Electronics and Telecommunications Trends (전자통신동향분석)

Electronics and Telecommunications Research Institute (한국전자통신연구원)
권호 표지
DOI QR코드

DOI QR Code

Application Trends of Deep Learning Artificial Intelligence in Autonomous Things

자율사물을 위한 심층학습 인공지능 기술 적용 동향

  • Published : 2020.12.01
Download PDF
⟨ Previous Next ⟩

Abstract

Recently, autonomous things, which are pieces of equipment or devices that grasp the context of circumstances on their own and perform actions appropriate for the situation in the surrounding environment, are attracting much research interest. This is because autonomous things are expected to be able to interact with humans more naturally, supersede humans in many tasks, and further solve problems by themselves by collaborating with each other without human intervention. This prospect leans heavily on AI as deep learning has delivered astonishing breakthroughs recently and broadened its range of applications. This paper surveys application trends in deep learning-based AI techniques for autonomous things, especially autonomous driving vehicles, because they present a wide range of problems involving perception, decision, and actions that are very common in other autonomous things.

Keywords

Acknowledgement

본 연구 논문은 한국전자통신연구원 연구운영지원사업의 일환으로 수행되었음[20ZR1100, 산업 및 사회문제 해결을 위한 사물 분산지능 핵심원천 기술 개발].

References

  1. 최진철, 손영성, "인공지능 기발 자율 사물 개발 동향과 발전전망," 주간기술 동향, 1937호, 2020.
  2. Gartner, "Top 10 Strategic Technology Trends for 2019," G00374252, 2018.
  3. 양희태 외, "인공지능 기술 전망과 혁신정책 방향-국가 인공지능 R&D 정책 개선방안을 중심으로," 정책연구 2018-13, 2018.
  4. KDB 산업은행 경제연구소, "자율주행차 국내외 개발 현황," 이슈분석 제771호, 2020.2.
  5. J. Ni et al., "A Survey on Theories and Applications for SelfDriving Cars Based on Deep Learning Methods," Applied Sciences, vol. 10 no. 8, 2020.
  6. S. Grigorescu et al., "A survey of deep learning techniques for autonomous driving," Journal of field robotics, vol. 37 no. 3, Apr. 2020.
  7. E. Yurtsever et al., "A Survey of Autonomous Driving: Common Practices and Emerging Technologies," IEEE Access, vol. 8, 2020.
  8. 스튜어드 러셀, 피터 노빅, "인공지능: 현대적 접근방식," 제이펍, 2016.
  9. Tractica, "Artificial intelligence for enterprise applications," 2015.
  10. H. Xu and G. Srivastava, "Automatic recognition algorithm of traffic signs based on convolution neural network," Multimedia Tools and Applications, vol. 79, Jan. 2020.
  11. E. Lee and D. Kim, "Accurate traffic light detection using deep neural network with focal regression loss," Image and Vision Computing, vol. 87, July. 2019.
  12. J. Kim, J. Kim, G. Jang and M. Lee, "Fast learning method for convolutional neural networks using extreme learning machine and its application to lane detection," Neural Networks, vol. 87, Mar. 2017.
  13. V. John et al., "Real-time road surface and semantic lane estimation using deep features," Signal, Image and Video Processing, vol. 12, Mar. 2018.
  14. D. Neven et al., "Towards End-to-End Lane Detection: an Instance Segmentation Approach," arXiv: 1802.05591, 2018.
  15. A. Krizhevsky, I. Sutskever and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," in Proc. Adv. Neural Inf. Process. Syst. 2012.
  16. M. Mancini et al., "J-MOD2: Joint monocular obstacle detection and depth estimation," IEEE Robot. Autom. Lett. 2018.
  17. Y. Zhong, H. Li and Y. Dai, "Open-world stereo video matching with deep RNN," in Proc. Eur. Conf. Comput. Vis. Sep. 2018.
  18. B. Huval et al., "An empirical evaluation of deep learning on highway driving," Apr. 2015, arXiv:1504.01716
  19. L. Wang et al., "Knowledge guided disambiguation for largescale scene classification with multi-resolution CNNs," IEEE Trans. Image Process. 2017.
  20. W. Byeon et al., "Scene labeling with LSTM recurrent neural networks," In Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Jun. 2015.
  21. D. Barnes et al., "Driven to Distraction: Self-Supervised Distractor Learning for Robust Monocular Visual Odometry in Urban Environments," in Proc. IEEE Int. Conf. on Robotics and Automation, 2018.
  22. A. K. Ushani and R. M. Eustice, "Feature Learning for Scene Flow Estimation from LIDAR," in Proc. Conf. Robot Learning, vol. 87, Oct. 2018.
  23. E. Yurtsever, C. Miyajima and K. Takeda, "A traffic flow simulation framework for learning driver heterogeneity from naturalistic driving data using autoencoders," Int. J. Automot. Eng. vol. 10 no. 1, 2019.
  24. K. Sama et al., "Driving feature extraction and behavior classification using an autoencoder to reproduce the velocity styles of experts," in Proc. Int. Conf. Intell. Transp. Syst. Nov. 2018.
  25. S. Shalev-Shwartz, S. Shammah and A. Shashua, "Safe, MultiAgent, Reinforcement Learning for Autonomous Driving," 2016.
  26. A. I. Panov, K. S. Yakovlev and R. Suvorov, "Grid Path Planning with Deep Reinforcement Learning: Preliminary Results," Procedia Computer Science, vol. 123, 2018