Intelligent Road Tracking and Real-time Acceleration-deceleration for Autonomous Driving Using Modified Convolutional Neural Networks
Article Sidebar
Main Article Content
Abstract
Neural network is one of the most widely used method in autonomous driving. Current researchers use only steering angle to train artificial neural networks, ignoring the importance of acceleration and deceleration for autonomous vehicles. We used an intelligent driving platform built with the Raspberry Pi 4 Model B, a front wide-angle camera, and a 1:16 scale model car to achieve real-time acceleration and deceleration while performing road tracking. Existing models cannot learn steering angle and throttle values well. This research proposed a novel architecture CNN model (PBLM-CNN21) to achieve real-time acceleration and deceleration while achieving road tracking. The PBLM-CNN21 model can learn steering angle and throttle value. The training loss value of our proposed PBLM-CNN21 model was 35% lower than the current TDD model, and the stability of our proposed road tracking model was 82% greater than that of the current TDD model. Furthermore, we tested the impact of different hyper-parameters on training model loss and road tracking performance. In addition, we also tested the effectiveness of varying lighting conditions and speed ratios on road tracking performance. The PBLM-CNN21 model proved more robust than the existing TDD models. Moreover, the PBLM-CNN21 model achieved road tracking under different lighting conditions and was more suitable for high-speed ratios.
Keywords: autonomous driving; raspberry pi; deep learning; road tracking; convolutional neural networks; PBLM-CNN21
*Corresponding author: Tel.: (+66) 0966033973
E-mail: [email protected]
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright Transfer Statement
The copyright of this article is transferred to Current Applied Science and Technology journal with effect if and when the article is accepted for publication. The copyright transfer covers the exclusive right to reproduce and distribute the article, including reprints, translations, photographic reproductions, electronic form (offline, online) or any other reproductions of similar nature.
The author warrants that this contribution is original and that he/she has full power to make this grant. The author signs for and accepts responsibility for releasing this material on behalf of any and all co-authors.
Here is the link for download: Copyright transfer form.pdf
References
Karni, U., Ramachandran, S.S., Sivaraman, K. and Veeraraghavan, A.K., 2019. Development of autonomous downscaled model car using neural networks and machine learning. 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, March 27-29, 2019, pp. 1089-1094.
Bae, Y., Gomez, E., Haywood, A., Lazo, J., Whitson, P. and Wang, Y., 2021. Prototyping a system of cost-effective autonomous guided vehicles. Proceedings of the 2021 Annual General Donald R. Keith Memorial Capstone Conference, New York, USA., April 28, 2021, pp. 138-143.
Lee, K.L. and Lam, H.Y., 2021. Development of deep learning autonomous car using raspberry Pi. Progress in Engineering Application and Technology, 2(1), 534-548.
Du, X., Ang, M.H. and Rus, D., 2017. Car detection for autonomous vehicle: LIDAR and vision fusion approach through deep learning framework. 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, Canada, September 24-28, 2017, pp. 749-754.
Zang, S., Ding, M., Smith, D., Tyler, P., Rakotoarivelo, T. and Kaafar, M.A., 2019. The impact of adverse weather conditions on autonomous vehicles: how rain, snow, fog, and hail affect the performance of a self-driving car. IEEE Vehicular Technology Magazine, 14(2), 103-111.
Yuenyong, S. and Jian, Q., 2017. Generating synthetic training images for deep reinforcement learning of a mobile robot. Journal of Intelligent Informatics and Smart Technology, 2, 16-20.
Do, T.-D., Duong, M.-T., Dang, Q.-V. and Le, M.-H., 2018. Real-time self-driving car navigation using deep neural network. The 4th International Conference on Green Technology and Sustainable Development (GTSD), Ho Chi Min City, Vietnam, November 23-24, 2018, pp. 7-12.
Rausch, V., Hansen, A., Solowjow, E., Liu, C., Kreuzer, E. and Hedrick, J.K., 2017. Learning a deep neural net policy for end-to-end control of autonomous vehicles. 2017 American Control Conference (ACC), Seattle, USA., May 24-26, 2017, pp. 4914-4919.
Lin, W.-Y., Hsu, W.-H. and Chiang, Y.-Y., 2018. A combination of feedback control and vision-based deep learning mechanism for guiding self-driving cars. 2018 IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR), Taichung, Taiwan, December 10-12, 2018, pp. 262-266.
Bechtel, M.G., McEllhiney, E., Kim, M. and Yun, H., 2018. Deeppicar: A low-cost deep neural network-based autonomous car. 2018 IEEE 24th International Conference on Embedded and Real-time Computing Systems and Applications (RTCSA), Hakodate, Japan, August 28-31, 2018, pp. 11-21.
Goldfain, B., Drews, P., You, C., Barulic, M., Velev, O., Tsiotras, P. and Rehg, J.M., 2019. Autorally: An open platform for aggressive autonomous driving. IEEE Control Systems Magazine, 39(1), 26-55.
Bayar, B. and Stamm, M.C., 2016. A deep learning approach to universal image manipulation detection using a new convolutional layer. Proceedings of the 4th ACM Workshop on Information Hiding and Multimedia Security, Vigo Galicia, Spain, June 20-22, 2016, pp. 5-10.
Nagi, J., Ducatelle, F., Di Caro, G.A., Cireşan, D., Meier, U., Giusti, A., Nagi, F., Schmidhuber, J. and Gambardella, L.M., 2011. Max-pooling convolutional neural networks for vision-based hand gesture recognition. 2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), Kuala Lumpur, Malaysia, November 16-18, 2011, pp. 342-347.
Wang, S., Jiang, Y., Hou, X., Cheng, H. and Du, S., 2017. Cerebral micro-bleed detection based on the convolution neural network with rank based average pooling. IEEE Access, 5, 16576-16583.
Liu, K., Kang, G., Zhang, N. and Hou, B., 2018. Breast cancer classification based on fully-connected layer first convolutional neural networks. IEEE Access, 6, 23722-23732.
Chen, Z. and Huang, X., 2017. End-to-end learning for lane keeping of self-driving cars. 2017 IEEE Intelligent Vehicles Symposium (IV), Los Angeles, USA., June 11-14, 2017, pp. 1856-1860.
Setiawan, F.B., Siva, P.M., Pratomo, L.H. and Riyadi, S., 2021. Design and implementation of smart forklift for automatic guided vehicle using raspberry Pi 4. Journal of Robotics and Control, 2(6), 508-514.
Maizar, G.A., Hidayat, B. and Kharisma, O.B., 2021. Home electrical intelligent control using node-red based on raspberry Pi-3 B+. Indonesian Journal of Electrical Engineering and Renewable Energy, 1(1), 1-7.
Dewangan, D.K. and Sahu, S.P., 2020. Deep learning-based speed bump detection model for intelligent vehicle system using raspberry Pi. IEEE Sensors Journal, 21(3), 3570-3578.
Seth, A., James, A. and Mukhopadhyay, S.C., 2020. 1/10th scale autonomous vehicle based on convolutional neural network. International Journal on Smart Sensing and Intelligent Systems, 13(1), 1-17.
Hong, P.-H., Qiu, M. and Wang, Y., 2020. Autonomous driving and control: Case studies with self-driving platforms. IEEE 2020 7th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/2020 6th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom), New York, USA, August 1-3, 2020, pp. 17-22.