Autonomous Driving Implementation in an Experimental Environment
- URL: http://arxiv.org/abs/2106.15274v1
- Date: Sun, 23 May 2021 11:14:09 GMT
- Title: Autonomous Driving Implementation in an Experimental Environment
- Authors: Namig Aliyev, Oguzhan Sezer, Mehmet Turan Guzel
- Abstract summary: 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.
For the vehicle to avoid obstacles, corner detection, optical flow, focus of expansion, time to collision, balance calculation, and decision mechanism were created.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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