First-Order Short-Range Mover Prediction Model (SRMPM)

An important problem for intelligent autonomous mobile systems/agents is the ability to predict the motions of other objects/agents. This has natural extensions to co-operative behavior control, where mobile agents avoid each other by predicting the other's motion. In this paper, we have formulated a spatial probability distribution for moving objects with respect to First-Order predictions, which take into account mobility characteristics and how they relate to probable motion. This is a novel method since the most common approach uses Kalman Filters to estimate future states based upon observed previous states only, assuming a geospatial 2-D Gaussian distribution with monolithic variances in both the normal and tangential directions of motion. Unlike prior approaches, our methodology takes into consideration specific dynamic constraints (e.g., Ackermann Steering), and probable decision making capabilities of the mover. By adding higher levels of fidelity to prediction models, more accurate and precise object tracking, avoidance, or engagement can be accomplished with already developed techniques.