First-order short-range mover prediction model (SRMPM)

Jamahl Overstreet, Farshad Khorrami

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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 cooperative 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.

Original languageEnglish (US)
Title of host publicationProceedings of the 2011 American Control Conference, ACC 2011
Pages5318-5323
Number of pages6
StatePublished - 2011
Event2011 American Control Conference, ACC 2011 - San Francisco, CA, United States
Duration: Jun 29 2011Jul 1 2011

Other

Other2011 American Control Conference, ACC 2011
CountryUnited States
CitySan Francisco, CA
Period6/29/117/1/11

Fingerprint

Mobile agents
Gaussian distribution
Kalman filters
Probability distributions
Decision making

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

Cite this

Overstreet, J., & Khorrami, F. (2011). First-order short-range mover prediction model (SRMPM). In Proceedings of the 2011 American Control Conference, ACC 2011 (pp. 5318-5323). [5991026]

First-order short-range mover prediction model (SRMPM). / Overstreet, Jamahl; Khorrami, Farshad.

Proceedings of the 2011 American Control Conference, ACC 2011. 2011. p. 5318-5323 5991026.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Overstreet, J & Khorrami, F 2011, First-order short-range mover prediction model (SRMPM). in Proceedings of the 2011 American Control Conference, ACC 2011., 5991026, pp. 5318-5323, 2011 American Control Conference, ACC 2011, San Francisco, CA, United States, 6/29/11.
Overstreet J, Khorrami F. First-order short-range mover prediction model (SRMPM). In Proceedings of the 2011 American Control Conference, ACC 2011. 2011. p. 5318-5323. 5991026
Overstreet, Jamahl ; Khorrami, Farshad. / First-order short-range mover prediction model (SRMPM). Proceedings of the 2011 American Control Conference, ACC 2011. 2011. pp. 5318-5323
@inproceedings{7c98907b02874e809ea890503efaeeda,
title = "First-order short-range mover prediction model (SRMPM)",
abstract = "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 cooperative 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.",
author = "Jamahl Overstreet and Farshad Khorrami",
year = "2011",
language = "English (US)",
isbn = "9781457700804",
pages = "5318--5323",
booktitle = "Proceedings of the 2011 American Control Conference, ACC 2011",

}

TY - GEN

T1 - First-order short-range mover prediction model (SRMPM)

AU - Overstreet, Jamahl

AU - Khorrami, Farshad

PY - 2011

Y1 - 2011

N2 - 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 cooperative 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.

AB - 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 cooperative 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.

UR - http://www.scopus.com/inward/record.url?scp=80053155217&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80053155217&partnerID=8YFLogxK

M3 - Conference contribution

SN - 9781457700804

SP - 5318

EP - 5323

BT - Proceedings of the 2011 American Control Conference, ACC 2011

ER -