IJMLC 2018 Vol.8(2): 181-185 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2018.8.2.684

Prospects of Improving the self-Driving Car Development Pipeline: Transfer of Algorithms from Virtual to Physical Environment

Nauris Dorbe, Ingars Ribners, and Krisjanis Nesenbergs

Abstract—Problem of transferring and testing self-driving algorithms developed in virtual environment to a physical environment is explored by transferring a Convolutional Neural Network based self-driving car steering algorithm from virtual environment to physical RC card based environment for validation and testing as a step on the way for full scale self-driving car tests. In the process a novel approach for synthetic training data generation from single camera is developed, thus reducing the real world physical requirements for the algorithm and demonstrating the improved self-driving algorithm development pipeline from fully virtual environments to scaled physical models, to full self-driving cars, potentially leveraging the global developer community for development.

Index Terms—Convolutional neural network, training data generation, self-driving cars, virtual and physical models.

The authors are with the Institute of Electronics and Computer Science, Dzerbenes 14, Riga, Latvia (e-mail: naurisdorbe@gmail.com, e-mail: ram3a12@gmail.com, krisjanis.nesenbergs@gmail.com).

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Cite: Nauris Dorbe, Ingars Ribners, and Krisjanis Nesenbergs, "Prospects of Improving the self-Driving Car Development Pipeline: Transfer of Algorithms from Virtual to Physical Environment," International Journal of Machine Learning and Computing vol. 8, no. 2, pp. 181-185, 2018.

General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Scopus (since 2017), Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net