Testing and Debugging Machine Learning-based Autonomous Systems

点击翻译按钮获取中文摘要

To deploy autonomous systems, such as fully autonomous cars in open human environments, it is necessary to ensure their adherence to functional safety requirements. With systems integrating traditional software components, this is achieved through functional safety certification practices that assure specifications validity, implementation understandability, and implementation correctness. Unfortunately, modern autonomous systems rely on Deep Neural Network (DNN) components that prevent the adoption of traditional certification practices because of their black-box nature. Indeed, the specifications of DNN-based systems are implicit and consist of the inputs used to train the DNN; also, their implementation consists of layers of neurons whose logic cannot be understood through inspection. We will overcome these limitations by developing automated solutions to verify the compliance of autonomous system software with its functional safety requirements. To achieve this objective, we will develop innovative and efficient methods to leverage simulation software. Indeed, with DNN-based systems, simulated environments are necessary because specifications validity, implementation understandability, and implementation correctness can be evaluated only by exercising the system. To efficiently perform safety compliance verification activities, we will use evolutionary algorithms to drive simulators towards the generation of inputs that lead to DNN errors. To support problem understanding, we will leverage algorithms that extract information concerning DNN behaviour. To improve the system, we will generate training sets using simulators and generative networks.