# Learning Critical Scenarios in Feedback Control Systems for Automated Driving

arXiv: https://arxiv.org/abs/2209.12586 · HTML: https://mlsanity.com/paper/2209.12586/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Adversarial scenarios and safety testing, one of 9 directions in End-to-end autonomous driving.
- Builds on 5 of its 26 references that are in the map.
- First posted on arXiv 26 Sep 2022.

## Placed in

- End-to-end autonomous driving / [Adversarial scenarios and safety testing](https://mlsanity.com/driving/adversarial-scenarios-and-safety-testing/)

## Builds on (most cited first, 5 of 5)

- [Scenarios for Development, Test and Validation of Automated Vehicles](https://mlsanity.com/paper/1801.08598/) (2018)
- [Global optimization via inverse distance weighting and radial basis functions](https://mlsanity.com/paper/1906.06498/) (2019)
- [Finding Critical Scenarios for Automated Driving Systems: A Systematic Literature Review](https://mlsanity.com/paper/2110.08664/) (2021)
- [Preference-based MPC calibration](https://mlsanity.com/paper/2003.11294/) (2020)
- [C-GLISp: Preference-Based Global Optimization under Unknown Constraints with Applications to Controller Calibration](https://mlsanity.com/paper/2106.05639/) (2021)

## Built on by (most cited first, 1 of 1)

- [Inverse Optimality for Fair Digital Twins: A Preference-based approach](https://mlsanity.com/paper/2512.01650/) (2025)

## Abstract (arXiv)

Testing is essential for verifying and validating control designs, especially in safety-critical applications. In particular, the control system governing an automated driving vehicle must be proven reliable enough for its acceptance on the market. Recently, much research has focused on scenario-based methods. However, the number of possible driving scenarios to test is in principle infinite. In this paper, we formalize a learning-based optimization framework to generate corner test-cases, where we take into account the operational design domain. We examine the approach on the case of a feedback control system for automated driving, for which we suggest the design of the objective function expressing the criticality of scenarios. Numerical tests on two logical scenarios of the case study demonstrate that the approach can identify critical scenarios within a limited number of closed-loop experiments.