Abstract
There is a growing interest in deploying complex deep neural networks (DNN) in autonomous systems to extract task-specific information from real-time sensor data and drive critical tasks. The perturbations in sensor data due to noise or environmental conditions can lead to errors in information extraction and degrade reliability of the entire autonomous systems. This paper presents a light-weight deep learning plat-form, WarningNet, that operates on sensor data to estimate potential task failures due to spatiotemporal input perturbations. Experimental results show that WarningNet can provide early warning of the performance degradation of different tasks within a fraction of the time required for the task to complete. As a case-study, we show that the early warning can be leveraged to improve the task reliability under adverse condition using on-demand input pre-processing.
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