How to Cite
Publication Dates
Abstract
Self-driving automobiles are prominent in science and technology, which affect social and economic development. Deep learning (DL) is the most common area of study in artificial intelligence (AI). In recent years, deep learning-based solutions have been presented in the field of self-driving cars and have achieved outstanding results. Different studies investigated a variety of significant technologies for autonomous vehicles, including car navigation systems, path planning, environmental perception, as well as car control. End-to-end learning control directly converts sensory data into control commands in autonomous driving. This research aims to identify the most accurate pre-trained Deep Neural Network (DNN) for predicting the steering angle of a self-driving vehicle that is suitable to be applied to embedded automotive technologies with limited performance. Three well-known pre-trained models were compared in this study: AlexNet, ResNet18, and DenseNet121.
Transfer learning was utilized by modifying the final layer of pre-trained models in order to predict the steering angle of the vehicle. Experiments were conducted on the dataset collected from two different tracks. According to the study's findings, ResNet18 and DenseNet121 have the lowest error percentage for steering angle values. Furthermore, the performance of the modified models was evaluated on predetermined tracks. ResNet18 outperformed DenseNet121 in terms of accuracy, with less deviation from the center of the track, while DenseNet121 demonstrated greater adaptability across multiple tracks, resulting in better performance consistency.
References
D. B. O. Boesl and B. Liepert, “4 Robotic revolutions - Proposing a holistic phase model describing future disruptions in the evolution of robotics and automation and the rise of a new Generation ‘R’ of Robotic Natives,” IEEE Int. Conf. Intell. Robot. Syst., vol. 2016-Novem, pp. 1262–1267, 2016.
Anderson, J., Kalra, N., Stanley, K., Sorensen, P., Samaras, C. and Oluwatola, T., 2016. Autonomous Vehicle Technology: A Guide for Policymakers. [online] Rand.org. Available at: <https://www.rand.org/pubs/research_reports/RR443-2.html> [Accessed 9 September 2022].
S. Singh, “Critical Reasons for Crashes Investigated in the National Motor Vehicle Crash Causation Survey,” Natl. High. Traffic Saf. Adm., no. March, pp. 1–3, 2018, [Online]. Available: https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812506.
A. Brown, B. Repac, and J. Gonder, “Autonomous Vehicles Have a Wide Range of Possible Energy Impacts,” Work. Road Veh. Autom., no. 2012, p. 59210, 2013.
S. Grigorescu, B. Trasnea, T. Cocias, and G. Macesanu, “A survey of deep learning techniques for autonomous driving,” J. F. Robot., vol. 37, no. 3, pp. 362–386, 2020,
Y. Wang, D. Liu, H. Jeon, Z. Chu, and E. T. Matson, “End-to-end learning approach for autonomous driving: A convolutional neural network model,” ICAART 2019 - Proc. 11th Int. Conf. Agents Artif. Intell., vol. 2, no. Icaart, pp. 833–839, 2019.
Y. LeCun, U. Muller, J. Ben, E. Cosatto, and B. Flepp, “Off-road obstacle avoidance through end-to-end learning,” Adv. Neural Inf. Process. Syst., pp. 739–746, 2005.
S. Sharma, G. Tewolde, and J. Kwon, “Behavioral Cloning for Lateral Motion Control of Autonomous Vehicles Using Deep Learning,” in 2018 IEEE International Conference on Electro/Information Technology (EIT), May 2018, pp. 0228–0233.
S. Sharma, G. Tewolde, and J. Kwon, “Lateral and Longitudinal Motion Control of Autonomous Vehicles using Deep Learning,” in 2019 IEEE International Conference on Electro Information Technology (EIT), May 2019, pp. 1–5,
J. Kocić, N. Jovičić, and V. Drndarević, “An End-to-End Deep Neural Network for Autonomous Driving Designed for Embedded Automotive Platforms,” Sensors, vol. 19, no. 9, p. 2064, May 2019,
S. Du, H. Guo, and A. Simpson, “Self-Driving Car Steering Angle Prediction Based on Image Recognition.” 2019, [Online]. Available: http://arxiv.org/abs/1912.05440.
M. K. Islam, M. N. Yeasmin, C. Kaushal, M. Al Amin, M. R. Islam, and M. I. Hossain Showrov, “Comparative Analysis of Steering Angle Prediction for Automated Object using Deep Neural Network,” in 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Sep. 2021, pp. 1–7.
