No abstract

In 2007 and 2008, the Oak Ridge National Laboratory, in collaboration with several industry partners, collected real-world performance and situational data for long-haul operations of Class 8 trucks from a fleet engaged in normal freight operations. Such data and information are useful to support Class 8 modeling of combination truck performance and technology evaluation efforts for energy efficiency and to provide a means of accounting for real-world driving performance within combination truck research and analyses. Some general statistics, including distribution of idling times during long-haul trucking operations, are presented. However, the main focus is on the analysis of some of the extensive real-world information collected in this project, specifically on the assessment of the effect that different types of tires [i.e., dual tires versus new generation wide-based single tires (NGWBSTs)] have on the fuel efficiency of Class 8 trucks. The tire effect is also evaluated as a function of the vehicle load level. In all cases analyzed, the statistical tests strongly suggest that fuel efficiencies achieved when all NGWBSTs or combinations of duals and NGWBSTs are used are higher than in the case of a truck equipped with all dual tires. The results show that the fuel efficiency improvement increases as the number of NGWBSTs on the truck increases, with observed improvements of around 6% when either the tractor or the trailer was equipped with NGWBSTs and more than 9% when both were mounted with these types of tires.

The Virtual Human will be a research/simulation environment having an integrated system ofbiophysical models, data, and advanced computational algorithms. It will have a Web-based interface for easy, rapid access from several points of entry. The Virtual Human will serve as a platform for national and international users from governments, academia and industry to investigate the widest range of human biological and physical responses to stimuli, be they biological, chemical, or physical. This effort will go far beyond the modeling of anatomy to incorporate refmed computational models of whole-body processes, using mechanical and electrical tissue properties, and biology from physiology to biochemical information. The platform will respond mechanistically to varied and potentially iterative stimuli that can be visualized multi-dimensionally. This effort is in the formative stage of a several-year process that will lead to a program that is of similar proportion to the Human Genome, but will be much more computationally intensive.The main purpose of this paper is to communicate our early ideas about the philosophic basis of the program, to identify some of the applications for which the Virtual Human would be used, to elicit comments, and to provide a basis to identify prospective collaborators.

A stochastic computer model for simulating the actions r.nd be'.iavior of nuclear Dover plant maintenance personnal is described. The model considers oersonnel. environmental, and motivational variables to yield predictions of maintenance performance quality and ti.^ie to perform. The model has been fully developed and sensitivity tested. Additional evaluation of the model is now taking place.

Many of the components associated with the deployment of Intelligent Transportation Systems (ITS) to support a traffic management center (TMC) such as remote control cameras, traffic speed detectors, and variable message signs, have been available for many years. Their deployment, however, has been expensive and applied primarily to freeways and interstates, and have been deployed principally in the major metropolitan areas in the US; not smaller cities.The Knoxville (Tennessee) Transportation Planning Organization is sponsoring a project that will test the integration of several technologies to estimate near-real time traffic information data and information that could eventually be used by travelers to make better and more informed decisions related to their travel needs. The uniqueness of this demonstration is that it will seek to predict traffic conditions based on cellular phone signals already being collected by cellular communications companies. Information about the average speed on various portions of local arterials and incident identification (incident location) will be collected and compared to similar data generated by "probe vehicles". Successful validation of the speed information generated from cell phone data will allow traffic data to be generated much more economically and utilize technologies that are minimally infrastructure invasive. Furthermore, when validated, traffic information could be provided to the traveling public allowing then to make better decisions about trips. More efficient trip planning and execution can reduce congestion and associated vehicle emissions. This paper will discuss the technologies, the demonstration project, the project details, and future directions.

Human operator simulation models can play an important information role in the allocation of functions in person-machine systems. A prototype simulation model system developed at ORNL is described in which a human operator model (INTEROPS) and a nuclear power plant (NPP) process model are dynamically integrated. INTEROPS is a cognitive/ performance simulation model which is itself a dynamic integration of a SAINT task network model and a knowledge-based subsystem which reasons with uncertainty. Potential contributions of INTEROPS to NPP advanced control design are evaluated.

This paper presents an architecture which combines artificial neural networks (ANNs) and an expert system (ES) into a hybrid, self-improving artificial intelligence (AI) system. The purpose of this project is to explore methods of combining multiple AI technologies into a hybrid intelligent diagnostic and advisory system. ANNs and ESs have different strengths and weaknesses, which can be exPloited in such a way that they are complementary to each other: strengths in one system make up for weaknesses in the other, and vice versa. There is, presently, considerable interest in ways to exploit the strengths of these methodologies to produce an intelligent system which is more robust and flexible than one using either technology alone. Any process which involves both data-driven (bottom-up) and conceptdriven (top-down) processing is especially well suited to displaying the capabilities of such a hybrid system. The system can take an incoming pattern of signals, as from various points in an automated manufacturing process, and make intelligent process control decisions on the basis of the pattern as preprocessed by the ANNs, with rule-based heuristic help or corroboration from the ES. Patterns of data from the environment which can be classified by either the ES or a human consultant can result in a high-level ANN being created and trained to recognizet/aat paitem on faJture-occurrences, in subsequent cases in which the ANN and the ES fail to agree on a decision concerning the environmental situation, the system can resolve those differences and retrain the networks and/or modify the models of the environment stored in the ES, Work on a hybrid system for perception in machine vision has been funded initially by an Oak Ridge National Lab0ratoryseed grant, and most of the system components are operating presently in a parallel distributed computer environment.

FMCSA Supplementary NotesThis program was administered through the Federal Motor Carrier Safety Administration (FMCSA). The FMCSA Program Manager is Jeff Loftus. AbstractThe Federal Motor Carrier Safety Administration (FMCSA) funded this project to Determine the feasibility of gathering vehicle, driver and carrier data to be used to format and wirelessly transmit from a commercial motor vehicle a safety data message set. The results of this effort will be used in the decision to move forward to conduct a pilot test.