Abstract

DOE contracted with Sandia to install a radar acquisition system (RAMS) to gather aircraft flight data near the Pantex Plant in Amarillo, TX. To support this effort, data reduction tools were needed to help analyze the radar data. Plot-flight is one of several data reduction tools that comprise the Sandia Airspace Recording System (SARS). The radar data is needed to support the Pantex Environmental Impact Study. Plot-flight is a DOS-based plot program that allows analysts to replay pre-recorded air traffic over Albuquerque and Amarillo. The program is flexible enough to permit replay of daily flights either sequentially, by range, or by Beacon ID. In addition to replay, the program is setup for data entry. Analysts can correlate electronic aircraft flight data to the green strip flights logs obtained from the local air traffic control center. The green strips are used by air traffic controllers to record each scheduled flight. The green strips have information not available electronically such as aircraft type and aircraft ID. This type of information is necessary to accommodate the current models used in aircraft crash analysis. Plot-flight correlates the hand-written information from the green strips to the recorded aircraft flight. Document type: Report

Abstract

A variety of deep learning techniques are actively employed for advanced driver assistance systems, which in turn require gathering lots of heterogeneous driving data, such as traffic conditions, driver behavior, vehicle status and location information. However, these different types of driving data easily become more than tens of GB per day, forming a significant hurdle due to the storage and network cost. To address this problem, this paper proposes a novel scheme, called CoDR, which can reduce data volume by considering the correlations among heterogeneous driving data. Among heterogeneous datasets, CoDR first chooses one set as a pivot data. Then, according to the objective of data collection, it identifies data ranges relevant to the objective from the pivot dataset. Finally, it investigates correlations among sets, and reduces data volume by eliminating irrelevant data from not only the pivot set but also other remaining datasets. CoDR gathers four heterogeneous driving datasets: two videos for front view and driver behavior, OBD-II and GPS data. We show that CoDR decreases data volume by up to 91%. We also present diverse analytical results that reveal the correlations among the four datasets, which can be exploited usefully for edge computing to reduce data volume on the spot. Document type: Article