Genetic Optimization for Self-Driving Vehicles Based on Automated Behavior Cloning Convolutional Neural Network
mohamed gouda, A., Rahouma, K., donkol, A. (2024). Genetic Optimization for Self-Driving Vehicles Based on Automated Behavior Cloning Convolutional Neural Network. EKB Journal Management System, 43(1), 311-318. doi: 10.21608/jaet.2022.144496.1198
Ahmed abd el moaty mohamed gouda; Kamel Hussein Rahouma; ahmed abd el-baset donkol. "Genetic Optimization for Self-Driving Vehicles Based on Automated Behavior Cloning Convolutional Neural Network". EKB Journal Management System, 43, 1, 2024, 311-318. doi: 10.21608/jaet.2022.144496.1198
mohamed gouda, A., Rahouma, K., donkol, A. (2024). 'Genetic Optimization for Self-Driving Vehicles Based on Automated Behavior Cloning Convolutional Neural Network', EKB Journal Management System, 43(1), pp. 311-318. doi: 10.21608/jaet.2022.144496.1198
mohamed gouda, A., Rahouma, K., donkol, A. Genetic Optimization for Self-Driving Vehicles Based on Automated Behavior Cloning Convolutional Neural Network. EKB Journal Management System, 2024; 43(1): 311-318. doi: 10.21608/jaet.2022.144496.1198

Genetic Optimization for Self-Driving Vehicles Based on Automated Behavior Cloning Convolutional Neural Network

Journal of Advanced Engineering Trends
Volume 43, Issue 1, January 2024, Page 311-318  XML PDF (746.57 K)
Document Type: Original Article
DOI: 10.21608/jaet.2022.144496.1198
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Authors
Ahmed abd el moaty mohamed gouda email 1; Kamel Hussein Rahoumaorcid 2; ahmed abd el-baset donkol3
1communication and computing, engineering, Nahda university, Egypt
2Minia University
3Department of Communication and Computer,Engineering, Faculty of Engineering, Nahda University, Beni Suef 62521, Egypt.
Abstract
Recently, autonomous driving technology has learned to drive safely and smoothly. Nonetheless, using convolutional neural networks (CNNs) has significantly influenced designs of autonomous driving technology. Most current designs of CNN-based autonomous driving technology are built manually by specialists in both CNNs and the investigated topics. Thus, finding the optimum CNN designs for learning safe driving behavior is complex. This study uses genetic algorithms to construct automated genetic behavior cloning (AGBC). The “Automated” features of the proposed algorithm require little knowledge of CNNs. Using a front-facing camera and an experienced driver’s steering directions, researchers can acquire a strong CNNs architecture for learning safe driving behavior. Furthermore, it can be used to teach AGBC-CNNs to emulate human driving behavior. We compared our proposed technique AGBC-CNN with four manually adjustment behavior cloning CNNs (MABC-CNNs) and one automated genetic manually adjustment behavior cloning CNNs (AGMABC-CNNs) to validate its effectiveness and robustness. Studies show that AGBC-CNN outperforms existing architecture design techniques (MABC-CNNs and AGMABC-CNN) in terms of classification accuracy.
Keywords
genetic-CNN; Self-driving; Behavior cloning
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