# Testing Scenario Library Generation for Connected and Automated Vehicles, Part I: Methodology

arXiv: https://arxiv.org/abs/1905.03419 · HTML: https://mlsanity.com/paper/1905.03419/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2019.

## Built on by (most cited first, 25 of 25)

- [Corner Case Generation and Analysis for Safety Assessment of Autonomous Vehicles](https://mlsanity.com/paper/2102.03483/) (2021)
- [Milestones in Autonomous Driving and Intelligent Vehicles Part I: Control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors](https://mlsanity.com/paper/2305.11239/) (2023)
- [Finding Critical Scenarios for Automated Driving Systems: A Systematic Literature Review](https://mlsanity.com/paper/2110.08664/) (2021)
- [Coverage based testing for V&V and Safety Assurance of Self-driving Autonomous Vehicles: A Systematic Literature Review](https://mlsanity.com/paper/2103.04364/) (2021)
- [A Scenario-Based Functional Testing Approach to Improving DNN Performance](https://mlsanity.com/paper/2307.07083/) (2023)
- [(Re)2H2O: Autonomous Driving Scenario Generation via Reversely Regularized Hybrid Offline-and-Online Reinforcement Learning](https://mlsanity.com/paper/2302.13726/) (2023)
- [PEM: Perception Error Model for Virtual Testing of Autonomous Vehicles](https://mlsanity.com/paper/2302.11919/) (2023)
- [Continual Driving Policy Optimization with Closed-Loop Individualized Curricula](https://mlsanity.com/paper/2309.14209/) (2023)
- [Make Full Use of Testing Information: An Integrated Accelerated Testing and Evaluation Method for Autonomous Driving Systems](https://mlsanity.com/paper/2501.11924/) (2025)
- [Accurately Predicting Probabilities of Safety-Critical Rare Events for Intelligent Systems](https://mlsanity.com/paper/2403.13869/) (2024)
- [HAD-Gen: Human-like and Diverse Driving Behavior Modeling for Controllable Scenario Generation](https://mlsanity.com/paper/2503.15049/) (2025)
- [Few-Shot Scenario Testing for Autonomous Vehicles Based on Neighborhood Coverage and Similarity](https://mlsanity.com/paper/2402.01795/) (2024)
- [Adaptive Testing Environment Generation for Connected and Automated Vehicles with Dense Reinforcement Learning](https://mlsanity.com/paper/2402.19275/) (2024)
- [Exploring Critical Testing Scenarios for Decision-Making Policies: An LLM Approach](https://mlsanity.com/paper/2412.06684/) (2024)
- [Flow to Rare Events: An Application of Normalizing Flow in Temporal Importance Sampling for Automated Vehicle Validation](https://mlsanity.com/paper/2407.07320/) (2024)
- [Controllable Latent Diffusion for Traffic Simulation](https://mlsanity.com/paper/2503.11771/) (2025)
- [Post-Convergence Sim-to-Real Policy Transfer: A Principled Alternative to Cherry-Picking](https://mlsanity.com/paper/2504.15414/) (2025)
- [Evolving Testing Scenario Generation Method and Intelligence Evaluation Framework for Automated Vehicles](https://mlsanity.com/paper/2306.07142/) (2023)
- [Analyze Drivers' Intervention Behavior During Autonomous Driving -- A VR-incorporated Approach](https://mlsanity.com/paper/2312.01669/) (2023)
- [Training Adversarial yet Safe Agent to Characterize Safety Performance of Highly Automated Vehicles](https://mlsanity.com/paper/2402.01576/) (2024)
- [Dynamically Expanding Capacity of Autonomous Driving with Near-Miss Focused Training Framework](https://mlsanity.com/paper/2406.02865/) (2024)
- [Optimizing Falsification for Learning-Based Control Systems: A Multi-Fidelity Bayesian Approach](https://mlsanity.com/paper/2409.08097/) (2024)
- [Control Analysis and Design for Autonomous Vehicles Subject to Imperfect AI-Based Perception](https://mlsanity.com/paper/2509.12137/) (2025)
- [SSP: An Event-Matched Syn2Sim2Phy Cross-Domain Evaluation Framework for Autonomous Driving VLA Models](https://mlsanity.com/paper/2608.14024/) (2026)
- [How corner is a corner case? Percentile control for highway scenario generation](https://mlsanity.com/paper/2610.05003/) (2026)

## Abstract (arXiv)

Testing and evaluation is a critical step in the development and deployment of connected and automated vehicles (CAVs), and yet there is no systematic framework to generate testing scenario library. This study aims to provide a general framework for the testing scenario library generation (TSLG) problem with different operational design domains (ODDs), CAV models, and performance metrics. Given an ODD, the testing scenario library is defined as a critical set of scenarios that can be used for CAV test. Each testing scenario is evaluated by a newly proposed measure, scenario criticality, which can be computed as a combination of maneuver challenge and exposure frequency. To search for critical scenarios, an auxiliary objective function is designed, and a multi-start optimization method along with seed-filling is applied. The proposed framework is theoretically proved to obtain accurate evaluation results with much fewer number of tests, if compared with the on-road test method. In part II of the study, three case studies are investigated to demonstrate the proposed methodologies. Reinforcement learning based technique is applied to enhance the searching method under high-dimensional scenarios.