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Francesco Bellotti1, Nisrine Osman1, Eduardo H Arnold2

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Summary

This study introduces a robust workflow for handling sensitive big data from multiple partners in automated driving impact assessments. It ensures data validity and protects intellectual property while answering key research questions.

Keywords:
collaborative project methodologyconnected and automated drivingdeployment and field testingimpact assessmentknowledge managementresearch data collection and sharingvehicular sensors

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Area of Science:

  • Automotive Engineering
  • Data Science
  • Big Data Analytics

Background:

  • Extracting meaningful insights from big data is crucial, yet challenges exist in managing sensitive data shared by diverse project partners.
  • Literature gaps are identified regarding collaborative handling of sensitive data for collective research question (RQ) answering, particularly for automated driving technologies.
  • Assessing the impact of new automated driving technologies necessitates robust data management strategies across multiple stakeholders.

Purpose of the Study:

  • To present a developed workflow for handling sensitive big data from multiple partners in automated driving impact assessment.
  • To address challenges in ensuring methodological soundness and data validity while protecting intellectual property.
  • To share experiences from a large-scale, multi-partner project on advanced automated driving functions.

Main Methods:

  • Application of an established reference piloting methodology to develop a coherent and robust workflow.
  • Quantitative requirement capture for each research question, specifying data needs from tests.
  • Implementation of a data management process involving multiple partners with varying perspectives and requirements.
  • Deployment of an integrated system within the project's big data toolchain for partner accessibility.

Main Results:

  • A functional workflow was developed and deployed, integrating data from vehicular sensors and questionnaires.
  • The process facilitated collaboration among 34 partners across 10 European countries, including vehicle manufacturers, research institutions, suppliers, and developers.
  • The workflow successfully managed diverse data types and partner requirements while maintaining data integrity.

Conclusions:

  • A reference methodology is essential for theoretically informing and coherently managing complex, multi-partner projects.
  • Effective and efficient tools are critical for supporting the daily work of diverse research teams, from vehicle manufacturers to data analysts.
  • The presented workflow provides a scalable solution for collaborative big data analysis in the field of automated driving impact assessment.