Testing autonomous vehicles (AVs) under various environmental scenarios that lead the vehicles to unsafe situations is known to be 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 and then calculates probabilities based on these features with the attention mechanism. To assess the cost-effectiveness of EpiTESTER, we compare it with a classical genetic algorithm (GA) (i.e., without any epigenetic mechanism implemented) and EpiTESTER with equal probability for each gene. We evaluate EpiTESTER with four initial environments from CARLA, an open-source simulator for autonomous driving research, and an end-to-end AV controller, Interfuser. 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.

翻译：在多种可能导致自动驾驶车辆（AV）陷入危险场景的环境条件下进行测试，已被公认为一项挑战。考虑到环境场景的无限可能性，高效识别关键场景至关重要。为此，我们受表观遗传学启发，提出了一种新颖的测试方法——EpiTESTER，该机制使物种能够适应突发环境变化。具体而言，EpiTESTER采用基因沉默作为其表观遗传机制，通过调控基因表达来抑制特定基因的表达，并且基因表达概率随环境变化动态计算。针对AV场景中多种数据模态（如图像、激光雷达点云），EpiTESTER利用多模型融合Transformer从环境因子中提取高层特征表示，并通过注意力机制基于这些特征计算概率。为评估EpiTESTER的成本效益，我们将其与经典遗传算法（GA，即未实现任何表观遗传机制）以及为每个基因赋予相等概率的EpiTESTER变体进行了对比。基于自动驾驶研究开源仿真器CARLA中的四个初始环境及端到端AV控制器Interfuser进行测试，结果表明：与基线方法相比，EpiTESTER在识别关键场景方面取得了显著性能提升，证实了将表观遗传机制应用于实际问题求解是一种有效方案。