Towards artificial situation awareness by autonomous vehicles

Abstract This paper presents a novel approach to artificial situation awareness for an autonomous vehicle operating in complex dynamic environments populated by other agents. A key aspect of situation awareness is the use of mental models to predict future states of the environment, allowing safe and rational routing decisions to be made. We present a technique for predicting future discrete state transitions (such as the commencement of a turn) by other agents, based upon an uncertain mental model. Predictions take the form of univariate Gaussian Probability Density Functions which capture the inherent uncertainty in transition time whilst still providing great benefit to a decision making system. The prediction distributions are compared with Monte Carlo simulations and show an excellent correlation over long prediction horizons.