Testing autonomous vehicles (AVs) under various environmental scenarios that lead the vehicles to unsafe situations is challenging. Given the infinite possible environmental scenarios, it is essential to find critical scenarios efficiently. To this end, we propose a novel testing method, named EpiTESTER , by taking inspiration from epigenetics, which enables species to adapt to sudden environmental changes. In particular, EpiTESTER adopts gene silencing as its epigenetic mechanism, which regulates gene expression to prevent the expression of a certain gene, and the probability of gene expression is dynamically computed as the environment changes. Given different data modalities (e.g., images, lidar point clouds) in the context of AV, EpiTESTER benefits from a multi-model fusion transformer to extract high-level feature representations from environmental factors. Next, it calculates probabilities based on these features with the attention mechanism. To assess the cost-effectiveness of EpiTESTER , we compare it with a probabilistic search algorithm (Simulated Annealing, SA), a classical genetic algorithm (GA) (i.e., without any epigenetic mechanism implemented), and EpiTESTER with equal probability for each gene. We evaluate EpiTESTER with six initial environments from CARLA, an open-source simulator for autonomous driving research, and two end-to-end AV controllers, Interfuser and TCP. Our results show that EpiTESTER achieved a promising performance in identifying critical scenarios compared to the baselines, showing that applying epigenetic mechanisms is a good option for solving practical problems.

中文翻译：

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EpiTESTER：使用表观遗传算法和注意力机制测试自动驾驶汽车


在各种环境情景下测试自动驾驶汽车 （AV） 具有挑战性，这些场景会导致车辆进入不安全的情况。鉴于无限可能的环境场景，必须有效地找到关键场景。为此，我们从表观遗传学中汲取灵感，提出了一种名为 EpiTESTER 的新型测试方法，使物种能够适应突然的环境变化。特别是，EpiTESTER 采用基因沉默作为其表观遗传机制，调节基因表达以防止某个基因的表达，并且随着环境的变化动态计算基因表达的概率。在 AV 环境中给定不同的数据模态（例如图像、激光雷达点云），EpiTESTER 受益于多模型融合转换器，可以从环境因素中提取高级特征表示。接下来，它使用注意力机制根据这些特征计算概率。为了评估 EpiTESTER 的成本效益，我们将其与概率搜索算法（模拟退火，SA）、经典遗传算法 （GA）（即未实施任何表观遗传机制）和 EpiTESTER 进行比较，每个基因的概率相同。我们使用 CARLA 的六个初始环境（一个用于自动驾驶研究的开源模拟器）和两个端到端 AV 控制器 Interfuser 和 TCP 来评估 EpiTESTER。我们的结果表明，与基线相比，EpiTESTER 在识别关键情景方面取得了有希望的性能，表明应用表观遗传机制是解决实际问题的不错选择。