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Remote Diagnosis, Maintenance and Prognosis for Advanced Driver Assistance Systems Using Machine Learning Algorithms

Journal Article
2016-01-0076
ISSN: 1946-4614, e-ISSN: 1946-4622
DOI: https://doi.org/10.4271/2016-01-0076
Published April 05, 2016 by SAE International in United States
Remote Diagnosis, Maintenance and Prognosis for Advanced Driver Assistance Systems Using Machine Learning Algorithms
Sector:
Citation: Taie, M., Moawad, E., Diab, M., and ElHelw, M., "Remote Diagnosis, Maintenance and Prognosis for Advanced Driver Assistance Systems Using Machine Learning Algorithms," SAE Int. J. Passeng. Cars – Electron. Electr. Syst. 9(1):114-122, 2016, https://doi.org/10.4271/2016-01-0076.
Language: English

References

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  3. Tang , L. , Kacprzynski , G. J. , Goebel , K. , Saxena , A. et al. Prognostics-enhanced automated contingency management for advanced autonomous systems Prognostics and Health Management, 2008. PHM 2008. International Conference on 1 9 IEEE 2008
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  8. Taie , M. A. , Diab , M. , ElHelw , M. Remote prognosis, diagnosis and maintenance for automotive architecture based on least squares support vector machine and multiple classifiers Ultra Modern Telecommunications and Control Systems and Workshops (ICUMT), 2012 4th International Congress on 128 134 IEEE 2012
  9. Balaban , E. , Narasimhan , S. , Daigle , M. , Roychoudhury , I. et al. Development of a mobile robot test platform and methods for validation of prognostics-enabled decision making algorithms International Journal of Prognostics and Health Management 4 1 2013
  10. Levinson , J. , and Thrun , S. Automatic Online Calibration of Cameras and Lasers Robotics: Science and Systems 2013
  11. Taie , M. , El-Faramawy , I. , and Elmawazini , M. Methods for Prediction, Simulation and Verification of Real-Time Software Architectural Design based on Machine Learning Algorithms SAE Technical Paper 2015-01-0190 2015 10.4271/2015-01-0190
  12. Texas Instrument Advanced Driver Assistance (ADAS) Solutions Guide

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