Version 1
: Received: 23 May 2021 / Approved: 24 May 2021 / Online: 24 May 2021 (13:03:33 CEST)
How to cite:
Aliyev, N.; Guzel, M.T.; Sezer, O. Autonomous Driving Implementation in an Experimental Environment. Preprints2021, 2021050568. https://doi.org/10.20944/preprints202105.0568.v1
Aliyev, N.; Guzel, M.T.; Sezer, O. Autonomous Driving Implementation in an Experimental Environment. Preprints 2021, 2021050568. https://doi.org/10.20944/preprints202105.0568.v1
Aliyev, N.; Guzel, M.T.; Sezer, O. Autonomous Driving Implementation in an Experimental Environment. Preprints2021, 2021050568. https://doi.org/10.20944/preprints202105.0568.v1
APA Style
Aliyev, N., Guzel, M.T., & Sezer, O. (2021). Autonomous Driving Implementation in an Experimental Environment. Preprints. https://doi.org/10.20944/preprints202105.0568.v1
Chicago/Turabian Style
Aliyev, N., Mehmet Turan Guzel and Oguzhan Sezer. 2021 "Autonomous Driving Implementation in an Experimental Environment" Preprints. https://doi.org/10.20944/preprints202105.0568.v1
Abstract
Autonomous systems require identifying the environment and it has a long way to go before putting it safely into practice. In autonomous driving systems, the detection of obstacles and traffic lights are of importance as well as lane tracking. In this study, an autonomous driving system is developed and tested in the experimental environment designed for this purpose. In this system, a model vehicle having a camera is used to trace the lanes and avoid obstacles to experimentally study autonomous driving behavior. Convolutional Neural Network models were trained for Lane tracking. For the vehicle to avoid obstacles, corner detection, optical flow, focus of expansion, time to collision, balance calculation, and decision mechanism were created, respectively.
Convolutional Neural Network; Lane tracking; Optical Flow; Focus of Expansion; Time to Collision
Subject
Computer Science and Mathematics, Algebra and Number Theory
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
