Browsing by Author "Toy, Ibrahim"

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Model Predictive Control for Reliable and Efficient Path Tracking in Autonomous Vehicles
(Institute of Electrical and Electronics Engineers Inc., 2025) Toy, Ibrahim; Yusefi, Abdullah; Durdu, Akif
In recent years, there have been countless studies on autonomous vehicles. And this field is growing. Considering this growth, the issue of planning and control, which has an important place in autonomous vehicles, comes to the fore. In this study, a path tracking algorithm based on Model Predictive Control (MPC) is developed for autonomous vehicle control. MPC is basically to predict the future behavior of a generated cost function to be minimized by optimization methods. In the proposed algorithm, control inputs are calculated over a prediction horizon using the vehicle dynamic model and the reference path to optimize the vehicle progression. In order to add the obstacle avoidance mechanism to the system, obstacle locations are detected from an occupancy grid map generated with three-dimensional LiDAR and added to the cost function. Simulation and real-world tests have shown that the MPC algorithm can optimally follow the reference path while avoiding obstacles. © 2025 Elsevier B.V., All rights reserved.
Real-Time Safety Helmets and Vests Detection in Industrial Environments Using YOLO
(Institute of Electrical and Electronics Engineers Inc., 2025) Souare, Mamady Cheick; Toy, Ibrahim; Yusefi, Abdullah; Durdu, Akif
Worker safety is a critical concern in industrial and construction environments, where hazardous conditions can pose significant risks to employees. Ensuring that workers wear appropriate safety equipment, such as safety helmets and vests, is essential in preventing serious workplace injuries and illnesses. However, traditional monitoring methods may be insufficient for effectively detecting whether workers are adhering to safety regulations. Manual inspections, while common, can be time-consuming, and difficult to implement consistently across large worksites. This paper explores the application of the You Only Look Once object detection algorithm to automatically detect safety helmets and vests in real-time. By combining deep learning and computer vision methods, the implemented solution aims to enhance workplace safety compliance by providing an efficient, scalable, and accurate method for monitoring workers. The real-time nature of YOLO enables swift identification of safety violations, allowing for prompt corrective actions. This approach has the potential to significantly improve worker protection while reducing the reliance on manual inspection processes, ultimately contributing to a safer and more efficient working environment. © 2025 Elsevier B.V., All rights reserved.