A Combinatorial Approach to Testing Deep Neural Network-based Autonomous Driving Systems

Abstract

Recent advancements in the field of deep learning have enabled its application in Autonomous Driving Systems (ADS). A Deep Neural Network (DNN) model is often used to perform tasks such as pedestrian detection, object detection, and steering control in ADS. Unfortunately, DNN models could exhibit incorrect or unexpected behavior in real-world scenarios. There is a need to rigorously test these models with real-world driving scenarios so that safety-critical bugs can be detected before their deployment in the real world.In this paper, we propose a combinatorial approach to testing DNN models. Our approach generates test images by applying a set of combinations of some basic image transformation operations to a seed image. First, we identify a set of valid transformation operations or simply transformations. Next, we design an input parameter model based on the valid transformations and generate a t-way (t=2) combinatorial test set. Each test represents a combination of transformations, and can be used to produce a test image. We execute the test images on a DNN model and distinguish between consistent and inconsistent behavior using a relation. We conducted an experimental evaluation of our approach on three DNN models that are used in the Udacity challenge. Our results suggest that test images generated by our approach can effectively identify inconsistent behaviors and can significantly increase neuron coverage. To the best of our knowledge, our work is the first effort to use a combinatorial testing approach to generating test images based on image transformations for testing DNNs used in ADS.