Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

Empirical comparison of predictive models for mobile agents

Published

Author(s)

A Henniner, Rajmohan (. Madhavan

Abstract

The need to predict an agent?s intents or future actions has been well documented in multi-agent system?s literature and has been motivated by both systematically practical and psychologically principled concerns. However, little effort has focused on the comparison of predictive modeling techniques. This paper compares the performance of three predictive models all developed for the same, well-defined modeling task. Specifically, this paper compares the performance of an Extended Kalman Filter (EKF) based model, a neural network based model and a Newtonian-based dead-reckoning model, all used to predict an agent?s trajectory and position. After introducing the background and motivation for the research, this paper reviews the form of the algorithms, the integration of the models into a large-scale simulation environment, and the means by which the performance measures are generated. Performance measures are presented over increasing levels of error tolerance.
Citation
Journal of Robotics & Autonomous Systems
Volume
49

Keywords

dead-reckoning, error tolerance, extended Kalman filter, Mobility, neural networks, Performance Metrics, Unmanned Systems

Citation

Henniner, A. and Madhavan, R. (2004), Empirical comparison of predictive models for mobile agents, Journal of Robotics & Autonomous Systems, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=822608 (Accessed March 28, 2024)
Created July 22, 2004, Updated October 12, 2021