JCEEES
Journal of Computer & Electrical and Electronics Engineering Sciences

JCEEES aims to publish original articles covering the theoretical foundations of major computer, electronic and electrical engineering sciences, as well as academic, commercial and educational aspects that propose new ideas for the application and design of artificial intelligence, software and information systems. In addition to wide-ranging regular topics, JCEEES also makes it a principle to include special topics covering specific topics in all areas of interest mainly in computational medicine, artificial intelligence, computer science, and electrical & electronic engineering science.

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Original Article
Steering angle prediction in autonomous vehicles: a deep learning approach combining Vgg16 and Lstm
Abstract
The field of autonomous driving has seen remarkable progress in recent years, with the prediction of steering angles based on varying road conditions emerging as a critical area of focus. While previous efforts have concentrated on lane detection, road object identification, and 3-D reconstruction, our research centers on a vision-based model that leverages deep networks to translate raw camera images into steering angles without the need for predefined feature learning. In our paper, we introduce an end-to-end model that employs deep transfer learning to predict steering angles from image sequences captured by onboard cameras. This model merges two deep learning architectures: a convolutional neural network (CNN) and a long short-term memory (LSTM) network. We utilize the VGG16 network, pre-trained on ImageNet and renowned for its performance, to extract spatial features from the images. Concurrently, the LSTM network processes the temporal information embedded within the image sequences. Our proposed model comprehensively processes spatial-temporal data and adeptly models the nonlinear relationship between the input images and the steering angles. We conducted an experimental study using a publicly available dataset to evaluate the model's effectiveness. The outcomes of our experimental analysis reveal that our model delivers highly efficient and accurate steering angle predictions, effectively emulating human driving patterns. Moreover, it surpasses current models in both performance and training efficiency, achieving a Mean Squared Error (MSE) score of 0.0728 as its loss function.

Keywords : Steering angle prediction, VGG16, LSTM, deep learning, convolutional neural networks

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Article available in :
Volume 2, Issue 2, 2024
Page : 46-51
https://doi.org/10.51271/JCEEES-0017
Article Information
Received : 13 Eyl 2024
Accepted : 27 Eyl 2024
Published : 29 Eki 2024
Corresponding Author :

Ahmet Mehmet Karadeniz: Doctoral School of Multidisciplinary Engineering Sciences, Széchenyi István University, Györ, Hungary

[email protected]
Citation : Karadeniz, A.M., & Koçak, N.F. (2024) Steering angle prediction in autonomous vehicles: a deep learning approach combining VGG16 and LSTM. J Comp Electr Electron Eng Sci, 2(2), 46-51.

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