Power line communication channel modeling for in-vehicle applications

This paper addresses the problem of generating an accurate power line communication channel model for in-vehicle applications. The proposed modeling methodology is based on a state-of-the-art behavioral representation based on the multipath propagation of signals in a possibly complicated interconnected power structure. The procedure for the computation of model parameters is thoroughly discussed. The effectiveness of the approach has been demonstrated on a set of real measurements carried out on a commercial automobil

Arguing Machines: Human Supervision of Black Box AI Systems That Make Life-Critical Decisions

We consider the paradigm of a black box AI system that makes life-critical decisions. We propose an "arguing machines" framework that pairs the primary AI system with a secondary one that is independently trained to perform the same task. We show that disagreement between the two systems, without any knowledge of underlying system design or operation, is sufficient to arbitrarily improve the accuracy of the overall decision pipeline given human supervision over disagreements. We demonstrate this system in two applications: (1) an illustrative example of image classification and (2) on large-scale real-world semi-autonomous driving data. For the first application, we apply this framework to image classification achieving a reduction from 8.0% to 2.8% top-5 error on ImageNet. For the second application, we apply this framework to Tesla Autopilot and demonstrate the ability to predict 90.4% of system disengagements that were labeled by human annotators as challenging and needing human supervision