Abstract
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.
Highlights
Solving grand challenges, such as automated connected driving, often requires collaboration across multiple domains and technical areas
A wide range of research questions (RQs) was created, not limiting them by means of any single for the ADF compared to manual driving.”
An extensive use of abstractions, in order to support functional extensibility and module/code reusability the modular approach depicted in Figure 3 for extracting performance indicators (PIs) from signal time series revealed itself very useful to deal with a set of specification upgrades, that occurred during the project the development of a tool that computes the PIs from the raw data and makes them ready for sharing the possibility of postediting the PIs before inserting them in the shared database the definition of a tool for efficiently uploading files to the database
Summary
Solving grand challenges, such as automated connected driving, often requires collaboration across multiple domains and technical areas. The literature is rich in guidelines, techniques and tools for general project challenges (e.g., [1,2]), but there is a lack of specific information and tools for different partners to deal with sensitive data in order to answer a set of research questions (RQs) at project level. A key novelty in this process is the use of the Consolidated Database (CDB), which allows data from all the pilot sites to be shared anonymously and securely amongst project partners to facilitate data analysis aimed at answering the project’s RQs. This paper presents the challenges we have faced in implementing the methodology and developing a coherent, robust workflow.
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