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The U.S. Army uses the root mean square and power spectral density of elevation to characterize road/terrain (off-road) roughness for durability. This paper describes research aimed toward improving these metrics. The focus is on taking previously developed metrics and applying them to mathematically generated terrains to determine how each metric discerns the relative roughness of the terrains from a vehicle durability perspective. Multiple terrains for each roughness level were evaluated to determine the variability for each terrain rating metric. One method currently under consideration is running a relatively simple, yet vehicle class specific, model over a given terrain and using predicted vehicle response(s) to classify or characterize the terrain.

Standard visual database modeling practices in driving simulation reduce geometric complexity of terrain surfaces by using texture maps to simulate high frequency detail. Typically the vehicle dynamics model queries a correlated database that contains the polygons from the high level of detail of the visual database. However the vehicle dynamics database does not contain any of the high frequency information included in the texture maps. To overcome this issue and enhance both the visual and vehicle dynamics databases, a mathematical model of the high frequency content of the ground surface is developed using a set of Non-Uniform Rational B-Splines (NURBS) patches. The patches are combined in the terrain query by superimposing them over the low-frequency polygonal terrain, reintroducing the missing content. The patches are also used to generate Bump Map textures for the image generator so that the visual representation matches the terrain query.

The U.S. Army uses the root mean square and power spectral density of elevation to characterize road/terrain (off-road) roughness for durability. This paper describes research aimed toward improving these metrics. The focus is on taking previously developed metrics and applying them to mathematically generated terrains to determine how each metric discerns the relative roughness of the terrains from a vehicle durability perspective. Multiple terrains for each roughness level were evaluated to determine the variability for each terrain rating metric. One method currently under consideration is running a relatively simple, yet vehicle class specific, model over a given terrain and using predicted vehicle response(s) to classify or characterize the terrain.

The 21st Century Truck Initiative represents the premier partnership between government (Departments of Defense, Army, Energy, Transportation and the Environmental Protection Agency) and the U.S. trucking and supporting industries in seeking to develop and demonstrate commercially viable advanced technologies for trucks in the 21st century. At the request of senior leadership within the U.S. Departments of Defense and the Army, the Tank-automotive and Armaments Command's (TACOM) National Automotive Center (NAC), located at TACOM's Tank-Automotive Research, Development & Engineering Center (TARDEC), spearheaded the creation of this government-industry partnership to pursue the necessary leap-ahead technologies. By teaming the research and development efforts of government and industry, the partnership will improve fuel efficiency, increase safety, reduce owning and operating costs, and reduce emissions, while maintaining or enhancing the performance of military and commercial trucks.

This paper will describe the Army's Vehicle Intelligence Program and discuss some of the VI technologies being considered for use within the Army's Tactical Wheeled Vehicle fleet. It will describe some initial modeling efforts that focus on the fuel efficiency impacts of selected VI technologies and will suggest the impacts of an integrated and networked fleet with regard to logistics. Lastly, it will identify several areas of AVIP research that are being considered in the near term. All of these programs impact directly on the 21st Century (21T) Truck program. [1]