Recent advances in Deep Neural Networks (DNNs) and sensor technologies are enabling autonomous driving systems (ADSs) with an ever-increasing level of autonomy. However, assessing their dependability remains a critical concern. State-of-the-art ADS testing approaches modify the controllable attributes of a simulated driving environment until the ADS misbehaves. In such approaches, environment instances in which the ADS is successful are discarded, despite the possibility that they could contain hidden driving conditions in which the ADS may misbehave. In this paper, we present GENBO (GENerator of BOundary state pairs), a novel test generator for ADS testing. GENBO mutates the driving conditions of the ego vehicle (position, velocity and orientation), collected in a failure-free environment instance, and efficiently generates challenging driving conditions at the behavior boundary (i.e., where the model starts to misbehave) in the same environment instance. We use such boundary conditions to augment the initial training dataset and retrain the DNN model under test. Our evaluation results show that the retrained model has, on average, up to 3x higher success rate on a separate set of evaluation tracks with respect to the original DNN model.

翻译：深度神经网络与传感器技术的最新进展正推动自动驾驶系统实现日益提升的自主性水平。然而，评估其可靠性仍是关键问题。当前最先进的自动驾驶系统测试方法通过持续修改模拟驾驶环境中的可控属性，直至系统出现异常行为。此类方法会丢弃自动驾驶系统运行成功的环境实例，却忽略了这些实例中可能隐藏着导致系统异常行为的潜在驾驶条件。本文提出GENBO（边界状态对生成器），一种用于自动驾驶系统测试的新型测试生成器。GENBO对从无故障环境实例中采集的自我车辆驾驶条件（位置、速度与方向）进行变异，并高效地在同一环境实例中生成行为边界处（即模型开始出现异常的区域）的挑战性驾驶条件。我们利用此类边界条件对初始训练数据集进行增强，并重新训练待测的深度神经网络模型。评估结果表明，在独立的评估轨迹集上，重新训练后的模型相较于原始深度神经网络模型平均可获得高达3倍的成功率提升。