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Article

An End-to-End Deep Neural Network for Autonomous Driving Designed for Embedded Automotive Platforms

School of Electrical Engineering, University of Belgrade, 11120 Belgrade, Serbia
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Author to whom correspondence should be addressed.
Sensors 2019, 19(9), 2064; https://doi.org/10.3390/s19092064
Received: 15 March 2019 / Revised: 16 April 2019 / Accepted: 30 April 2019 / Published: 3 May 2019
(This article belongs to the Section Intelligent Sensors)
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. View Full-Text
Keywords: autonomous driving; camera; convolutional neural network; deep neural network; embedded systems; end-to-end learning; machine learning autonomous driving; camera; convolutional neural network; deep neural network; embedded systems; end-to-end learning; machine learning
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MDPI and ACS Style

Kocić, J.; Jovičić, N.; Drndarević, V. An End-to-End Deep Neural Network for Autonomous Driving Designed for Embedded Automotive Platforms. Sensors 2019, 19, 2064. https://doi.org/10.3390/s19092064

AMA Style

Kocić J, Jovičić N, Drndarević V. An End-to-End Deep Neural Network for Autonomous Driving Designed for Embedded Automotive Platforms. Sensors. 2019; 19(9):2064. https://doi.org/10.3390/s19092064

Chicago/Turabian Style

Kocić, Jelena, Nenad Jovičić, and Vujo Drndarević. 2019. "An End-to-End Deep Neural Network for Autonomous Driving Designed for Embedded Automotive Platforms" Sensors 19, no. 9: 2064. https://doi.org/10.3390/s19092064

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