Managing Big Data for Addressing Research Questions in a Collaborative Project on Automated Driving Impact Assessment
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/23/6773 |
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| author | Francesco Bellotti Nisrine Osman Eduardo H. Arnold Sajjad Mozaffari Satu Innamaa Tyron Louw Guilhermina Torrao Hendrik Weber Johannes Hiller Alessandro De Gloria Mehrdad Dianati Riccardo Berta |
| author_facet | Francesco Bellotti Nisrine Osman Eduardo H. Arnold Sajjad Mozaffari Satu Innamaa Tyron Louw Guilhermina Torrao Hendrik Weber Johannes Hiller Alessandro De Gloria Mehrdad Dianati Riccardo Berta |
| author_sort | Francesco Bellotti |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T14:27:46Z |
| format | Article |
| id | doaj.art-0209e9d42bce45b2a17954601c7077d3 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T14:27:46Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-0209e9d42bce45b2a17954601c7077d32023-11-20T22:49:08ZengMDPI AGSensors1424-82202020-11-012023677310.3390/s20236773Managing Big Data for Addressing Research Questions in a Collaborative Project on Automated Driving Impact AssessmentFrancesco Bellotti0Nisrine Osman1Eduardo H. Arnold2Sajjad Mozaffari3Satu Innamaa4Tyron Louw5Guilhermina Torrao6Hendrik Weber7Johannes Hiller8Alessandro De Gloria9Mehrdad Dianati10Riccardo Berta11Department of Electrical, Electronics and Telecommunication Engineering and Naval Architecture (DITEN), University of Genova, 16145 Genova, ItalyDepartment of Electrical, Electronics and Telecommunication Engineering and Naval Architecture (DITEN), University of Genova, 16145 Genova, ItalyWarwick Manufacturing Group (WMG), University of Warwick, Coventry CV4 7AL, UKWarwick Manufacturing Group (WMG), University of Warwick, Coventry CV4 7AL, UKVTT Technical Research Centre of Finland Ltd., P.O. Box 1000, FI-02044 VTT Espoo, FinlandInstitute for Transport Studies, University Road, University of Leeds, Leeds LS2 9JT, UKVTT Technical Research Centre of Finland Ltd., P.O. Box 1000, FI-02044 VTT Espoo, FinlandInstitute for Automotive Engineering, RWTH Aachen University, Steinbachstr 7, 52074 Aachen, GermanyInstitute for Automotive Engineering, RWTH Aachen University, Steinbachstr 7, 52074 Aachen, GermanyDepartment of Electrical, Electronics and Telecommunication Engineering and Naval Architecture (DITEN), University of Genova, 16145 Genova, ItalyWarwick Manufacturing Group (WMG), University of Warwick, Coventry CV4 7AL, UKDepartment of Electrical, Electronics and Telecommunication Engineering and Naval Architecture (DITEN), University of Genova, 16145 Genova, ItalyWhile 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.https://www.mdpi.com/1424-8220/20/23/6773research data collection and sharingconnected and automated drivingdeployment and field testingvehicular sensorsimpact assessmentknowledge management |
| spellingShingle | Francesco Bellotti Nisrine Osman Eduardo H. Arnold Sajjad Mozaffari Satu Innamaa Tyron Louw Guilhermina Torrao Hendrik Weber Johannes Hiller Alessandro De Gloria Mehrdad Dianati Riccardo Berta Managing Big Data for Addressing Research Questions in a Collaborative Project on Automated Driving Impact Assessment Sensors research data collection and sharing connected and automated driving deployment and field testing vehicular sensors impact assessment knowledge management |
| title | Managing Big Data for Addressing Research Questions in a Collaborative Project on Automated Driving Impact Assessment |
| title_full | Managing Big Data for Addressing Research Questions in a Collaborative Project on Automated Driving Impact Assessment |
| title_fullStr | Managing Big Data for Addressing Research Questions in a Collaborative Project on Automated Driving Impact Assessment |
| title_full_unstemmed | Managing Big Data for Addressing Research Questions in a Collaborative Project on Automated Driving Impact Assessment |
| title_short | Managing Big Data for Addressing Research Questions in a Collaborative Project on Automated Driving Impact Assessment |
| title_sort | managing big data for addressing research questions in a collaborative project on automated driving impact assessment |
| topic | research data collection and sharing connected and automated driving deployment and field testing vehicular sensors impact assessment knowledge management |
| url | https://www.mdpi.com/1424-8220/20/23/6773 |
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