NOTICE: Due to a lapse in annual appropriations, most of this website is not being updated. Learn more.

Form submissions will still be accepted but will not receive responses at this time. Sections of this site for programs using non-appropriated funds (such as NVLAP) or those that are excepted from the shutdown (such as CHIPS and NVD) will continue to be updated.

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.

PUBLICATIONS

A Hierarchical, Multi-Resolutional Moving Object Prediction Approach for Autonomous On-Road Driving

Published

Author(s)

Craig I. Schlenoff, Rajmohan Madhavan, Anthony J. Barbera

Abstract

In this paper, we present a hierarchical, multi-resolutional approach for moving object prediction via estimation-theoretic and situation-based probabilistic techniques. The results of the prediction are made available to a planner to allow it to make accurate plans in the presence of a dynamic environment. We have applied this approach to an on-road driving control hierarchy being developed as part of the DARPA Mobile Autonomous Robotic Systems (MARS) effort. Experimental results are shown in two simulation environments.
Citation
Proceedings of the 2004 ICRA Conference
Pub Type
Journals

Download Paper

Local Download

Keywords

4D/RCS, dynamic environment, Kalman filter, moving object prediction, multi-resolutional, on-road driving, probabilistic prediction

Citation

Schlenoff, C. , Madhavan, R. and Barbera, A. (2004), A Hierarchical, Multi-Resolutional Moving Object Prediction Approach for Autonomous On-Road Driving, Proceedings of the 2004 ICRA Conference, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=824500 (Accessed October 8, 2025)

Issues

If you have any questions about this publication or are having problems accessing it, please contact [email protected].

Created May 3, 2004, Updated February 17, 2017
Was this page helpful?