Vehicle Motion Data Research Articles

Determining Turbulent Vertical Velocity, and Fluxes of Heat and Salt with an Autonomous Underwater Vehicle

The authors show that vertical turbulent fluxes in the upper ocean can be measured directly with an autonomous underwater vehicle (AUV). A horizontal profile of vertical water velocity is obtained by applying a Kalman smoother to AUV motion data. The smoother uses a linearized model for vehicle motion and vehicle data such as depth, pitch, and pitch rate to produce an optimal estimate of the state of the system, which includes other vehicle variables and the vertical water velocity. Vertical water velocity estimated by applying the smoother to data from the autonomous microconductivity temperature vehicle (AMTV) is accurate at horizontal scales from three to several hundred meters, encompassing the energy-containing scales of most oceanic turbulence. The zero-lag covariances between vertical water velocity and concurrent measurements of temperature or salinity represent the heat and salt fluxes, respectively. The authors have measured horizontal profiles of turbulent fluxes with two different AUVs in three separate polar ocean experiments using this technique. Flux magnitudes and directions are reasonable and in general agreement with fixed turbulence sensors. With this technique, one can gather boundary layer data in inaccessible regions without disturbing or affecting the surface.

Results of Car Following Analyses Using Global Positioning System

One of the key elements in the evolution of traffic flow theory has been the development of the car following theory. Applications of this research can be seen in traffic simulation models, such as CORSIM, that use car following models to simulate traffic flow and predict operational performance characteristics on arterial roadways. Researchers at Louisiana State University have recently developed a technique using the Global Positioning System to record and analyze car following behavior. In this study, these procedures were used to collect vehicle motion data and compare aspects of driver behavior recorded in the field to those predicted by the General Motors car following models. These comparisons were also used to develop numeric values of driver sensitivity and time lag from field data.

The Synthetic Battlebridge: a tool for large-scale VEs

The Synthetic BattleBridge gives users interface tools that help them navigate, analyze, and comprehend a complex, active, distributed virtual battlespace environment. A primary objective for the SBB project is to develop an observatory for real time monitoring and assessment of the activities of intelligently behaving autonomous actors and manned simulators within a VE. Therefore, the SBB uses both environment and computation distribution to let users monitor and assess in real time the activity within a VE and to provide cognitive support for situation analysis. The SBB also addresses the development and evaluation of advanced user interfaces, information aggregation techniques, and information presentation techniques. For the SBB to function as we require, it must present to the user the spatial orientation, type, motion, and distribution of actors in a VE. Key issues regarding these capabilities are updating and displaying the VE at interactive display rates and providing a very large scale environment containing a wide variety of actor types, sizes, and speeds, The SBB provides these capabilities by computing vehicle position, motion, and velocity data for all actors in the battlespace and presenting this information in real time using a 3D rendering of the battlespace and its contents. The user controls the SBB and information presentation with an interface consisting of a combination of visual icons and text, which we describe.