DeepTest: automated testing of deep-neural-network-driven autonomous cars.

摘要

A very promising and well-argued account, this paper presents a novel approach to systematically testing and automatically detecting erroneous behaviors in deep neural network (DNN)-driven vehicles. The purpose of the proposed research is to find a solution for the "incorrect/unexpected corner-case behaviors that can lead to dangerous consequences." The authors claim that this can be achieved conceptually "by adding error-inducing inputs to the training dataset and also by possibly changing the model structure." So, the proposed solution leverages the notion of neuron coverage "as a guidance mechanism for systematically exploring different types of car behaviors" and demonstrates that different image transformations lead to activating "different sets of neurons in self-driving car DNNs." A result of combining these two observations, transformation-specific metamor-phic relations between multiple executions of the tested DNN are being used to automatically detect erroneous corner-case behaviors.