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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
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.

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