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Enhanced Reliability of ADAS Sensors Based on the Observation of the Power Supply Current and Neural Network Application

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Computational Collective Intelligence (ICCCI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10449))

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Abstract

Advanced Driver Assistance Systems (ADAS) are essential parts for developing the autonomous vehicle concept. They cooperate with different on-board car equipment to make driving safe and comfortable. There are many ways to monitor their behaviour and assess their reliability. The presented solution combines the versatility of applications (it can be used with almost any kind of sensors), low cost (data acquisition using this method requires only a simple electronic circuit) and requires no adjustments of the sensor’s software or hardware. Using this type of analysis, one can determine the device’s family, find any over- and under-voltages that can damage the sensor or even detect two-way CAN communication malfunctions. Since the data acquired is complex (and can be troublesome during processing) – one of the best solutions is to cope with the problem by using a variety of neural networks.

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Acknowledgements

This work was supported by the European Union from the FP7-PEOPLE-2013-IAPP AutoUniMo project “Automotive Production Engineering Unified Perspective based on Data Mining Methods and Virtual Factory Model” (Grant Agreement No. 612207) and research work financed from the funds for science in the years 2016–2017, which are allocated to an international co-financed project (Grant Agreement No. 3491/7.PR/15/2016/2).

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Authors and Affiliations

  1. Electronics and Computer Science, Faculty of Automation Control, Silesian University of Technology, Gliwice, Poland

    Damian Grzechca, Adam Ziębiński & Paweł Rybka

Authors
  1. Damian Grzechca
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  2. Adam Ziębiński
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  3. Paweł Rybka
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Corresponding author

Correspondence to Damian Grzechca .

Editor information

Editors and Affiliations

  1. Department of Information Systems, Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Department of Computer Science, University of Cyprus, Nicosia, Cyprus

    George A. Papadopoulos

  3. Department of Information Systems, Gdynia Maritime University, Gdynia, Poland

    Piotr Jędrzejowicz

  4. Department of Information Systems, Faculty of Computer Science and Management, Wrocław University of Science and Technology, Wrocław, Poland

    Bogdan Trawiński

  5. Department of Information Systems, University of Münster, Münster, Germany

    Gottfried Vossen

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Grzechca, D., Ziębiński, A., Rybka, P. (2017). Enhanced Reliability of ADAS Sensors Based on the Observation of the Power Supply Current and Neural Network Application. In: Nguyen, N., Papadopoulos, G., Jędrzejowicz, P., Trawiński, B., Vossen, G. (eds) Computational Collective Intelligence. ICCCI 2017. Lecture Notes in Computer Science(), vol 10449. Springer, Cham. https://doi.org/10.1007/978-3-319-67077-5_21

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  • DOI: https://doi.org/10.1007/978-3-319-67077-5_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67076-8

  • Online ISBN: 978-3-319-67077-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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