Earticle

초록

영어

CNN (Convolutional Neural Network), a type of deep learning algorithm, is a type of artificial neural network used to analyze visual images. In deep learning, it is classified as a deep neural network and is most commonly used for visual image analysis. Accordingly, an AI autonomous driving model was constructed through real-time image processing, and a crosswalk image of a road was used as an obstacle. In this paper, we proposed a low-cost model that can actually implement autonomous driving based on the CNN model. The most well-known deep neural network technique for autonomous driving is investigated and an end-to-end model is applied. In particular, it was shown that training and self-driving on a simulated road is possible through a practical approach to realizing lane detection and keeping

목차

Abstract
1. Introduction
2. Theoretical Background
2.1 Deep Learning Method for Self-Driving Cars
2.2 Recognition of autonomous driving through deep learning
3. Architecture Design for Low-cost Autonomous car
3.1 Data Collection & Preprocessing
3.2 Training of Self-driving car prototype
3.3 Network Architecture
4. Implementation
5. Conclusion
Acknowledgement
References

키워드

저자

• Mi-Hwa Song [ Assistant Professor, School of Information and Communication Science, Semyung University, Jecheon, Korea ] Corresponding Author

참고문헌

발행기관

간행물

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• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380