As industrial research in automated driving is rapidly advancing, it is of paramount importance to
analyze field data from extensive road tests. This thesis presents a research work done in L3Pilot,
the first comprehensive test of automated driving functions (ADFs) on public roads in Europe.
L3Pilot is now completing the test of ADFs in vehicles by 13 companies. The tested functions are
mainly of Society of Automotive Engineers (SAE) automation level 3, some of level 4. The overall
collaboration among several organizations led to the design and development of a toolchain aimed
at processing and managing experimental data sharable among all the vehicle manufacturers to
answer a set of 100+ research questions (RQs) about the evaluation of ADFs at various levels,
from technical system functioning to overall impact assessment. The toolchain was designed to
support a coherent, robust workflow based on Field opErational teSt supporT Action (FESTA), a
well-established reference methodology for automotive piloting. Key challenges included ensuring
methodological soundness and data validity while protecting the vehicle manufacturers\u2019
intellectual property. Through this toolchain, the project set up what could become a reference
architecture for managing research data in automated vehicle tests. In the first step of the workflow,
the methodology partners captured the quantitative requirements of each RQ in terms of the
relevant data needed from the tests. L3Pilot did not intend to share the original vehicular signal
timeseries, both for confidentiality reasons and for the enormous amount of data that would have
been shared. As the factual basis for quantitatively answering the RQs, a set of performance
indicators (PIs) was defined. The source vehicular signals were translated from their proprietary
format into the common data format (CDF), which was defined by L3Pilot to support efficient
processing through multiple partners\u2019 tools, and data quality checking. The subsequent vi
performance indicator (PI) computation step consists in synthesizing the vehicular time series into
statistical syntheses to be stored in the project-shared database, namely the Consolidated Database
(CDB). Computation of the PIs is segmented based on experimental condition, road type and
driving scenarios, as required to answer the RQs. The supported analysis concerns both objective
data, from vehicular sensors, and subjective data from user (test drivers and passengers)
questionnaires. The overall L3Pilot toolchain allowed setting up a data management process
involving several partners (vehicle manufacturers, research institutions, suppliers, and developers),
with different perspectives and requirements. The system was deployed and used by all the relevant
partners in the pilot sites. The experience highlights the importance of the reference methodology
to theoretically inform and coherently manage all the steps of the project and the need for effective
and efficient tools, to support the everyday work of all the involved research teams, from vehicle
manufacturers to data analysts
