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An End-to-End Deep Neural Network for Autonomous Driving Designed for Embedded Automotive Platforms
In this paper, one solution for an end-to-end deep neural network for autonomous driving is presented. The main objective of our work was to achieve autonomous driving with a light deep neural network suitable for deployment on embedded automotive platforms. There are several end-to-end deep neural...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6539483/ https://www.ncbi.nlm.nih.gov/pubmed/31058820 http://dx.doi.org/10.3390/s19092064 |
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author | Kocić, Jelena Jovičić, Nenad Drndarević, Vujo |
author_facet | Kocić, Jelena Jovičić, Nenad Drndarević, Vujo |
author_sort | Kocić, Jelena |
collection | PubMed |
description | In this paper, one solution for an end-to-end deep neural network for autonomous driving is presented. The main objective of our work was to achieve autonomous driving with a light deep neural network suitable for deployment on embedded automotive platforms. There are several end-to-end deep neural networks used for autonomous driving, where the input to the machine learning algorithm are camera images and the output is the steering angle prediction, but those convolutional neural networks are significantly more complex than the network architecture we are proposing. The network architecture, computational complexity, and performance evaluation during autonomous driving using our network are compared with two other convolutional neural networks that we re-implemented with the aim to have an objective evaluation of the proposed network. The trained model of the proposed network is four times smaller than the PilotNet model and about 250 times smaller than AlexNet model. While complexity and size of the novel network are reduced in comparison to other models, which leads to lower latency and higher frame rate during inference, our network maintained the performance, achieving successful autonomous driving with similar efficiency compared to autonomous driving using two other models. Moreover, the proposed deep neural network downsized the needs for real-time inference hardware in terms of computational power, cost, and size. |
format | Online Article Text |
id | pubmed-6539483 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-65394832019-06-04 An End-to-End Deep Neural Network for Autonomous Driving Designed for Embedded Automotive Platforms Kocić, Jelena Jovičić, Nenad Drndarević, Vujo Sensors (Basel) Article In this paper, one solution for an end-to-end deep neural network for autonomous driving is presented. The main objective of our work was to achieve autonomous driving with a light deep neural network suitable for deployment on embedded automotive platforms. There are several end-to-end deep neural networks used for autonomous driving, where the input to the machine learning algorithm are camera images and the output is the steering angle prediction, but those convolutional neural networks are significantly more complex than the network architecture we are proposing. The network architecture, computational complexity, and performance evaluation during autonomous driving using our network are compared with two other convolutional neural networks that we re-implemented with the aim to have an objective evaluation of the proposed network. The trained model of the proposed network is four times smaller than the PilotNet model and about 250 times smaller than AlexNet model. While complexity and size of the novel network are reduced in comparison to other models, which leads to lower latency and higher frame rate during inference, our network maintained the performance, achieving successful autonomous driving with similar efficiency compared to autonomous driving using two other models. Moreover, the proposed deep neural network downsized the needs for real-time inference hardware in terms of computational power, cost, and size. MDPI 2019-05-03 /pmc/articles/PMC6539483/ /pubmed/31058820 http://dx.doi.org/10.3390/s19092064 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kocić, Jelena Jovičić, Nenad Drndarević, Vujo An End-to-End Deep Neural Network for Autonomous Driving Designed for Embedded Automotive Platforms |
title | An End-to-End Deep Neural Network for Autonomous Driving Designed for Embedded Automotive Platforms |
title_full | An End-to-End Deep Neural Network for Autonomous Driving Designed for Embedded Automotive Platforms |
title_fullStr | An End-to-End Deep Neural Network for Autonomous Driving Designed for Embedded Automotive Platforms |
title_full_unstemmed | An End-to-End Deep Neural Network for Autonomous Driving Designed for Embedded Automotive Platforms |
title_short | An End-to-End Deep Neural Network for Autonomous Driving Designed for Embedded Automotive Platforms |
title_sort | end-to-end deep neural network for autonomous driving designed for embedded automotive platforms |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6539483/ https://www.ncbi.nlm.nih.gov/pubmed/31058820 http://dx.doi.org/10.3390/s19092064 |
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