Acknowledgement
본 연구 논문은 한국전자통신연구원 연구운영지원사업의 일환으로 수행되었음[20ZR1100, 산업 및 사회문제 해결을 위한 사물 분산지능 핵심원천 기술 개발].
References
- 최진철, 손영성, "인공지능 기발 자율 사물 개발 동향과 발전전망," 주간기술 동향, 1937호, 2020.
- Gartner, "Top 10 Strategic Technology Trends for 2019," G00374252, 2018.
- 양희태 외, "인공지능 기술 전망과 혁신정책 방향-국가 인공지능 R&D 정책 개선방안을 중심으로," 정책연구 2018-13, 2018.
- KDB 산업은행 경제연구소, "자율주행차 국내외 개발 현황," 이슈분석 제771호, 2020.2.
- 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.
- S. Grigorescu et al., "A survey of deep learning techniques for autonomous driving," Journal of field robotics, vol. 37 no. 3, Apr. 2020.
- E. Yurtsever et al., "A Survey of Autonomous Driving: Common Practices and Emerging Technologies," IEEE Access, vol. 8, 2020.
- 스튜어드 러셀, 피터 노빅, "인공지능: 현대적 접근방식," 제이펍, 2016.
- Tractica, "Artificial intelligence for enterprise applications," 2015.
- H. Xu and G. Srivastava, "Automatic recognition algorithm of traffic signs based on convolution neural network," Multimedia Tools and Applications, vol. 79, Jan. 2020.
- 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.
- 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.
- V. John et al., "Real-time road surface and semantic lane estimation using deep features," Signal, Image and Video Processing, vol. 12, Mar. 2018.
- D. Neven et al., "Towards End-to-End Lane Detection: an Instance Segmentation Approach," arXiv: 1802.05591, 2018.
- A. Krizhevsky, I. Sutskever and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," in Proc. Adv. Neural Inf. Process. Syst. 2012.
- M. Mancini et al., "J-MOD2: Joint monocular obstacle detection and depth estimation," IEEE Robot. Autom. Lett. 2018.
- Y. Zhong, H. Li and Y. Dai, "Open-world stereo video matching with deep RNN," in Proc. Eur. Conf. Comput. Vis. Sep. 2018.
- B. Huval et al., "An empirical evaluation of deep learning on highway driving," Apr. 2015, arXiv:1504.01716
- L. Wang et al., "Knowledge guided disambiguation for largescale scene classification with multi-resolution CNNs," IEEE Trans. Image Process. 2017.
- W. Byeon et al., "Scene labeling with LSTM recurrent neural networks," In Proc. IEEE Conf. Comput. Vis. Pattern Recognit. Jun. 2015.
- 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.
- A. K. Ushani and R. M. Eustice, "Feature Learning for Scene Flow Estimation from LIDAR," in Proc. Conf. Robot Learning, vol. 87, Oct. 2018.
- 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.
- 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.
- S. Shalev-Shwartz, S. Shammah and A. Shashua, "Safe, MultiAgent, Reinforcement Learning for Autonomous Driving," 2016.
- A. I. Panov, K. S. Yakovlev and R. Suvorov, "Grid Path Planning with Deep Reinforcement Learning: Preliminary Results," Procedia Computer Science, vol. 123, 2018