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

• Published in: Sensors (Basel, Switzerland) Vol. 19; no. 9; p. 2064
• Main Authors: Kocić, Jelena, Jovičić, Nenad, Drndarević, Vujo
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 03-05-2019; MDPI
• Subjects: Artificial intelligence; autonomous driving; Autonomous vehicles; camera; Cameras; Complexity; Computer architecture; convolutional neural network; Data collection; Deep learning; deep neural network; Embedded systems; end-to-end learning; Inference; International conferences; Internet of Things; Machine learning; Neural networks; Sensors; Serbia; United States > US; end-to-end learning; convolutional neural network; embedded systems; camera; machine learning; deep neural network; autonomous driving
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s19092064

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.