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Search Publications by: Craig I. Schlenoff (Fed)

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Displaying 101 - 125 of 150

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

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
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

Knowledge Engineering for Real Time Intelligent Control

December 31, 2003
Author(s)
Elena R. Messina, James S. Albus, Craig I. Schlenoff, J L. Evans
The key to real-time intelligent control lies in the knowledge models that the system contains. We argue that there needs to be a more rigorous approach to engineering the knowledge within intelligent controllers. Three main classes of knowledge are

Using Ontologies to Aid Navigation Planning in Autonomous Vehicles

September 1, 2003
Author(s)
Craig I. Schlenoff, Stephen B. Balakirsky, M Uschold, R Provine, S J. Smith
This paper describes a system whose overall goal is to utilize ontologies to enhance the capabilities and performance of autonomous vehicles, particularly in the area of navigation planning. Our approach is to develop an ontology of objects based upon a

Moving Object Prediction for Off-Road Autonomous Navigation

August 25, 2003
Author(s)
Rajmohan Madhavan, Craig I. Schlenoff
The realization of on- and o.-road autonomous navigation of Unmanned Ground Vehicles (UGVs) requiresreal-time motion planning in the presence of dynamic objects with unknown trajectories. To successfully planpaths and to navigate in an unstructured

An Approach to Predicting the Location of Moving Objects During On-Road Navigation

August 15, 2003
Author(s)
Craig I. Schlenoff, Rajmohan Madhavan, Stephen B. Balakirsky
For an autonomous vehicle to navigate in real-time within a dynamic environment, it must be able to respond to moving objects. In particular, it must be able to predict, with appropriate levels of confidence, where those objects are expected to be at times

Moving Object Prediction for Off-road Autonomous Navigation

April 25, 2003
Author(s)
Rajmohan Madhavan, Craig I. Schlenoff
The realization of on- and off-road autonomous navigation of Unmanned Ground Vehicles (UGVs) requires real-time motion planning in the presence of dynamic objects with unknown trajectories. To successfully plan paths and to navigate in an unstructured