Information Framework for Transportation Applications, 10-R8175

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Principal Investigator
Steven W. Dellenback, Ph.D., PMP

Inclusive Dates:  07/01/10 – 06/30/11

Background - In transportation, information can be derived from a number of sources. Consider the following examples:

  • A traditional manned vehicle 20 years ago: a driver relies on his/her vision and wits to determine the safest and most direct path to get between two points.

  • A traditional manned vehicle today: a driver has navigation systems, radio feeds, real-time traffic conditions on his/her PDA and/or Internet connection as well as potentially other sensors in their vehicle to assist in the safest and quickest path between two points.

  • An unmanned vehicle of several years ago: uses sensors to determine the "state" of the world around the vehicle and navigates between two points using pre-programmed "way points."

  • An unmanned vehicle of today: uses data from other vehicles and/or the roadside to more accurately determine the optimal path.

While the Global Positioning System (GPS) is important to locating a vehicle, there is a major push in the military world to have intelligent vehicles (whether manned or not) operate in "GPS-denied" environments; that is, use their surroundings to determine their location and not rely on specific GPS data. This project is developing algorithms and techniques to create a world view model to combine a number of data sources, both static and dynamic, to create a world model. Because of the expected huge amounts of data, an important component of the research is to have an algorithm that extracts regions of interest that are limited to what data is required by the application.

Approach - Sources of data (i.e., the proposed research is not creating this data) that will be placed into the model will include:

  • Data generated from processing information from sensors on vehicles.

  • Navigation data.

  • Traffic condition data available from Internet sources and radio broadcasts (available on a data feed in parallel with voice data).

  • Traffic management center data.

  • Landmarks such as buildings, stadiums, etc.

While the internal structure of the model will be based on a coordinate system, it is critical that the data can be extracted either in spatial (feature) or geolocation form.

Accomplishments - The program is still in the initial stages. To date, base algorithm development has been initiated.

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