Generating Summaries from FOT Data
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Generating Summaries from FOT Data ITS World Congress, Detroit 2014 Dr. Sami Koskinen, VTT [email protected] Data Processing in DRIVE C2X and TeleFOT. Field Operational Tests (FOTs) are large-scale user tests which aim at comprehensive assessment as well as promotion of latest functions

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Generating Summaries from FOT Data

ITS World Congress, Detroit 2014

Dr. Sami Koskinen, VTT

[email protected]


Data processing in drive c2x and telefot
Data Processing in DRIVE C2X and TeleFOT

  • Field Operational Tests (FOTs) are large-scale user tests which aim at comprehensive assessment as well as promotion of latest functions

  • FOTs collect detailed data for assessment, commonly in the range of terabytes. Data comes from various sensors, traffic and weather information systems, communication, functions etc.

  • This presentation covers data processing approaches used in EU projects DRIVE C2X and TeleFOT, where multiple FOTs were carried out

    • Data was shared between partners, documented in detail and analysed collaboratively across test sites.

    • Analyses concerned safety, traffic efficiency, environment, user acceptance and technical performance


Motivation for generating summaries
Motivation for Generating Summaries

  • In FOTs, the amount of collected data is generally too large for the test to be comprehensively analyzed without first generating summaries.

    • Data can be split over multiple hard drives and each simple calculation may take a week to complete.

  • Driving diaries and event lists are common summary tables

  • When several analysts collaboratively work with FOT data, same post-processing and set of indicators make their work more efficient

  • A couple of professional programmers can implement hundreds of indicators based on analysts’ requirements

  • Summarized content minimally gives an index into raw data.


Harmonisation across tests
Harmonisation Across Tests

  • Harmonised map matching and calculation of indicators and summary tables reduces individual analyst’s work and also ensures comparability of indicators across tests

  • Post-processing handles different log file formats, function trigger and communication definitions, lists of broken loggers and important dates such as changing to winter-time speed limits

  • As a result of post-processing, analysts get similar summary data sets from each FOT

    • Variables derived from logger data are calculated using the same definition

    • Shared post-processing also helps to avoid some coding errors that would happen if each test site / analyst works separately


Main summary tables legs
Main Summary Tables - Legs

  • Driving diaries, where each leg is described by a long list of derived variables such as time stamps, total distance driven, number of hard braking events, fuel consumption and percent driven on a road type

  • More than 200 derived variables / indicators for statistical analyses

  • One caninstantlygeneratereportsfrom the summarytables, e.g.


Main summary tables events
Main Summary Tables - Events

  • Table rows describing events, e.g. periods of HMI activity: coordinates, information shown, speed at the beginning and end

  • Can also be lists of e.g. speeding events

  • Enables selection for analysis

GPS Visualizer & Google



Conclusions
Conclusions

  • Harmonizing logging and post-processing enables analysts to more easily cover several tests in collaborative projects.

  • Summarized content minimally gives an index into raw data. In the best case, analysts work together with professional programmers, providing them the data for further analyses.

  • Summary tables are of manageable size whereas the size of raw FOT datasets often causes practical problems

  • Enriching and combining different data is beneficial: map data linked with coordinates, events linked with manual video annotations and driving diaries with user and vehicle data.

  • Data documentation and sharing are the keys for comprehensive analysis!


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