# Diversity-guided Search Exploration for Self-driving Cars Test Generation through Frenet Space Encoding

arXiv: https://arxiv.org/abs/2401.14682 · HTML: https://mlsanity.com/paper/2401.14682/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2024.

## Built on by (most cited first, 2 of 2)

- [Generating Critical Scenarios for Testing Automated Driving Systems](https://mlsanity.com/paper/2412.02574/) (2024)
- [Learning test generators for cyber-physical systems](https://mlsanity.com/paper/2410.03202/) (2024)

## Abstract (arXiv)

The rise of self-driving cars (SDCs) presents important safety challenges to address in dynamic environments. While field testing is essential, current methods lack diversity in assessing critical SDC scenarios. Prior research introduced simulation-based testing for SDCs, with Frenetic, a test generation approach based on Frenet space encoding, achieving a relatively high percentage of valid tests (approximately 50%) characterized by naturally smooth curves. The "minimal out-of-bound distance" is often taken as a fitness function, which we argue to be a sub-optimal metric. Instead, we show that the likelihood of leading to an out-of-bound condition can be learned by the deep-learning vanilla transformer model. We combine this "inherently learned metric" with a genetic algorithm, which has been shown to produce a high diversity of tests. To validate our approach, we conducted a large-scale empirical evaluation on a dataset comprising over 1,174 simulated test cases created to challenge the SDCs behavior. Our investigation revealed that our approach demonstrates a substantial reduction in generating non-valid test cases, increased diversity, and high accuracy in identifying safety violations during SDC test execution.