Information and Communication Support for Automotive Testing and Validation

Johanson, Mathias

doi:10.5772/12964

Cited by 2 publications

(3 citation statements)

References 19 publications

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“…These are products that buyers generally consider investment goods. Quite a number of sources [13][14][15][16][17][18][19][20][21][22][23][24] 1 report on data collection from automobiles. Other sources predominantly report on B2B applications in aircraft [5,18,25], military equipment [26], industrial equipment [27][28][29][30][31], and infrastructure such as bridges [32,33], street lights [34] or elevators [35].…”

Section: Characterization Of Products That Have Been Deployed To Collmentioning

confidence: 99%

“…The ODB in automobiles physically manifests in the form of the ODB-II connector which is connected to the Controller Area Network (CAN) bus. The CAN bus is in turn responsible for the communication between the electronic control units (ECUs) of the car [15]. Diagnostic trouble codes (DTCs) from ECUs are routinely being read out during service from customer vehicles using a wired connection.…”

Section: Technologies Infrastructures and Plat-forms To Support Utilmentioning

confidence: 99%

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Taking Advantage of Data Generated by Products: Trends, Opportunities and Challenges

Vegte

Frederik²

2016

Volume 1B: 36th Computers and Information in Engineering Conference

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Now that all kinds of products are increasingly getting connected to the Internet, it is expected that it will become easier to collect data on how they are actually used during the middle-of-life stage of their product lifecycles. At the same time, a growing number of data analytics technologies offers opportunities to transform this data into actionable knowledge. Over the years, such knowledge extracted from usage data has already become a reliable input for managing maintenance and related services, but other uses such as feedback to design — where product data management systems have started to offer support for data collection practices — and providing advice to end users are now also being considered. Most data from sensors and other product-embedded information devices are collected in batches and analyzed retrospectively. In order for companies to further benefit from data collection in terms of efficacy and acceptance in society, two key challenges are (i) finding ways to effectively use data analytics techniques — which currently do not seem to be used to their full potential, and (ii) finding a good trade-off between respecting privacy and yet producing useful knowledge.

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Section: Characterization Of Products That Have Been Deployed To Collmentioning

confidence: 99%

Section: Technologies Infrastructures and Plat-forms To Support Utilmentioning

confidence: 99%

Taking Advantage of Data Generated by Products: Trends, Opportunities and Challenges

Vegte

Frederik²

2016

Volume 1B: 36th Computers and Information in Engineering Conference

View full text Add to dashboard Cite

show abstract

“…In the testing, verification and validation phases of automotive product development, large volumes of measurement data are being gathered from fleets of connected test vehicles [1,2]. With the advent of telematics systems and improved means of wireless vehicular communication more or less ubiquitously, the opportunities to capture and collect data has improved tremendously over the past few years.…”

Section: Introductionmentioning

confidence: 99%

Big Automotive Data: Leveraging large volumes of data for knowledge-driven product development

Johanson

Belenki

Jalminger

et al. 2014

2014 IEEE International Conference on Big Data (Big Data)

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To be successful in the increasingly competitive consumer vehicle market, automotive manufacturers must be highly responsive to customer needs and market trends, while responding to the challenges of climate change and sustainable development. One key to achieving this is to promote knowledge-driven product development through large scale collection of data from connected vehicles, to capture customer needs and to gather performance data, diagnostic data and statistics. Since the volume of data collected from fleets of vehicles using telematics services can be very high, it is important to design the systems and frameworks in a way that is highly scalable and efficient. This can be described as a Big Data challenge in an automotive context. In this paper, we explore the opportunities of leveraging Big Automotive Data for knowledge driven product development, and we present a technological framework for capture and online analysis of data from connected vehicles.

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Information and Communication Support for Automotive Testing and Validation

Cited by 2 publications

References 19 publications

Taking Advantage of Data Generated by Products: Trends, Opportunities and Challenges

Taking Advantage of Data Generated by Products: Trends, Opportunities and Challenges

Big Automotive Data: Leveraging large volumes of data for knowledge-driven product development

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