International Journal For Multidisciplinary Research

E-ISSN: 2582-2160     Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 6 Issue 2 March-April 2024 Submit your research before last 3 days of April to publish your research paper in the issue of March-April.

Parking for Autonomous Vehicle using Deep Learning Approach

Author(s) FUAD ALIEW, EBUBEKIR CEYLAN
Country Turkey
Abstract In this study, a model is proposed to allow the vehicle to park safely itself using deep learning approach for autonomous parking problem. The proposed method is included three main steps. In the first stage, the vehicle which a remotely controllable is designed to collect data. The goal of second part is prepared a track and label the collected data in order to collect real data with the designed vehicle. In an addition this collected data, is also added CNR parking dataset which is also based on real camera pictures. It has been used 75% of these data as training and 25% of them as testing. Lastly, a deep learning model has been developed based on CNN and transfer learning according to these data. This model classifies whether parking slot is empty or not. if classification is called free, then the vehicle starts to be parking maneuver, the vehicle doesn’t do parking maneuver while it is called busy. As a result, accuracy of the model was calculated about 0.96 which is quite good for park slot occupy detection. It was also performed successfully 7,6 out of every 10-parking maneuver.
Keywords Autonomous Vehicles, Deep Learning, Autonomous Parking, Self-Driving Car, Convolutional Neural Network
Field Engineering
Published In Volume 6, Issue 2, March-April 2024
Published On 2024-03-23
Cite This Parking for Autonomous Vehicle using Deep Learning Approach - FUAD ALIEW, EBUBEKIR CEYLAN - IJFMR Volume 6, Issue 2, March-April 2024. DOI 10.36948/ijfmr.2024.v06i02.14622
DOI https://doi.org/10.36948/ijfmr.2024.v06i02.14622
Short DOI https://doi.org/gtn32w

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