Yazar "Baykar, Ali Omer" seçeneğine göre listele

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    Deep Learning based Vehicle-To-Vehicle See Through System with Auto-Start Function
    (Institute of Electrical and Electronics Engineers Inc., 2022) Kaya, Mehmet Mucahit; Ozisik, Maide Elif; Baykar, Ali Omer; Yannier, Selim; Yildiz, Tuba Conka
    Today, with the increasing human population, the need for transportation is also increasing. These increasing transportation needs bring along some problems such as traffic density and traffic accidents. It is known that many of these accidents are caused by incorrect overtaking on single-lane roads and highways. In this research study, a solution proposal for such wrongful overtaking situations is discussed. The most important of these situations is that the vehicle to be overtaken is obstructing the view. In this study, by establishing a WLAN-based communication system between vehicles, it is discussed to eliminate the related loss of vision by image transfer between each vehicles. Unlike existing systems, this image transfer process is equipped with an automatic trigger with artificial intelligence. When overtaking is required, it is determined by means of deep networks whether vision loss occurs or not. As a main essence of this work, If vision loss is detected, the image transfer process between vehicles starts automatically and informs the driver. As a result of the real-Time tests, it has been observed that it works successfully within the processing time required for the system to process 20 fps image data at a distance of 100 meters. © 2022 IEEE.
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    Öğe
    Design of a Mobile Data Collection Robot for Learning-based Localization and Autonomous Driving
    (Institute of Electrical and Electronics Engineers Inc., 2023) Baykar, Ali Omer; Lambrecht, Jens; Kural, Ayhan; Uygur, Selcuk Eray; Yildiz, Ahmet
    This study introduces a mobile robot capable of collecting position and corresponding visual data seamlessly from both indoor and outdoor settings within the same sequence. The mobile robot has been specifically designed to navigate obstacles such as stairs and steps during transitions between indoor and outdoor environments. To accomplish this, the robot incorporates differential driving dynamics and is equipped with essential sensors including two stereo cameras, LIDAR, IMU, and GNSS. The entire system operates on the Robot Operating System (ROS). Consequently, it becomes possible to create a comprehensive dataset that encompasses not only the routes traversed by mobile vehicles but also includes all vehicle and pedestrian roads, as well as indoor spaces, found within a campus environment. © 2023 IEEE.