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.

Search Publications by: Craig I. Schlenoff (Fed)

Displaying 201 - 225 of 300

Towards Autonomous On-Road Driving via Multi-Resolutional and Hierarchical Moving Object Prediction

October 28, 2004

Author(s)

Jerome G. Ajot, Craig I. Schlenoff, Rajmohan (. Madhavan

In this paper, we present the PRIDE framework (Prediction In Dynamic Environments), which is a hierarchical multiresolutional approach for moving object prediction that incorporates multiple prediction algorithms into a single, unifying framework. PRIDE is

The Effect of Process Models on Short-term Prediction of Moving Objects for Unmanned Ground Vehicles

October 6, 2004

Author(s)

Rajmohan Madhavan, Craig I. Schlenoff

We are developing a novel framework, PRIDE (PRediction In Dynamic Environments), to perform moving object prediction for unmanned ground vehicles. The underlying concept is based upon a multi-resolutional, hierarchical approach which incorporates multiple

Achieving Intelligent Performance in Autonomous On-Road Driving

October 1, 2004

Author(s)

Craig I. Schlenoff, J L. Evans, Anthony J. Barbera, James S. Albus, Elena R. Messina, Stephen B. Balakirsky

This paper describes NIST s efforts in evaluating what it will take to achieve autonomous human-level driving skills in terms of time and funding. NIST has approached this problem from several perspectives: considering the current state-of-the-art in

Task Analysis of Autonomous On-Road Driving

October 1, 2004

Author(s)

Anthony J. Barbera, John A. Horst, Craig I. Schlenoff, David Aha

The Real-time Control System (RCS) Methodology has evolved over a number of years as a technique to capture task knowledge and organize it into a framework conducive to implementation in computer control systems. The fundamental premise of this methodology

Editorial: Knowledge engineering on ontologies for autonomous systems 2004 AAAI Spring Symposium

August 4, 2004

Author(s)

Craig I. Schlenoff, M Uschold

The NIST Road Network Database: Version 1.0

July 30, 2004

Author(s)

Craig I. Schlenoff, Stephen B. Balakirsky, Tony Barbera, Christopher J. Scrapper Jr, Jerome G. Ajot, Eric Hui

For an autonomous vehicle to be able to navigate a road network, it must be aware of and must respond appropriately to any object it encounters. This includes other vehicles, pedestrians, debris, construction, accidents, emergency vehicles, ? and it also

The NIST Road Network Database: Version 1.0

July 30, 2004

Author(s)

Craig I. Schlenoff, Stephen B. Balakirsky, Anthony J. Barbera, Christopher J. Scrapper Jr, Jerome G. Ajot, Eric Hui, M Paredes

Experiences in Developing an Intelligent Ground Vehicle (IGV) Ontology in Protege

July 8, 2004

Author(s)

Craig I. Schlenoff, Tony Barbera, R Washington

How Task Analysis Can Be Used to Derive and Organize the Knowledgefor the Control of Autonomous Vehicles

June 17, 2004

Author(s)

Anthony J. Barbera, James S. Albus, Elena R. Messina, Craig I. Schlenoff, John A. Horst

The Real-time Control System (RCS) Methodology has evolved over a number ofyears as a technique to capture task knowledge and organize it in aframework conducive to implementation in computer control systems. The fundamental premise of this methodology is

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

May 3, 2004

Author(s)

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

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

Report on 2004 AAAI Spring Symposia: Knowledge Representation and Ontologies for Autonomous Systems

April 30, 2004

Author(s)

Craig I. Schlenoff

This report describes the results of the 2004 AAAI Knowledge Representation and Ontologies for Autonomous Systems Spring Symposium, which was held at Stanford on March 22-23, 2004. The symposium was motivated by the desire to bring together colleagues in

Editorial Knowledge Representation and Ontologies for Autonomous Systems--2004 AAAI Spring Symposium

March 24, 2004

Author(s)

Craig I. Schlenoff, M Uschold

How Task Analysis Can Be Used to Derive and Organize the Knowledge for the Control of Autonomous Vehicles

March 24, 2004

Author(s)

Tony Barbera, James S. Albus, Elena R. Messina, Craig I. Schlenoff, John A. Horst

The Real-time Control System (RCS) Methodology has evolved over a number of years as a technique to capture task knowledge and organize it in a framework conducive to implementation in computer control systems. The fundamental premise of this methodology