Generating Summaries from FOT Data ITS World Congress, Detroit 2014 Dr. Sami Koskinen, VTT

project start date: | end date: 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

project start date: | end date: 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

project start date: | end date: 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

project start date: | end date: 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 can instantly generate reports from the summary tables, e.g

project start date: | end date: 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

project start date: | end date: Processing Steps

project start date: | end date: 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!