2022)
- [Comparing Offline and Online Testing of Deep Neural Networks: An Autonomous Car Case Study](https://mlsanity.com/paper/1912.00805/) (2019)
- [Can Offline Testing of Deep Neural Networks Replace Their Online Testing?](https://mlsanity.com/paper/2101.11118/) (2021)
- [Assessing Quality Metrics for Neural Reality Gap Input Mitigation in Autonomous Driving Testing](https://mlsanity.com/paper/2404.18577/) (2024)
- [Identifying and Explaining Safety-critical Scenarios for Autonomous Vehicles via Key Features](https://mlsanity.com/paper/2212.07566/) (2022)
- [Achieving Generalizable Robustness of Deep Neural Networks by Stability Training](https://mlsanity.com/paper/1906.00735/) (2019)
- [Metamorphic Testing for Object Detection Systems](https://mlsanity.com/paper/1912.12162/) (2019)
- [EpiTESTER: Testing Autonomous Vehicles with Epigenetic Algorithm and Attention Mechanism](https://mlsanity.com/paper/2312.00207/) (2023)
- [Towards Reliable AI: Adequacy Metrics for Ensuring the Quality of System-level Testing of Autonomous Vehicles](https://mlsanity.com/paper/2311.08049/) (2023)
- [Bridging the Gap between Open Source Software and Vehicle Hardware for Autonomous Driving](https://mlsanity.com/paper/1905.02980/) (2019)
- [Panoptic Perception for Autonomous Driving: A Survey](https://mlsanity.com/paper/2408.15388/) (2024)
- [Benchmarking Image Perturbations for Testing Automated Driving Assistance Systems](https://mlsanity.com/paper/2501.12269/) (2025)
- [On the Need for a Statistical Foundation in Scenario-Based Testing of Autonomous Vehicles](https://mlsanity.com/paper/2505.02274/) (2025)
- [Learning When to Use Adaptive Adversarial Image Perturbations against Autonomous Vehicles](https://mlsanity.com/paper/2212.13667/) (2022)

## Abstract (arXiv)

Autonomous Driving Systems (ADS) must operate reliably under diverse conditions, yet representative data for rare or adverse scenarios is difficult to obtain. Perturbation-based testing is widely used to assess robustness, but most studies focus on offline datasets or simulation, leaving open questions about how such results translate to real-world driving. We present a large-scale study of 72 camera and LiDAR perturbations, evaluated across three testing modalities: offline model-level analysis, hardware-in-the-loop execution, and closed-loop system-level testing on a full-scale autonomous vehicle. The study covers both an end-to-end vision-based driving model and a modular LiDAR-based perception and planning stack. Our results reveal a clear gap between testing levels. For camera-based systems, perturbations with limited offline impact can still induce unstable control and failures in real-world driving. For LiDAR-based systems, degradation is more consistent at the perception level but weakly predictive of system-level failures. Across both modalities, model-level metrics alone are insufficient to identify the most harmful perturbations. We further show that real-time feasibility is a key constraint in real-world testing, and that robustness observations obtained from recorded data do not consistently transfer to closed-loop behavior on a physical vehicle, highlighting the importance of complementary real-world, system-level evaluation.