Autonomous Navigation Assurance with Explainable AI and Security Monitoring
Denzel Hamilton, Kevin Kornegay, Lanier A. Watkins · 2020
With the rise of disruptive artificial intelligence (AI) technology, assuring autonomous systems is of the utmost importance. The drive to spread autonomous vehicles to all whether military or the commercial space is makes autonomous navigation a must for assurance. In this paper, we introduce the concept of using explainable AI to assure the operational autonomy of a vehicle while monitoring the security of the system. We demonstrate our paradigm by developing a system monitor using AI (i.e., Random Forest Tree algorithm) to assure the native autonomy of an autonomous Turtlebot3 and monitor security status of the Turtlebot3 computer system and sensor data. Then we dig into the decision trees of the AI algorithm and extract information to help explain the navigation autonomy and decisions made by our monitor. The explainability of the security monitor is not part of this experiment but the security monitor is useful for helping the autonomous navigation make decisions in the instance of attack. Further, we demonstrate the efficacy of the monitor by subjecting the Turtlebot3 to a "Sharp Turn Maze", which was designed to be hard to solve by the native autonomy, and a barrage of security attacks. These experiments allow the AI-Monitor to demonstrate its ability to identify and explain unproductive or wrong decisions made by the native autonomy and identify cyber-security related faults in order to trigger navigation contingencies.