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Evaluating architecture impacts on deep imitation learning performance for autonomous driving

conference contribution
posted on 2019-01-01, 00:00 authored by Parham M Kebria, Roohallah Alizadehsani, Syed Salaken, Ibrahim HossainIbrahim Hossain, Abbas KhosraviAbbas Khosravi, Dipu Kabir, Afsaneh Koohestani, Houshyar AsadiHoushyar Asadi, Saeid Nahavandi, Edward Tunsel, Mehrdad Saif
Imitation learning has gained huge popularity due to its promises in different fields such as robotics and autonomous systems. A great deal of past research work in the field of imitation learning has been devoted to developing efficient and effective policies using deep convolutional neural networks (CNNs). The performance of CNN-based control policies intimately depends on the network architecture. Determination of the optimal architecture for CNNs is still a hot research topic for the deep learning community. This study comprehensively investigates and quantifies the impact of CNN architecture on the performance of learned policy for an autonomous vehicle. CNN models with different architectures (number of layers and filters) are fed by visual information from multiple cameras obtained from multiple driving simulations. These networks are trained to precisely find the mapping between visual information and the steering angle. Two ensemble approaches are also introduced to further improve the overall accuracy of steering angle estimations. Obtained results indicate that deeper networks show a better performance than less deep networks during autonomous driving. Also it is observed that best results are achieved by ensemble approaches.

History

Event

IEEE Industrial Electronics Society. Conference (20th : 2019 : Melbourne, Vic.)

Series

IEEE Industrial Electronics Society Conference

Pagination

865 - 870

Publisher

Institute of Electrical and Electronics Engineers

Location

Melbourne, Vic.

Place of publication

Piscataway, N.J.

Start date

2019-02-13

End date

2019-02-15

Language

eng

Publication classification

E1 Full written paper - refereed

Copyright notice

2019, IEEE

Editor/Contributor(s)

[Unknown]

Title of proceedings

ICIT 2019 : Proceedings of the 2019 20th IEEE International Conference on Industrial Technology