一句话：提出 Prunario，利用模拟预测剪枝冗余测试场景，提升自动驾驶系统测试的场景多样性与缺陷发现能力。
自动驾驶系统（ADS）的安全性至关重要，其失效可能造成严重人员伤亡。近年来 ADS 测试方法虽有进展，但仍难以生成能够诱发不同驾驶行为的多样化测试场景，而这正是全面评估 ADS 的关键需求。为解决该问题，Prunario 采用一种新颖的模拟预测技术来估计 ADS 的运行时行为，并据此剪枝那些会产生相似驾驶记录的冗余测试场景，从而把测试预算集中在更具区分度的场景上。实验结果表明了 Prunario 的有效性：它在一个工业级 ADS 中发现了 23 个此前未被检测到的缺陷，并优于三种当前最先进的测试技术。
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.
@article{paperbot3808,
title = {Prunario: Testing Autonomous Driving Systems by Pruning Likely Redundant Scenarios},
author = {Minsu Kim and Sunbeom So and Hakjoo Oh},
journal = {Proceedings of the ACM on Programming Languages},
volume = {10},
number = {OOPSLA1},
year = {2026},
doi = {10.1145/3798262}
}