Autonomous driving systems (ADSs) must be sufficiently tested to ensure their safety. Though various ADS testing methods have shown promising results, they are limited to a fixed set of vehicle characteristics settings (VCSs). The impact of variations in vehicle characteristics (e.g., mass, tire friction) on the safety of ADSs has not been sufficiently and systematically studied.Such variations are often due to wear and tear, production errors, etc., which may lead to unexpected driving behaviours of ADSs. To this end, in this paper, we propose a method, named SAFEVAR, to systematically find minimum variations to the original vehicle characteristics setting, which affect the safety of the ADS deployed on the vehicle. To evaluate the effectiveness of SAFEVAR, we employed two ADSs and conducted experiments with two driving simulators. Results show that SAFEVAR, equipped with NSGA-II, generates more critical VCSs that put the vehicle into unsafe situations, as compared with two baseline algorithms: Random Search and a mutation-based fuzzer. We also identified critical vehicle characteristics and reported to which extent varying their settings put the ADS vehicles in unsafe situations.

翻译：自主驾驶系统（ADSs）必须经过充分测试以确保其安全性。尽管各种ADS测试方法已展现出良好效果，但它们仅限于固定的车辆特性参数设置（VCSs）。车辆特性（如质量、轮胎摩擦力）的变化对ADS安全性的影响尚未得到充分且系统的研究。此类变化通常源于磨损、生产误差等因素，可能导致ADS产生意外驾驶行为。为此，本文提出一种名为SAFEVAR的方法，系统性地寻找相对于原始车辆特性设置的最小变化，这些变化会影响部署在车辆上的ADS的安全性。为评估SAFEVAR的有效性，我们采用两个ADS并在两个驾驶模拟器上进行实验。结果表明，与两种基线算法（随机搜索和基于变异的模糊测试器）相比，配备NSGA-II的SAFEVAR能够生成更多导致车辆陷入不安全状态的临界VCSs。我们还识别了关键车辆特性，并报告了其参数设置变化在何种程度上使ADS车辆陷入不安全状态。