# Adaptive Stress Testing for Autonomous Vehicles

arXiv: https://arxiv.org/abs/1902.01909 · HTML: https://mlsanity.com/paper/1902.01909/

## Key facts

- Cited by 71 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Feb 2019.

## Built on by (most cited first, 30 of 71)

- [Explainability of deep vision-based autonomous driving systems: Review and challenges](https://mlsanity.com/paper/2101.05307/) (2021)
- [Corner Case Generation and Analysis for Safety Assessment of Autonomous Vehicles](https://mlsanity.com/paper/2102.03483/) (2021)
- [Neural Network Guided Evolutionary Fuzzing for Finding Traffic Violations of Autonomous Vehicles](https://mlsanity.com/paper/2109.06126/) (2021)
- [Many-Objective Reinforcement Learning for Online Testing of DNN-Enabled Systems](https://mlsanity.com/paper/2210.15432/) (2022)
- [How Do We Fail? Stress Testing Perception in Autonomous Vehicles](https://mlsanity.com/paper/2203.14155/) (2022)
- [Rethinking Robustness Assessment: Adversarial Attacks on Learning-based Quadrupedal Locomotion Controllers](https://mlsanity.com/paper/2405.12424/) (2024)
- [Finding Critical Scenarios for Automated Driving Systems: A Systematic Literature Review](https://mlsanity.com/paper/2110.08664/) (2021)
- [MDPFuzz: Testing Models Solving Markov Decision Processes](https://mlsanity.com/paper/2112.02807/) (2021)
- [Learning to Drive via Asymmetric Self-Play](https://mlsanity.com/paper/2409.18218/) (2024)
- [Adversarial Deep Reinforcement Learning for Improving the Robustness of Multi-agent Autonomous Driving Policies](https://mlsanity.com/paper/2112.11937/) (2021)
- [SEAL: Towards Safe Autonomous Driving via Skill-Enabled Adversary Learning for Closed-Loop Scenario Generation](https://mlsanity.com/paper/2409.10320/) (2024)
- [Detecting Multi-Sensor Fusion Errors in Advanced Driver-Assistance Systems](https://mlsanity.com/paper/2109.06404/) (2021)
- [Finding Failures in High-Fidelity Simulation using Adaptive Stress Testing and the Backward Algorithm](https://mlsanity.com/paper/2107.12940/) (2021)
- [(Re)2H2O: Autonomous Driving Scenario Generation via Reversely Regularized Hybrid Offline-and-Online Reinforcement Learning](https://mlsanity.com/paper/2302.13726/) (2023)
- [Assisted Robust Reward Design](https://mlsanity.com/paper/2111.09884/) (2021)
- [Simulating the Unseen: Crash Prediction Must Learn from What Did Not Happen](https://mlsanity.com/paper/2505.21743/) (2025)
- [Building Safer Autonomous Agents by Leveraging Risky Driving Behavior Knowledge](https://mlsanity.com/paper/2103.10245/) (2021)
- [Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification Algorithms](https://mlsanity.com/paper/2210.16575/) (2022)
- [Adaptive Failure Search Using Critical States from Domain Experts](https://mlsanity.com/paper/2304.00365/) (2023)
- [Continual Driving Policy Optimization with Closed-Loop Individualized Curricula](https://mlsanity.com/paper/2309.14209/) (2023)
- [Safety-Critical Traffic Simulation with Adversarial Transfer of Driving Intentions](https://mlsanity.com/paper/2503.05180/) (2025)
- [ARC: Adversarially Robust Control Policies for Autonomous Vehicles](https://mlsanity.com/paper/2107.04487/) (2021)
- [Certifiable Deep Importance Sampling for Rare-Event Simulation of Black-Box Systems](https://mlsanity.com/paper/2111.02204/) (2021)
- [Querying Labelled Data with Scenario Programs for Sim-to-Real Validation](https://mlsanity.com/paper/2112.00206/) (2021)
- [Causal Scene BERT: Improving object detection by searching for challenging groups of data](https://mlsanity.com/paper/2202.03651/) (2022)
- [Model-based Validation as Probabilistic Inference](https://mlsanity.com/paper/2305.09930/) (2023)
- [Generating Transferable Adversarial Simulation Scenarios for Self-Driving via Neural Rendering](https://mlsanity.com/paper/2309.15770/) (2023)
- [ASTPrompter: Preference-Aligned Automated Language Model Red-Teaming to Generate Low-Perplexity Unsafe Prompts](https://mlsanity.com/paper/2407.09447/) (2024)
- [Adversarial and Reactive Traffic Entities for Behavior-Realistic Driving Simulation: A Review](https://mlsanity.com/paper/2409.14196/) (2024)
- [Rate-Informed Discovery via Bayesian Adaptive Multifidelity Sampling](https://mlsanity.com/paper/2411.17826/) (2024)

## Abstract (arXiv)

This paper presents a method for testing the decision making systems of autonomous vehicles. Our approach involves perturbing stochastic elements in the vehicle's environment until the vehicle is involved in a collision. Instead of applying direct Monte Carlo sampling to find collision scenarios, we formulate the problem as a Markov decision process and use reinforcement learning algorithms to find the most likely failure scenarios. This paper presents Monte Carlo Tree Search (MCTS) and Deep Reinforcement Learning (DRL) solutions that can scale to large environments. We show that DRL can find more likely failure scenarios than MCTS with fewer calls to the simulator. A simulation scenario involving a vehicle approaching a crosswalk is used to validate the framework. Our proposed approach is very general and can be easily applied to other scenarios given the appropriate models of the vehicle and the environment.