Article
Version 1
Preserved in Portico This version is not peer-reviewed
Autonomous Driving Implementation in an Experimental Environment
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. 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. Preprints 2021, 2021050568. 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.
Supplementary and Associated Material
https://github.com/auto-pilot: Source codes
Keywords
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.
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