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. Such approaches have two main drawbacks: (1) modifications to the simulated environment might not be easily transferable to the in-field test setting (e.g., changing the road shape); (2) 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. 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 up to 16 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（DNN）与传感器技术的近期进展正推动自动驾驶系统（ADS）不断提升自主化水平。然而，评估其可靠性仍是关键挑战。现有先进的ADS测试方法通过修改模拟驾驶环境中的可控属性，直至ADS出现异常行为。此类方法存在两大缺陷：（1）对模拟环境的修改可能难以直接迁移至实际测试场景（例如改变道路形状）；（2）ADS成功运行的环境实例被丢弃，尽管这些实例可能包含ADS潜在异常的隐藏驾驶条件。本文提出GenBo（边界状态对生成器），一种针对ADS测试的新型测试生成技术。GenBo对在无故障环境实例中采集的自车驾驶条件（位置、速度与航向角）进行突变，高效生成同一环境中行为边界处（即模型开始出现异常的点）的挑战性驾驶条件。我们利用此类边界条件扩充初始训练数据集，并对待测DNN模型进行重训练。评估结果表明，与原始DNN模型相比，重训练模型在独立评估路径集上的成功率最高提升16倍。