Akademik Veri Yönetim Sistemi
CMES - Computer Modeling in Engineering and Sciences, cilt.145, sa.3, ss.4055-4087, 2025 (SCI-Expanded, Scopus)
Recent advancements in autonomous vehicle technologies are transforming intelligent transportation systems. Artificial intelligence enables real-time sensing, decision-making, and control on embedded platforms with improved efficiency. This study presents the design and implementation of an autonomous radio-controlled (RC) vehicle prototype capable of lane line detection, obstacle avoidance, and navigation through dynamic path planning. The system integrates image processing and ultrasonic sensing, utilizing Raspberry Pi for vision-based tasks and Arduino Nano for real-time control. Lane line detection is achieved through conventional image processing techniques, providing the basis for local path generation, while traffic sign classification employs a You Only Look Once (YOLO) model optimized with TensorFlow Lite to support navigation decisions. Images captured by the onboard camera are processed on the Raspberry Pi to extract lane geometry and calculate steering angles, enabling the vehicle to follow the planned path. In addition, ultrasonic sensors placed in three directions at the front of the vehicle detect obstacles and allow real-time path adjustment for safe navigation. Experimental results demonstrate stable performance under controlled conditions, highlighting the system’s potential for scalable autonomous driving applications. This work confirms that deep learning methods can be efficiently deployed on low-power embedded systems, offering a practical framework for navigation, path planning, and intelligent transportation research.