标题： TigAug：自动驾驶系统中交通灯检测的测试数据增强 显示英文标题

标题： TigAug: Data Augmentation for Testing Traffic Light Detection in Autonomous Driving Systems

摘要： 自动驾驶车辆技术在过去几十年中得到了发展，最近的传感和计算技术进步推动了这一领域的发展。 确保自动驾驶系统（ADSs）的可靠性和鲁棒性存在迫切需求。 尽管在测试各种ADS模块方面取得了近期成果，但对ADS中交通灯检测模型的自动化测试关注较少。 一种常见做法是手动收集和标记交通灯数据。 然而，这需要大量的人力，并且在不同的驾驶环境中收集多样化数据甚至不可能。 为了解决这些问题，我们提出了并实现了TigAug，以自动增强已标记的交通灯图像，用于测试ADS中的交通灯检测模型。 我们基于对天气环境、相机属性和交通灯属性的系统理解，构建了两组变形关系和三组变换。 我们使用增强后的图像通过特定于变换的变形关系检测交通灯检测模型的错误行为，并通过重新训练提高交通灯检测模型的性能。 与四种最先进的交通灯检测模型和两个交通灯数据集进行的大规模实验表明，i) TigAug在测试交通灯检测模型方面是有效的，ii) TigAug在合成交通灯图像方面是高效的，iii) TigAug生成的交通灯图像具有可接受的自然性。 显示英文摘要

摘要： Autonomous vehicle technology has been developed in the last decades with recent advances in sensing and computing technology. There is an urgent need to ensure the reliability and robustness of autonomous driving systems (ADSs). Despite the recent achievements in testing various ADS modules, little attention has been paid on the automated testing of traffic light detection models in ADSs. A common practice is to manually collect and label traffic light data. However, it is labor-intensive, and even impossible to collect diverse data under different driving environments. To address these problems, we propose and implement TigAug to automatically augment labeled traffic light images for testing traffic light detection models in ADSs. We construct two families of metamorphic relations and three families of transformations based on a systematic understanding of weather environments, camera properties, and traffic light properties. We use augmented images to detect erroneous behaviors of traffic light detection models by transformation-specific metamorphic relations, and to improve the performance of traffic light detection models by retraining. Large-scale experiments with four state-of-the-art traffic light detection models and two traffic light datasets have demonstrated that i) TigAug is effective in testing traffic light detection models, ii) TigAug is efficient in synthesizing traffic light images, and iii) TigAug generates traffic light images with acceptable naturalness.