# Finding Critical Scenarios for Automated Driving Systems: A Systematic Literature Review

arXiv: https://arxiv.org/abs/2110.08664 · HTML: https://mlsanity.com/paper/2110.08664/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 178 references that are in the map.
- First posted on arXiv 16 Oct 2021.

## Builds on (most cited first, 30 of 31)

- [A Tutorial on Bayesian Optimization](https://mlsanity.com/paper/1807.02811/) (2018)
- [Deep Reinforcement Learning: An Overview](https://mlsanity.com/paper/1701.07274/) (2017)
- [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)
- [Driving in the Matrix: Can Virtual Worlds Replace Human-Generated Annotations for Real World Tasks?](https://mlsanity.com/paper/1610.01983/) (2016)
- [On a Formal Model of Safe and Scalable Self-driving Cars](https://mlsanity.com/paper/1708.06374/) (2017)
- [Augmented Reality Meets Computer Vision : Efficient Data Generation for Urban Driving Scenes](https://mlsanity.com/paper/1708.01566/) (2017)
- [Scalable End-to-End Autonomous Vehicle Testing via Rare-event Simulation](https://mlsanity.com/paper/1811.00145/) (2018)
- [Adaptive Stress Testing for Autonomous Vehicles](https://mlsanity.com/paper/1902.01909/) (2019)
- [A Survey of Algorithms for Black-Box Safety Validation of Cyber-Physical Systems](https://mlsanity.com/paper/2005.02979/) (2020)
- [Scenarios for Development, Test and Validation of Automated Vehicles](https://mlsanity.com/paper/1801.08598/) (2018)
- [Accelerated Evaluation of Automated Vehicles Safety in Lane Change Scenarios Based on Importance Sampling Techniques](https://mlsanity.com/paper/1605.04965/) (2016)
- [Liability, Ethics, and Culture-Aware Behavior Specification using Rulebooks](https://mlsanity.com/paper/1902.09355/) (2019)
- [Towards Corner Case Detection for Autonomous Driving](https://mlsanity.com/paper/1902.09184/) (2019)
- [Adaptive Stress Testing with Reward Augmentation for Autonomous Vehicle Validation](https://mlsanity.com/paper/1908.01046/) (2019)
- [Compositional Falsification of Cyber-Physical Systems with Machine Learning Components](https://mlsanity.com/paper/1703.00978/) (2017)
- [Simulation-based Adversarial Test Generation for Autonomous Vehicles with Machine Learning Components](https://mlsanity.com/paper/1804.06760/) (2018)
- [Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration](https://mlsanity.com/paper/1610.09064/) (2016)
- [Testing Scenario Library Generation for Connected and Automated Vehicles, Part I: Methodology](https://mlsanity.com/paper/1905.03419/) (2019)
- [Training Adversarial Agents to Exploit Weaknesses in Deep Control Policies](https://mlsanity.com/paper/2002.12078/) (2020)
- [Requirements-driven Test Generation for Autonomous Vehicles with Machine Learning Components](https://mlsanity.com/paper/1908.01094/) (2019)
- [ML-based Fault Injection for Autonomous Vehicles: A Case for Bayesian Fault Injection](https://mlsanity.com/paper/1907.01051/) (2019)
- [Fundamental Considerations around Scenario-Based Testing for Automated Driving](https://mlsanity.com/paper/2005.04045/) (2020)
- [Efficient Autonomy Validation in Simulation with Adaptive Stress Testing](https://mlsanity.com/paper/1907.06795/) (2019)
- [Testing Scenario Library Generation for Connected and Automated Vehicles, Part II: Case Studies](https://mlsanity.com/paper/1905.03428/) (2019)
- [Using control synthesis to generate corner cases: A case study on autonomous driving](https://mlsanity.com/paper/1807.09537/) (2018)
- [Performance Boundary Identification for the Evaluation of Automated Vehicles using Gaussian Process Classification](https://mlsanity.com/paper/1907.05364/) (2019)
- [An Agency-Directed Approach to Test Generation for Simulation-based Autonomous Vehicle Verification](https://mlsanity.com/paper/1912.05434/) (2019)
- [Safety Challenges for Autonomous Vehicles in the Absence of Connectivity](https://mlsanity.com/paper/2006.03987/) (2020)
- [Sequential Experimentation to Efficiently Test Automated Vehicles](https://mlsanity.com/paper/1707.00224/) (2017)

## Built on by (most cited first, 12 of 12)

- [A Survey on Scenario-Based Testing for Automated Driving Systems in High-Fidelity Simulation](https://mlsanity.com/paper/2112.00964/) (2021)
- [Enhancing Autonomous Vehicle Training with Language Model Integration and Critical Scenario Generation](https://mlsanity.com/paper/2404.08570/) (2024)
- [Federated Deep Learning Meets Autonomous Vehicle Perception: Design and Verification](https://mlsanity.com/paper/2206.01748/) (2022)
- [Space, Time, and Interaction: A Taxonomy of Corner Cases in Trajectory Datasets for Automated Driving](https://mlsanity.com/paper/2210.08885/) (2022)
- [A-BDD: Leveraging Data Augmentations for Safe Autonomous Driving in Adverse Weather and Lighting](https://mlsanity.com/paper/2408.06071/) (2024)
- [ECSAS: Exploring Critical Scenarios from Action Sequence in Autonomous Driving](https://mlsanity.com/paper/2209.10078/) (2022)
- [Learning Critical Scenarios in Feedback Control Systems for Automated Driving](https://mlsanity.com/paper/2209.12586/) (2022)
- [Attacking Motion Planners Using Adversarial Perception Errors](https://mlsanity.com/paper/2311.12722/) (2023)
- [Generating Critical Scenarios for Testing Automated Driving Systems](https://mlsanity.com/paper/2412.02574/) (2024)
- [A Real-time Critical-scenario-generation Framework for Testing Autonomous Driving System](https://mlsanity.com/paper/2206.00910/) (2022)
- [Vectorized Scenario Description and Motion Prediction for Scenario-Based Testing](https://mlsanity.com/paper/2302.01161/) (2023)
- [Declarative Scenario-based Testing with RoadLogic](https://mlsanity.com/paper/2603.09455/) (2026)

## Abstract (arXiv)

Scenario-based approaches have been receiving a huge amount of attention in research and engineering of automated driving systems. Due to the complexity and uncertainty of the driving environment, and the complexity of the driving task itself, the number of possible driving scenarios that an ADS or ADAS may encounter is virtually infinite. Therefore it is essential to be able to reason about the identification of scenarios and in particular critical ones that may impose unacceptable risk if not considered. Critical scenarios are particularly important to support design, verification and validation efforts, and as a basis for a safety case. In this paper, we present the results of a systematic literature review in the context of autonomous driving. The main contributions are: (i) introducing a comprehensive taxonomy for critical scenario identification methods; (ii) giving an overview of the state-of-the-art research based on the taxonomy encompassing 86 papers between 2017 and 2020; and (iii) identifying open issues and directions for further research. The provided taxonomy comprises three main perspectives encompassing the problem definition (the why), the solution (the methods to derive scenarios), and the assessment of the established scenarios. In addition, we discuss open research issues considering the perspectives of coverage, practicability, and scenario space explosion.