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.

eISSN: 2980-177X
Creative Commons CC BY-NC-ND 4.0 International License.

Online Manuscript System

Index
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

Download Article


1. Do, T.D., Duong, M.T., Dang, Q.V., & Le, M.H. (2018). Real-time self-driving car navigation using deep neural network. In 2018 4th International Conference on Green Technology and Sustainable Development (GTSD) (pp. 7-12). IEEE. doi:10.1109/GTSD.2018.8595590
2. Badue, C., Guidolini, R., Carneiro, R.V., Azevedo, P., Cardoso, V.B., Forechi, A., ... &amp; De Souza, A.F. (2021). Self-driving cars: a survey. <em>Expert Systems Appl,</em> 165,113816. doi:10.1016/j.eswa.2020.113816
3. Velaskar, P., Vargas-Clara, A., Jameel, O., &amp; Redkar, S. (2014). Guided navigation control of an unmanned ground vehicle using global positioning systems and inertial navigation systems. <em>Int J Electr Comput Engineer,</em> 4(3),2088-8708. doi:10.11591/ijece.v4i3.5183
4. Mammeri, A., Lu, G., &amp; Boukerche, A. (2015). Design of lane keeping assist system for autonomous vehicles. In 2015 7th International Conference on New Technologies, Mobility and Security (NTMS) (pp. 1-5). IEEE. doi:10.1109/NTMS.2015.7266483
5. Khodayari, A., Ghaffari, A., Ameli, S., &amp; Flahatgar, J. (2010). A historical review on lateral and longitudinal control of autonomous vehicle motions. In 2010 International Conference on Mechanical and Electrical Technology (pp. 421-429). IEEE. doi:10.1109/ICMET.2010.5598396
6. Alshbatat, A.I.N. (2013). Behavior-based approach for the detection of land mines utilizing off the shelf low cost autonomous robot. <em>Int J Robotics Automation,</em> 2(3),83. doi:10.11591/ijra.v2i3.2038
7. Saleem, H., Riaz, F., Mostarda, L., Niazi, M.A., Rafiq, A., &amp; Saeed, S. (2021). Steering angle prediction techniques for autonomous ground vehicles: a review. <em>IEEE Access,</em> 9,78567-78585. doi:10.1109/ACCESS.2021.3083890
8. Aparna, M.P., Gandhiraj, R., &amp; Panda, M. (2021). Steering angle prediction for autonomous driving using federated learning: The impact of vehicle-to-everything communication. In 2021 12th International Conference on Computing Communication and Networking Technologies (ICCCNT) (pp. 1-7). IEEE. doi:10.1109/ICCCNT51525.2021.9580097
9. Oussama, A., &amp; Mohamed, T. (2020). A literature review of steering angle prediction algorithms for self-driving cars. <em>Advan Intell Syst Appl Comput Sci,</em> 4,30-38. doi:10.1007/978-3-030-36674-2_4
10. Gidado, U.M., Chiroma, H., Aljojo, N., Abubakar, S., Popoola, S.I., &amp; Al-Garadi, M.A. (2020). A survey on deep learning for steering angle prediction in autonomous vehicles. <em>IEEE Access,</em> 8,163797-163817. doi:10.1109/ACCESS.2020.3017883
11. Jiang, H., Chang, L., Li, Q., &amp; Chen, D. (2020). Deep transfer learning enable end-to-end steering angles prediction for self-driving car. In 2020 IEEE Intelligent Vehicles Symposium (IV) (pp. 405-412). IEEE. doi:10.1109/IV47402.2020.9304611
12. Song, X., Cao, H., &amp; You, H. (2022, December). Steering Wheel Rotation Angle Prediction Based on VGG-16 and Data Augmentation. In 2022 4th International Conference on Control and Robotics (ICCR) (pp. 398-402). IEEE. doi:10.1109/ICCR55715.2022.10053867
13. Alsherif, M., Daowd, M., Bassiuny, A.M., &amp; Metered, H.A. (2023). Utilizing transfer learning in the udacity simulator to train a self-driving car for steering angle prediction. In 2023 Eleventh International Conference on Intelligent Computing and Information Systems (ICICIS) (pp. 134-139). IEEE. doi:10.1109/ICICIS58388.2023.10391185
14. Song, X., Cao, H., &amp; You, H. (2022). Steering wheel rotation angle prediction based on VGG-16 and data augmentation. In 2022 4th International Conference on Control and Robotics (ICCR) (pp. 398-402). IEEE. doi:10.1109/ICCR55715.2022.10053867
15. Hoang, T.N., Hong, P.P., Vinh, N.N., Nguyen, N.T., Nguyen, K.H., &amp; Quach, L.D. (2023). An improved lane-keeping controller for autonomous vehicles leveraging an integrated CNN-LSTM approach. <em>Int J Advan Comput Sci Appl,</em> 14,7. doi:10.14569/IJACSA.2023.0140723
16. Ismail, S.N.M.S., Razak, S.F.A., &amp; Aziz, N.A.A. (2024). Transfer learning for improved electrocardiogram diagnosis of cardiac disease: exploring the potential of pre-trained models. <em>Bullet Electr Engineer Informat,</em> 13(5),3288-3300. doi:10.11591/eei.v13i5.7053
17. Ismail, S.N.M.S., Razak, S.F.A., &amp; Aziz, N.A.A. (2024). Transfer learning for improved electrocardiogram diagnosis of cardiac disease: exploring the potential of pre-trained models. <em>Bullet Electr Engineer Informat,</em> 13(5),3288-3300. doi:10.11591/eei.v13i5.7053
18. Golnari, A., Komeili, M.H., &amp; Azizi, Z. (2024). Probabilistic deep learning and transfer learning for robust cryptocurrency price prediction. <em>Expert Syst Appl,</em> 124404.doi:10.1016/j.eswa.2024.124404
19. Karadeniz, A. M., Ballagi, &Aacute;., &amp; K&oacute;czy, L. T. (2024). Transfer learning-based steering angle prediction and control with fuzzy signatures-enhanced fuzzy systems for autonomous vehicles. <em>Symmetry, </em>16(9),1180. doi:10.3390/sym16091180
20. Turk, F. (2024). Investigation of machine learning algorithms on heart disease through dominant feature detection and feature selection. <em>SIViP,</em> 18,3943-3955. doi:10.1007/s11760-024-03060-0
21. Turk F. (2024). RNGU-NET: a novel efficient approach in segmenting tuberculosis using chest X-ray images. <em>PeerJ Comput Sci,</em> 10:e1780 doi:10.7717/peerj-cs.1780
22. https://www.kaggle.com/datasets/zaynena/selfdriving-car-simulator (accessed on 25 August 2024).
23. Du, S., Guo, H., &amp; Simpson, A. (2019). Self-driving car steering angle prediction based on image recognition. arXiv preprint arXiv:1912.05440. doi:10.48550/arXiv.1912.05440
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.

Read: 140
Cited: 0
Download : 65
_Footer