T. D. Do, M. T. Duong, Q. V. Dang, and M. H. Le, “Real-Time Self-Driving Car Navigation Using Deep Neural Network,” Proceedings 2018 4th International Conference on Green Technology and Sustainable Development, GTSD 2018. pp. 7–12, 2018.
Z. Yang, Y. Zhang, J. Yu, J. Cai, and J. Luo, “End-to-end Multi-Modal Multi-Task Vehicle Control for Self-Driving Cars with Visual Perceptions,” in 2018 24th International Conference on Pattern Recognition (ICPR), Aug. 2018, pp. 2289–2294, doi: 10.1109/ICPR.2018.8546189.
M. Al-Mukhtar, A. H. Morad, M. Albadri, and M. S. Islam, “Weakly Supervised Sensitive Heatmap framework to classify and localize diabetic retinopathy lesions,” Sci. Rep., vol. 11, no. 1, p. 23631, Dec. 2021.
M. Bojarski et al., “End to End Learning for Self-Driving Cars,” pp. 1–9, 2016.
A. Sangeetha and T. Rajendran, “Levenberg-Marquart logistic deep neural learning based energy efficient and load balanced routing in MANET,” Indones. J. Electr. Eng. Comput. Sci., vol. 23, no. 2, pp. 1002–1010, 2021.
A. Hussien Mary, Z. Bilal Kadhim, and Z. Saad Sharqi, “Face Recognition and Emotion Recognition from Facial Expression Using Deep Learning Neural Network,” IOP Conf. Ser. Mater. Sci. Eng., vol. 928, no. 3, p. 32061, 2020.
B. Jabir and N. Falih, “Dropout, a basic and effective regularization method for a deep learning model: A case study,” Indones. J. Electr. Eng. Comput. Sci., vol. 24, no. 2, pp. 1009–1016, 2021.
T. H. Abdullah, F. Alizadeh, and B. H. Abdullah, “COVID-19 Diagnosis System using SimpNet Deep Model,” Baghdad Sci. J., vol. 19, no. 5 SE-article, p. 1078, Oct. 2022.
A. M. Hamad Alhussainy and A. D. Jasim, “A novel pooling layer based on Gaussian function with wavelet transform,” Indones. J. Electr. Eng. Comput. Sci., vol. 20, no. 3, pp. 1289–1298, 2020.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” 2016 IEEE Conf. Comput. Vis. Pattern Recognit., pp. 770–778, Jun. 2016.
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely Connected Convolutional Networks,” in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Jul. 2017, pp. 2261–2269.
A. Asroni, K. R. Ku-Mahamud, C. Damarjati, and H. B. Slamat, “Arabic speech classification method based on padding and deep learning neural network,” Baghdad Science Journal, vol. 18. pp. 925–936, 2021, doi: 10.21123/bsj.2021.18.2(Suppl.).0925.
A. Mikolajczyk and M. Grochowski, “Data augmentation for improving deep learning in image classification problem,” in 2018 International Interdisciplinary PhD Workshop (IIPhDW), May 2018, pp. 117–122.
D. P. Kingma and J. Ba, “Adam: A Method for Stochastic Optimization,” 2017 2nd IEEE Int. Conf. Cloud Comput. Big Data Anal. ICCCBDA 2017, pp. 156–160, Dec. 2014. 10.1109/ICCCBDA.2017.7951902.
C. Sammut and G. I. Webb, Eds., “Mean Squared Error,” in Encyclopedia of Machine Learning, Boston, MA: Springer US, 2010, p. 653.
A. Driemel, S. Har-Peled, and C. Wenk, “Approximating the Fréchet distance for realistic curves in near linear time,” Discret. Comput. Geom., vol. 48, no. 1, pp. 94–127, 2012.
S. L. Seyler, A. Kumar, M. F. Thorpe, and O. Beckstein, “Path Similarity Analysis: A Method for Quantifying Macromolecular Pathways,” PLoS Comput. Biol., vol. 11, no. 10, pp. 1–36, 2015.
Copyright: Open Access authors retain the copyrights of their papers, and all open access articles are distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution and reproduction in any medium, provided that the original work is properly cited. The use of general descriptive names, trade names, trademarks, and so forth in this publication, even if not specifically identified, does not imply that these names are not protected by the relevant laws and regulations. While the advice and information in this journal are believed to be true and accurate on the date of its going to press, neither the authors, the editors, nor the publisher can accept any legal responsibility for any errors or omissions that may be made. The publisher makes no warranty, express or implied, with respect to the material contained herein.