# Adaptive Stress Testing with Reward Augmentation for Autonomous Vehicle Validation

arXiv: https://arxiv.org/abs/1908.01046 · HTML: https://mlsanity.com/paper/1908.01046/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2019.

## Built on by (most cited first, 30 of 33)

- [Explainability of deep vision-based autonomous driving systems: Review and challenges](https://mlsanity.com/paper/2101.05307/) (2021)
- [Generating and Characterizing Scenarios for Safety Testing of Autonomous Vehicles](https://mlsanity.com/paper/2103.07403/) (2021)
- [Many-Objective Reinforcement Learning for Online Testing of DNN-Enabled Systems](https://mlsanity.com/paper/2210.15432/) (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)
- [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)
- [Safety of the Intended Driving Behavior Using Rulebooks](https://mlsanity.com/paper/2105.04472/) (2021)
- [(Re)2H2O: Autonomous Driving Scenario Generation via Reversely Regularized Hybrid Offline-and-Online Reinforcement Learning](https://mlsanity.com/paper/2302.13726/) (2023)
- [Risk-Driven Design of Perception Systems](https://mlsanity.com/paper/2205.10677/) (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)
- [Sample-Based Bounds for Coherent Risk Measures: Applications to Policy Synthesis and Verification](https://mlsanity.com/paper/2204.09833/) (2022)
- [Model-based Validation as Probabilistic Inference](https://mlsanity.com/paper/2305.09930/) (2023)
- [DeepQTest: Testing Autonomous Driving Systems with Reinforcement Learning and Real-world Weather Data](https://mlsanity.com/paper/2310.05170/) (2023)
- [A Deep Reinforcement Learning Approach to Rare Event Estimation](https://mlsanity.com/paper/2211.12470/) (2022)
- [Adaptive Testing Environment Generation for Connected and Automated Vehicles with Dense Reinforcement Learning](https://mlsanity.com/paper/2402.19275/) (2024)
- [Reinforcement Learning for Online Testing of Autonomous Driving Systems: a Replication and Extension Study](https://mlsanity.com/paper/2403.13729/) (2024)
- [AED: Automatic Discovery of Effective and Diverse Vulnerabilities for Autonomous Driving Policy with Large Language Models](https://mlsanity.com/paper/2503.20804/) (2025)
- [Rationale-aware Autonomous Driving Policy utilizing Safety Force Field implemented on CARLA Simulator](https://mlsanity.com/paper/2211.10237/) (2022)
- [Suicidal Pedestrian: Generation of Safety-Critical Scenarios for Autonomous Vehicles](https://mlsanity.com/paper/2309.00249/) (2023)
- [Attacking Motion Planners Using Adversarial Perception Errors](https://mlsanity.com/paper/2311.12722/) (2023)
- [A novel framework for adaptive stress testing of autonomous vehicles in multi-lane roads](https://mlsanity.com/paper/2402.11813/) (2024)
- [Generating Critical Scenarios for Testing Automated Driving Systems](https://mlsanity.com/paper/2412.02574/) (2024)
- [Characterizing the Robustness of Black-Box LLM Planners Under Perturbed Observations with Adaptive Stress Testing](https://mlsanity.com/paper/2505.05665/) (2025)
- [Conveying Autonomous Robot Capabilities through Contrasting Behaviour Summaries](https://mlsanity.com/paper/2304.00367/) (2023)
- [ReMAV: Reward Modeling of Autonomous Vehicles for Finding Likely Failure Events](https://mlsanity.com/paper/2308.14550/) (2023)
- [Robust Planning for Autonomous Driving via Mixed Adversarial Diffusion Predictions](https://mlsanity.com/paper/2505.12327/) (2025)
- [Efficient Safety Testing of Autonomous Vehicles via Adaptive Search over Crash-Derived Scenarios](https://mlsanity.com/paper/2508.06575/) (2025)
- [Aircraft Collision Avoidance Systems: Technological Challenges and Solutions on the Path to Regulatory Acceptance](https://mlsanity.com/paper/2510.20916/) (2025)

## Abstract (arXiv)

Determining possible failure scenarios is a critical step in the evaluation of autonomous vehicle systems. Real-world vehicle testing is commonly employed for autonomous vehicle validation, but the costs and time requirements are high. Consequently, simulation-driven methods such as Adaptive Stress Testing (AST) have been proposed to aid in validation. AST formulates the problem of finding the most likely failure scenarios as a Markov decision process, which can be solved using reinforcement learning. In practice, AST tends to find scenarios where failure is unavoidable and tends to repeatedly discover the same types of failures of a system. This work addresses these issues by encoding domain relevant information into the search procedure. With this modification, the AST method discovers a larger and more expressive subset of the failure space when compared to the original AST formulation. We show that our approach is able to identify useful failure scenarios of an autonomous vehicle policy.