Acknowledgement
이 논문은 2022년도 한국연구재단의 지원을 받아 수행된 기초연구사업임 (2021R1F1A1050120, NRF-2020R1F1A1069361)
References
- A. Eskandarian, W. Chaoxian, "Research advances and challenges of autonomous and connected ground vehicles," IEEE Transactions on Intelligent Transportation Systems, Vol. 22, No. 2, pp. 683-711, Dec. 2019. https://doi.org/10.1109/TITS.2019.2958352
- A. Pandian, "Challenges in Autonomous Vehicle Development," International Conference on Industrial Engineering and Operations Management, Aug. 2019.
- E. Yurtsever, J. Lambert, "A survey of autonomous driving: Common practices and emerging technologies," IEEE access, Vol. 8, pp. 58443-58469, Mar. 2020. https://doi.org/10.1109/ACCESS.2020.2983149
- S. Kuutti, R. Bowden, "A survey of deep learning applications to autonomous vehicle control," IEEE Transactions on Intelligent Transportation Systems, Vol. 22, No. 2, pp. 712-733, Jan. 2020.
- A.E. Sallab, M. Abdou, "End-to-end deep reinforcement learning for lane keeping assist," arXiv preprint arXiv:1612.04340, 2016.
- W. Yuanqing, L. Siqin, "Deep reinforcement learning on autonomous driving policy with auxiliary critic network," IEEE transactions on neural networks and learning systems, Oct. 2021.
- A.V. Bernstein, E.V. Burnaev, "Reinforcement learning for computer vision and robot navigation," In: International Conference on Machine Learning and Data Mining in Pattern Recognition. Springer, Cham, pp. 258-272, Jul. 2018.
- R.S. Sutton, A.G. Barto, "Reinforcement learning: An introduction," MIT press, 2018.
- G. Chuan, P. Geoff, "On calibration of modern neural networks," In: International conference on machine learning. PMLR, pp. 1321-1330, 2017.
- Y. Gal, "Uncertainty in deep learning", PhD thesis, University of Cambridge, 2016.
- J. Chen, B. Yuan, M. Tomizuka, "Deep imitation learning for autonomous driving in generic urban scenarios with enhanced safety," In 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, pp. 2884-2890, Macau, China, Nov. 2019.
- S. Chen, M. Wang, W. Song and Y. Yang, "Stabilization approaches for reinforcement learning- based end-to-end autonomous driving," IEEE Transactions on Vehicular Technology, Vol. 69, No. 5, pp. 4740-4750, Mar. 2020. https://doi.org/10.1109/TVT.2020.2979493
- H. Liu, Z. Huang, J. Wu, "Improved deep reinforcement learning with expert demonstrations for urban autonomous driving," In 2022 IEEE Intelligent Vehicles Symposium (IV), pp. 921-928, Aachen, Germany, Jun. 2022.
- H. Gao, G. Shi, G. Xie, "Car-following method based on inverse reinforcement learning for autonomous vehicle decision-making," International Journal of Advanced Robotic Systems, Vol. 15, No. 6, Dec. 2018.
- Z. Qiao, Z. Tyree, "Hierarchical reinforcement learning method for autonomous vehicle behavior planning," In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 6084-6089, Las Vegas, USA, Oct. 2020.
- T. Shi, P. Wang, X. Cheng, "Driving decision and control for automated lane change behavior based on deep reinforcement learning," In 2019 IEEE intelligent transportation systems conference (ITSC), pp. 2895-2900, Auckland, New Zealand, Oct. 2019.
- Z. Cao, E. Biyik, "Reinforcement learning based control of imitative policies for near-accident driving," arXiv preprint arXiv:2007.00178, 2020.
- B. Gangopadhyay, P. Dasgupta, "Safe and Stable RL (S2RL) Driving Policies Using Control Barrier and Control Lyapunov Functions," IEEE Transactions on Intelligent Vehicles, Mar. 2022.
- K. Min, H. Kim, and K. Huh, "Deep distributional reinforcement learning based high-level driving policy determination," IEEE Transactions on Intelligent Vehicles, Vol. 4, No. 3, pp. 416-424, May, 2019. https://doi.org/10.1109/TIV.2019.2919467
- B. Lakshminarayanan, A. Pritzel, "Simple and scalable predictive uncertainty estimation using deep ensembles," NIPS'17: Proceedings of the 31st International Conference on Neural Information Processing Systems, pp. 6405-6416 Dec. 2017.
- C.J. Hoel, K. Wolff, and L. Laine, "Tactical decision-making in autonomous driving by reinforcement learning with uncertainty estimation," In 2020 IEEE Intelligent Vehicles Symposium (IV). IEEE, pp. 1563-1569, Las Vegas, USA, Oct. 2020.
- B. Lutjens, M. Everett, and J.P. How, "Safe reinforcement learning with model uncertainty estimates," In 2019 International Conference on Robotics and Automation (ICRA). IEEE, pp. 8662-8668, Montreal, Canada, May, 2019.
- M. Janner, Q. Li and S. Levine, "Reinforcement learning as one big sequence modeling problem," In ICML 2021 Workshop on Unsupervised Reinforcement Learning, Jun. 2021.
- L. Chen, K. Lu, A. Rajeswaran and K. Lee, "Decision transformer: Reinforcement learning via sequence modeling," Advances in neural information processing systems, pp. 15084-15097, 2021.
- Y. Gal, Z. Ghahramani, "Dropout as a bayesian approximation: Representing model uncertainty in deep learning," In international conference on machine learning. PMLR, pp. 1050-1059, 2016.
- G. Brockman, V. Cheung, L. Pettersson, J. Schneider, "OpenAI Gym," arXiv preprint arXiv:1606.01540, 2016.
- 김영광, 김진술, "자율주행에서 이미지 객체 분할을 위한 강화된 DFCN 알고리즘 성능 연구," 스마트미디어저널, 제9권, 제4호, 9-16쪽, 2020년 12월
- 김재상, 문해민, 반성범, "오픈소스 하드웨어 기반 차선검출 기술에 대한 연구," 스마트미디어저널, 제6권, 제3호, 15-20쪽, 2017년 9월
- H. N. Quach, H. J. Jo, S. W. Yeom, K. B. Kim, "Link Stability aware Reinforcement Learning based Network Path Planning," Smart Media Journal, Vol. 11, No. 5, pp. 82-90, Jun. 2022.