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End to End Autonomous Driving System using Out-layer Removal

  • Journal of Internet of Things and Convergence
  • Abbr : JKIOTS
  • 2023, 9(1), pp.65~70
  • DOI : 10.20465/KIOTS.2023.9.1.065
  • Publisher : The Korea Internet of Things Society
  • Research Area : Engineering > Computer Science > Internet Information Processing
  • Received : December 21, 2022
  • Accepted : January 23, 2023
  • Published : February 28, 2023

Seung- Hyuk Jung 1 윤동호 2 Sung-Hoon Hong 1

1전남대학교
2한국생산기술연구원

Accredited

ABSTRACT

In this paper, we propose an autonomous driving system using an end-to-end model to improve lane departure and misrecognition of traffic lights in a vision sensor-based system. End-to-end learning can be extended to a variety of environmental conditions. Driving data is collected using a model car based on a vision sensor. Using the collected data, it is composed of existing data and data with outlayers removed. A class was formed with camera image data as input data and speed and steering data as output data, and data learning was performed using an end-to-end model. The reliability of the trained model was verified. Apply the learned end-to-end model to the model car to predict the steering angle with image data. As a result of the learning of the model car, it can be seen that the model with the outlayer removed is improved than the existing model.

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