A Markovian Error Model for False Negatives in DNN-based Perception-Driven Control Systems

Abstract

This paper presents an improved Markovian error model for Deep Neural Network (DNN) based perception in autonomous vehicles and other perception-driven control systems. Many modern autonomous systems rely on DNN-driven perception-based control/planning methodologies such as autonomous navigation, where the perception errors significantly affect the control/planning performance and the systems’ safety. The traditional independent, identically-distributed (IID) perception error model is inadequate for perception-based control/planning applications because image sequences supplied to a DNN-based perception module are not independent in the real world. Based on this observation, we develop a novel Markov model to describe the error behavior of a DNN perception model—an error in one frame is likely to signal errors in successive frames, effectively reducing sample rate for the control command. We evaluate the effect of Markovian perception errors on a drone-control simulator and show that the Markovian error model provides a better estimate of control performance than does a traditional independent, identically-distributed (IID) model.