10 research outputs found
Big Data Management Towards Impact Assessment of Level 3 Automated Driving Functions
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
The L3Pilot Data Management Toolchain for a Level 3 Vehicle Automation Pilot
As industrial research in automated driving is rapidly advancing, it is of paramount importance to analyze field data from extensive road tests. This paper investigates the design and development of a toolchain to process and manage experimental data to answer a set of research questions about the evaluation of automated driving functions at various levels, from technical system functioning to overall impact assessment. We have faced this challenge in L3Pilot, the first comprehensive test of automated driving functions (ADFs) on public roads in Europe. L3Pilot is testing ADFs in vehicles made by 13 companies. The tested functions are mainly of Society of Automotive Engineers (SAE) automation level 3, some of them of level 4. In this context, the presented toolchain supports various confidentiality levels, and allows cross-vehicle owner seamless data management, with the efficient storage of data and their iterative processing with a variety of analysis and evaluation tools. Most of the toolchain modules have been developed to a prototype version in a desktop/cloud environment, exploiting state-of-the-art technology. This has allowed us to efficiently set up what could become a comprehensive edge-to-cloud reference architecture for managing data in automated vehicle tests. The project has been released as open source, the data format into which all vehicular signals, recorded in proprietary formats, were converted, in order to support efficient processing through multiple tools, scalability and data quality checking. We expect that this format should enhance research on automated driving testing, as it provides a shared framework for dealing with data from collection to analysis. We are confident that this format, and the information provided in this article, can represent a reference for the design of future architectures to implement in vehicles
Building a Data Management Toolchain for a Level 3 Vehicle Automation Pilot
L3Pilot is the first comprehensive test of ADFs with hands-off the wheel on public roads across Europe. L3Pilot will test ADFs in 100 cars with 1,000 drivers across 10 different countries in Europe. The tested functions will be mainly of SAE automation level 3, some of them of level 4. This paper describes the data management toolchain we have designed and developed in order to exploit pilot data for answering a set of research questions about evaluation of such aspects as: technical and traffic, user acceptance, impact, socioeconomic impact. The toolchain, supporting various confidentiality levels (prototype vehicle owner, consortium, public), has been designed to allow cross-vehicle owner data management, with efficient storage of data and its iterative processing with a variety of analysis and evaluation tools. Most of the tools in the data processing chain have been developed to a prototype version, tested in lab and are ready to be deployed for the pre-pilots in the various sites
Rare predicted loss-of-function variants of type I IFN immunity genes are associated with life-threatening COVID-19
BackgroundWe previously reported that impaired type I IFN activity, due to inborn errors of TLR3- and TLR7-dependent type I interferon (IFN) immunity or to autoantibodies against type I IFN, account for 15-20% of cases of life-threatening COVID-19 in unvaccinated patients. Therefore, the determinants of life-threatening COVID-19 remain to be identified in similar to 80% of cases.MethodsWe report here a genome-wide rare variant burden association analysis in 3269 unvaccinated patients with life-threatening COVID-19, and 1373 unvaccinated SARS-CoV-2-infected individuals without pneumonia. Among the 928 patients tested for autoantibodies against type I IFN, a quarter (234) were positive and were excluded.ResultsNo gene reached genome-wide significance. Under a recessive model, the most significant gene with at-risk variants was TLR7, with an OR of 27.68 (95%CI 1.5-528.7, P=1.1x10(-4)) for biochemically loss-of-function (bLOF) variants. We replicated the enrichment in rare predicted LOF (pLOF) variants at 13 influenza susceptibility loci involved in TLR3-dependent type I IFN immunity (OR=3.70[95%CI 1.3-8.2], P=2.1x10(-4)). This enrichment was further strengthened by (1) adding the recently reported TYK2 and TLR7 COVID-19 loci, particularly under a recessive model (OR=19.65[95%CI 2.1-2635.4], P=3.4x10(-3)), and (2) considering as pLOF branchpoint variants with potentially strong impacts on splicing among the 15 loci (OR=4.40[9%CI 2.3-8.4], P=7.7x10(-8)). Finally, the patients with pLOF/bLOF variants at these 15 loci were significantly younger (mean age [SD]=43.3 [20.3] years) than the other patients (56.0 [17.3] years; P=1.68x10(-5)).ConclusionsRare variants of TLR3- and TLR7-dependent type I IFN immunity genes can underlie life-threatening COVID-19, particularly with recessive inheritance, in patients under 60 years old