# Simulation-based Adversarial Test Generation for Autonomous Vehicles with Machine Learning Components

arXiv: https://arxiv.org/abs/1804.06760 · HTML: https://mlsanity.com/paper/1804.06760/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Apr 2018.

## Built on by (most cited first, 29 of 29)

- [How to Certify Machine Learning Based Safety-critical Systems? A Systematic Literature Review](https://mlsanity.com/paper/2107.12045/) (2021)
- [Few-Shot Adversarial Prompt Learning on Vision-Language Models](https://mlsanity.com/paper/2403.14774/) (2024)
- [CausalAdv: Adversarial Robustness through the Lens of Causality](https://mlsanity.com/paper/2106.06196/) (2021)
- [Can Offline Testing of Deep Neural Networks Replace Their Online Testing?](https://mlsanity.com/paper/2101.11118/) (2021)
- [SoK: On the Semantic AI Security in Autonomous Driving](https://mlsanity.com/paper/2203.05314/) (2022)
- [Towards Lightweight Black-Box Attacks against Deep Neural Networks](https://mlsanity.com/paper/2209.14826/) (2022)
- [Finding Critical Scenarios for Automated Driving Systems: A Systematic Literature Review](https://mlsanity.com/paper/2110.08664/) (2021)
- [ANTI-CARLA: An Adversarial Testing Framework for Autonomous Vehicles in CARLA](https://mlsanity.com/paper/2208.06309/) (2022)
- [Part-X: A Family of Stochastic Algorithms for Search-Based Test Generation with Probabilistic Guarantees](https://mlsanity.com/paper/2110.10729/) (2021)
- [Systematic Training and Testing for Machine Learning Using Combinatorial Interaction Testing](https://mlsanity.com/paper/2201.12428/) (2022)
- [EpiTESTER: Testing Autonomous Vehicles with Epigenetic Algorithm and Attention Mechanism](https://mlsanity.com/paper/2312.00207/) (2023)
- [Adaptive Failure Search Using Critical States from Domain Experts](https://mlsanity.com/paper/2304.00365/) (2023)
- [Test & Evaluation Best Practices for Machine Learning-Enabled Systems](https://mlsanity.com/paper/2310.06800/) (2023)
- [Querying Labelled Data with Scenario Programs for Sim-to-Real Validation](https://mlsanity.com/paper/2112.00206/) (2021)
- [A Neurosymbolic Approach to the Verification of Temporal Logic Properties of Learning enabled Control Systems](https://mlsanity.com/paper/2303.05394/) (2023)
- [ADV-0: Closed-Loop Min-Max Adversarial Training for Long-Tail Robustness in Autonomous Driving](https://mlsanity.com/paper/2603.15221/) (2026)
- [ICR-Drive: Instruction Counterfactual Robustness for End-to-End Language-Driven Autonomous Driving](https://mlsanity.com/paper/2604.05378/) (2026)
- [Provable Traffic Rule Compliance in Safe Reinforcement Learning on the Open Sea](https://mlsanity.com/paper/2402.08502/) (2024)
- [Reinforcement Learning for Online Testing of Autonomous Driving Systems: a Replication and Extension Study](https://mlsanity.com/paper/2403.13729/) (2024)
- [Rationale-aware Autonomous Driving Policy utilizing Safety Force Field implemented on CARLA Simulator](https://mlsanity.com/paper/2211.10237/) (2022)
- [Grammar-Constrained Refinement of Safety Operational Rules Using Language in the Loop: What Could Go Wrong](https://mlsanity.com/paper/2604.23523/) (2026)
- [Analysis of E-commerce Ranking Signals via Signal Temporal Logic](https://mlsanity.com/paper/2101.05415/) (2021)
- [Evaluating Automated Driving Planner Robustness against Adversarial Influence](https://mlsanity.com/paper/2205.14697/) (2022)
- [Metric Learning Improves the Ability of Combinatorial Coverage Metrics to Anticipate Classification Error](https://mlsanity.com/paper/2302.14616/) (2023)
- [Foundation Models for Rapid Autonomy Validation](https://mlsanity.com/paper/2411.03328/) (2024)
- [Context-aware, Ante-hoc Explanations of Driving Behaviour](https://mlsanity.com/paper/2511.14428/) (2025)
- [PPO guided Agentic Pipeline for Adaptive Prompt Selection and Test Case Generation](https://mlsanity.com/paper/2605.00942/) (2026)
- [FeedbackLLM: Metadata driven Multi-Agentic Language Agnostic Test Case Generator with Evolving prompt and Coverage Feedback](https://mlsanity.com/paper/2605.01264/) (2026)
- [DreamTest: World-Model Surrogates for Search-Based Testing of Deep Reinforcement Learning Agents](https://mlsanity.com/paper/2610.04494/) (2026)

## Abstract (arXiv)

Many organizations are developing autonomous driving systems, which are expected to be deployed at a large scale in the near future. Despite this, there is a lack of agreement on appropriate methods to test, debug, and certify the performance of these systems. One of the main challenges is that many autonomous driving systems have machine learning components, such as deep neural networks, for which formal properties are difficult to characterize. We present a testing framework that is compatible with test case generation and automatic falsification methods, which are used to evaluate cyber-physical systems. We demonstrate how the framework can be used to evaluate closed-loop properties of an autonomous driving system model that includes the ML components, all within a virtual environment. We demonstrate how to use test case generation methods, such as covering arrays, as well as requirement falsification methods to automatically identify problematic test scenarios. The resulting framework can be used to increase the reliability of autonomous driving systems.