We present Prunario, a novel technique for effectively testing autonomous driving systems (ADS). Ensuring the safety of ADS is critical, as their failures can lead to severe casualties. While ADS testing methods have advanced in recent years, they remain unsatisfactory in generating diverse test scenarios that induce distinct driving behaviors-a key requirement for thoroughly evaluating ADS across different situations. To address this, Prunario employs a novel simulation prediction technique to estimate ADS runtime behavior and prune redundant test scenarios that yield similar driving records. Experimental results demonstrate Prunario's effectiveness: it uncovered 23 previously undetected bugs in an industrial-strength ADS and outperformed three state-of-the-art testing techniques.
Kim et al. (Fri,) studied this question.