# AmbieGen: A Search-based Framework for Autonomous Systems Testing

arXiv: https://arxiv.org/abs/2301.01234 · HTML: https://mlsanity.com/paper/2301.01234/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jan 2023.

## Built on by (most cited first, 9 of 9)

- [Benchmarking Image Perturbations for Testing Automated Driving Assistance Systems](https://mlsanity.com/paper/2501.12269/) (2025)
- [DURA-CPS: A Multi-Role Orchestrator for Dependability Assurance in LLM-Enabled Cyber-Physical Systems](https://mlsanity.com/paper/2506.06381/) (2025)
- [Can Search-Based Testing with Pareto Optimization Effectively Cover Failure-Revealing Test Inputs?](https://mlsanity.com/paper/2410.11769/) (2024)
- [Navigating the Edge with the State-of-the-Art Insights into Corner Case Identification and Generation for Enhanced Autonomous Vehicle Safety](https://mlsanity.com/paper/2503.00077/) (2025)
- [Learning test generators for cyber-physical systems](https://mlsanity.com/paper/2410.03202/) (2024)
- [VisionGRU: A Linear-Complexity RNN Model for Efficient Image Analysis](https://mlsanity.com/paper/2412.18178/) (2024)
- [CORTEX-AVD: A Framework for CORner Case Testing and EXploration in Autonomous Vehicle Development](https://mlsanity.com/paper/2504.03989/) (2025)
- [Active Learning Methods for Efficient Data Utilization and Model Performance Enhancement](https://mlsanity.com/paper/2504.16136/) (2025)
- [DreamTest: World-Model Surrogates for Search-Based Testing of Deep Reinforcement Learning Agents](https://mlsanity.com/paper/2610.04494/) (2026)

## Abstract (arXiv)

Thorough testing of safety-critical autonomous systems, such as self-driving cars, autonomous robots, and drones, is essential for detecting potential failures before deployment. One crucial testing stage is model-in-the-loop testing, where the system model is evaluated by executing various scenarios in a simulator. However, the search space of possible parameters defining these test scenarios is vast, and simulating all combinations is computationally infeasible. To address this challenge, we introduce AmbieGen, a search-based test case generation framework for autonomous systems. AmbieGen uses evolutionary search to identify the most critical scenarios for a given system, and has a modular architecture that allows for the addition of new systems under test, algorithms, and search operators. Currently, AmbieGen supports test case generation for autonomous robots and autonomous car lane keeping assist systems. In this paper, we provide a high-level overview of the framework's architecture and demonstrate its practical use cases.