# Adversarial scenarios and safety testing

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

## Key facts

- Adversarial scenarios and safety testing: 141 papers on arXiv since 2021 (data through 14 Oct 2026).
- 24 of its 141 mapped papers (17%) cite “Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic Prior” (2021, arXiv 2112.05077).
- 17 of its 141 mapped papers (12%) cite “CAT: Closed-loop Adversarial Training for Safe End-to-End Driving” (2023, arXiv 2310.12432).
- 4 new papers 17 Sep–14 Oct.

## Most cited here

- [Generating Useful Accident-Prone Driving Scenarios via a Learned Traffic Prior](https://mlsanity.com/paper/2112.05077/) (2021), cited by 24 papers here
- [CAT: Closed-loop Adversarial Training for Safe End-to-End Driving](https://mlsanity.com/paper/2310.12432/) (2023), cited by 17 papers here
- [SafeBench: A Benchmarking Platform for Safety Evaluation of Autonomous Vehicles](https://mlsanity.com/paper/2206.09682/) (2022), cited by 10 papers here
- [Corner Case Generation and Analysis for Safety Assessment of Autonomous Vehicles](https://mlsanity.com/paper/2102.03483/) (2021), cited by 8 papers here
- [Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attacks](https://mlsanity.com/paper/2106.09249/) (2021), cited by 8 papers here
- [Adversarial Driving: Attacking End-to-End Autonomous Driving](https://mlsanity.com/paper/2103.09151/) (2021), cited by 6 papers here
- [Targeted Attack on Deep RL-based Autonomous Driving with Learned Visual Patterns](https://mlsanity.com/paper/2109.07723/) (2021), cited by 6 papers here
- [Attack End-to-End Autonomous Driving through Module-Wise Noise](https://mlsanity.com/paper/2409.07706/) (2024), cited by 6 papers here
- [Visual Adversarial Attack on Vision-Language Models for Autonomous Driving](https://mlsanity.com/paper/2411.18275/) (2024), cited by 6 papers here
- [(Re)2H2O: Autonomous Driving Scenario Generation via Reversely Regularized Hybrid Offline-and-Online Reinforcement Learning](https://mlsanity.com/paper/2302.13726/) (2023), cited by 5 papers here

## New (17 Sep–14 Oct)

- [Human Behavior-Informed Crash Scenario Generation with Real-World Crash Priors for Autonomous Vehicle Safety Evaluation](https://mlsanity.com/paper/2610.04366/) (2026-10-03)
- [TrafficSignBench: Rule-Centric Closed-Loop Evaluation of Traffic-Sign Compliance in Autonomous Driving](https://mlsanity.com/paper/2609.38463/) (2026-09-29)
- [ExceptionDrive: A Planning-Oriented Counterfactual Corner-Case Benchmark for Autonomous Driving](https://mlsanity.com/paper/2609.37871/) (2026-09-29)
- [NavSafe-∞: Benchmarking Closed-Loop Driving Safety in Photorealistic Environments](https://mlsanity.com/paper/2609.26618/) (2026-09-22)