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

TL;DR

This paper introduces a diversity-guided search method using Frenet space encoding and deep learning to generate varied and valid safety test scenarios for self-driving cars, improving over prior approaches.

Contribution

It proposes a novel approach combining a learned metric with genetic algorithms to enhance diversity and validity in SDC testing scenarios.

Findings

Significant reduction in invalid test case generation

Increased diversity of generated tests

High accuracy in detecting safety violations

Abstract

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.