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- Bärgman, Jonas, 1972, et al.
(författare)
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The UDrive dataset and key analysis results
- 2017
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- UDrive is a large European naturalistic driving study, sponsored by the European Commission (FP7).Nineteen partners across Europe have come together and, along with stakeholders, defined researchquestions, developed data acquisition, collected and managed data, and finally, performed a first analysis onthe UDrive dataset with respect to driver/rider behaviour related to traffic safety and the environment (ecodriving).This document presents key results of the UDrive analysis performed in UDrive Sub-project 4: Data analysis.It also describes the UDrive dataset and, in brief, how we got here.
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- Barnard, Yvonne, et al.
(författare)
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Data management and data sharing in field operational tests
- 2016
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Ingår i: Intelligent Transportation Systems: From Good Practices to Standards. - : CRC Press. - 9781498721875 ; , s. 59-72
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Bokkapitel (övrigt vetenskapligt/konstnärligt)abstract
- In this chapter it will be discussed how data from Field Operational Tests of Intelligent Transport Systems can be managed and shared. The Field Operational Tests, where hundreds of users get to experience the latest systems, aim to assess the impacts that would result from a wide-scale implementation. Evaluation principles of Field Operational Tests will be explained, and a closer look will be taken at the data that is collected for carrying out the assessments. The widely used FESTA methodology for designing and conducting Field Operational Tests and Naturalistic Driving Studies already provides several recommendations for managing data. This methodology will be discussed and illustrated by examples of its use in European projects. As field test projects set out to collect a huge set of data, the projects themselves do not usually have the scope or the resources to analyze the data from every perspective. Therefore re-use of the collected data also by other projects with different research questions has the potential to generate a wealth of new knowledge about what is happening in the interactions between drivers, vehicles and the infrastructure. Data sharing is the focus of a European support action, FOT-Net Data. The support action is working, with international collaboration, to form a data sharing framework, a data catalogue, and provide detailed recommendations for sharing and re-use. Outcomes from this activity will be discussed. Ways of sharing different types of data will be described, including the necessary steps to be taken to open up the data.
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| 4. |
- Gellerman, Helena, 1960, et al.
(författare)
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Data sharing of transport research data
- 2016
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Ingår i: Transportation Research Procedia. - : Elsevier BV. - 2352-1465 .- 2352-1457. ; 14, s. 2227-2236
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Konferensbidrag (refereegranskat)abstract
- With the rapid progress of the development of intelligent transport systems over the last 15 years, the need for testing them in the real world and collecting data about their impact became more and more important. We have seen a fast growth in the number of Field Operational Tests (FOT) and Naturalistic Driving Studies (NDS) performed worldwide. The need to better understand the benefits of safety systems and the factors behind the occurrence of incidents and accidents have been a main driving force and the data has therefore been collected through naturalistic driving by volunteer drivers. As the number of different datasets has increased and so also the awareness of the substantial effort and funding needed to run these FOT/NDS, the interest in data sharing has increased worldwide. The availability of a common Data Sharing Framework (DSF) could highly facilitate a larger use of the collected FOT/NDS data. The FOT-Net Data project has developed such a framework, in collaboration with a variety of stakeholders from Europe, the US, Japan and other countries. The seven topics addressed by the DSF are (1) project agreements, (2) data and metadata descriptions, (3) data protection, (4) training, (5) support and research services, (6) financial models and (7) applications procedures. Many of the topics are general and can be used for other types of transport research data as well. There remain challenges to make data sharing possible on a global scale. Some of these are: the project funding schemes, leading to multiple schemas of ownerships of data, and the legal settings in different countries. On a technical level, the documentation of datasets and of the metadata describing the test is not always sufficient. Furthermore, new projects need to be made aware of the importance of inserting the pre-requisites for data sharing into the different project agreements right from the start. This paper describes the content of the DSF with its hands-on recommendations on how to prepare for and perform data sharing of transport research data. It also presents the status of a use case, implementing the DSF into the European project UDRIVE.
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| 5. |
- Hiller, Johannes, et al.
(författare)
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The L3Pilot Data Management Toolchain for a Level 3 Vehicle Automation Pilot
- 2020
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Ingår i: Electronics (Switzerland). - : MDPI AG. - 2079-9292. ; 9:5
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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