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Article

Managing Big Data for Addressing Research Questions in a Collaborative Project on Automated Driving Impact Assessment

1
Department of Electrical, Electronics and Telecommunication Engineering and Naval Architecture (DITEN), University of Genova, 16145 Genova, Italy
2
Warwick Manufacturing Group (WMG), University of Warwick, Coventry CV4 7AL, UK
3
VTT Technical Research Centre of Finland Ltd., P.O. Box 1000, FI-02044 VTT Espoo, Finland
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Institute for Transport Studies, University Road, University of Leeds, Leeds LS2 9JT, UK
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Institute for Automotive Engineering, RWTH Aachen University, Steinbachstr 7, 52074 Aachen, Germany
*
Author to whom correspondence should be addressed.
Sensors 2020, 20(23), 6773; https://doi.org/10.3390/s20236773
Received: 28 September 2020 / Revised: 17 November 2020 / Accepted: 19 November 2020 / Published: 27 November 2020
While extracting meaningful information from big data is getting relevance, literature lacks information on how to handle sensitive data by different project partners in order to collectively answer research questions (RQs), especially on impact assessment of new automated driving technologies. This paper presents the application of an established reference piloting methodology and the consequent development of a coherent, robust workflow. Key challenges include ensuring methodological soundness and data validity while protecting partners’ intellectual property. The authors draw on their experiences in a 34-partner project aimed at assessing the impact of advanced automated driving functions, across 10 European countries. In the first step of the workflow, we captured the quantitative requirements of each RQ in terms of the relevant data needed from the tests. Most of the data come from vehicular sensors, but subjective data from questionnaires are processed as well. Next, we set up a data management process involving several partners (vehicle manufacturers, research institutions, suppliers and developers), with different perspectives and requirements. Finally, we deployed the system so that it is fully integrated within the project big data toolchain and usable by all the partners. Based on our experience, we highlight 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, in order to support the everyday work of all the involved research teams, from vehicle manufacturers to data analysts. View Full-Text
Keywords: research data collection and sharing; connected and automated driving; deployment and field testing; vehicular sensors; impact assessment; knowledge management; collaborative project methodology research data collection and sharing; connected and automated driving; deployment and field testing; vehicular sensors; impact assessment; knowledge management; collaborative project methodology
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MDPI and ACS Style

Bellotti, F.; Osman, N.; Arnold, E.H.; Mozaffari, S.; Innamaa, S.; Louw, T.; Torrao, G.; Weber, H.; Hiller, J.; De Gloria, A.; Dianati, M.; Berta, R. Managing Big Data for Addressing Research Questions in a Collaborative Project on Automated Driving Impact Assessment. Sensors 2020, 20, 6773. https://doi.org/10.3390/s20236773

AMA Style

Bellotti F, Osman N, Arnold EH, Mozaffari S, Innamaa S, Louw T, Torrao G, Weber H, Hiller J, De Gloria A, Dianati M, Berta R. Managing Big Data for Addressing Research Questions in a Collaborative Project on Automated Driving Impact Assessment. Sensors. 2020; 20(23):6773. https://doi.org/10.3390/s20236773

Chicago/Turabian Style

Bellotti, Francesco, Nisrine Osman, Eduardo H. Arnold, Sajjad Mozaffari, Satu Innamaa, Tyron Louw, Guilhermina Torrao, Hendrik Weber, Johannes Hiller, Alessandro De Gloria, Mehrdad Dianati, and Riccardo Berta. 2020. "Managing Big Data for Addressing Research Questions in a Collaborative Project on Automated Driving Impact Assessment" Sensors 20, no. 23: 6773. https://doi.org/10.3390/s20236773

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