Predictive Logistics Models for Autonomous Vehicles Deployment in Adversarial Environments

Abstract

Resilient logistics operations call for a holistic and crosscutting approach to proactively address both real-time and persistent adversarial events in several operational areas to outfit mobility platforms, networks, and Command and Control (C2) systems to support continued uninterrupted operations. This research proposes the development of robust mobility platforms for Unmanned Autonomous Vehicles deployment and remote maintenance in uncertain adversarial environment with predictive logistics guarantees, including platform reliability evaluation, and remote inspection. Artificial Intelligence/Machine Learning (AI/ML) predictive analytics are employed to select, deploy, monitor, and respond to mobility field mission events. An example use case of deployment with remote activities and maintenance requirements is provided.