# Search-based Test-Case Generation by Monitoring Responsibility Safety Rules

arXiv: https://arxiv.org/abs/2005.00326 · HTML: https://mlsanity.com/paper/2005.00326/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Apr 2020.

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

- [From Dashcam Videos to Driving Simulations: Stress Testing Automated Vehicles against Rare Events](https://mlsanity.com/paper/2411.16027/) (2024)

## Abstract (arXiv)

The safety of Automated Vehicles (AV) as Cyber-Physical Systems (CPS) depends on the safety of their consisting modules (software and hardware) and their rigorous integration. Deep Learning is one of the dominant techniques used for perception, prediction, and decision making in AVs. The accuracy of predictions and decision-making is highly dependant on the tests used for training their underlying deep-learning. In this work, we propose a method for screening and classifying simulation-based driving test data to be used for training and testing controllers. Our method is based on monitoring and falsification techniques, which lead to a systematic automated procedure for generating and selecting qualified test data. We used Responsibility Sensitive Safety (RSS) rules as our qualifier specifications to filter out the random tests that do not satisfy the RSS assumptions. Therefore, the remaining tests cover driving scenarios that the controlled vehicle does not respond safely to its environment. Our framework is distributed with the publicly available S-TALIRO and Sim-ATAV tools.