A Dynamic-Bayesian-Network-Based Approach to Predict Immediate Future Action of an Intelligent Agent

Abstract

Predicting immediate future actions taken by an intelligent agent is considered an essential problem in
human-autonomy teaming (HAT) in many fields, such as industries and transportation, particularly to
improve human comprehension of the agent as their non-human counterpart. Moreover, the results of such
predictions can shorten the human response time to gain control back from their non-human counterpart
when it is required. An example case of HAT that can be benefitted from the action predictor is partially
automated driving with the autopilot agent as the intelligent agent. Hence, this research aims to develop an
approach to predict the immediate future actions of an intelligent agent with partially automated driving
as the experimental case. The proposed approach relies on a machine learning method called naive Bayes
to develop an action classifier, and the Dynamic Bayesian Network (DBN) as the action predictor. The
autonomous driving simulation software called Carla is used for the simulation. The results show that the
proposed approach is applicable to predict an intelligent agent’s three-second time-window immediate future
action.