개인구독
소속 기관이 없으신 경우, 개인 정기구독을 하시면 저렴하게
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지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2024.4
- 수록면
- 492 - 499 (8page)
- DOI
- 10.5302/J.ICROS.2024.23.0165
이용수
초록· 키워드
오류제보하기
This study aims to verify the reliability of Vehicle-to-Everything (V2X) communication systems for improving safety in advanced driving scenarios. Current autonomous driving systems that rely on sensors such as GPS or LiDAR have limitations. Additionally, slight delays when changing lanes can cause traffic congestion. Connected autonomous vehicles can solve this problem by exchanging driving data and controlling movements through 5G New Radio V2X (5G-NR-V2X) communication. In this study, 5GNR- V2X is utilized to facilitate trajectory sharing as well as driving information exchange. Sharing driving trajectories can allow the proactive management of risks due to lane merging and increase maneuvering efficiency. Comparing the proposed method with experiments using LiDAR sensors revealed similar performance, and the former was more cost-effective. This study reveals how connected driving through trajectory sharing can alleviate problems related to road congestion and transportation efficiency.
목차
Abstract
I. 서론
II. 협력적 차선변경 수행 방법
III. 실험 및 결과
IV. 고찰
V. 결론
REFERENCES
참고문헌 (29)
참고문헌 신청
S. D. Pendleton, H. Andersen, X. Du, X. Shen, M. Meghjani, Y. H. Eng, D. Rus, and M. H. Ang, “Perception, planning, control, and coordination for autonomous vehicles,” Machines, vol. 5, no. 1, p. 6, 2017.
W. Hong, P. Liang, L. Jun, Y. Wenhao, and X. Xiong, “Safety decision of running speed based on real-time weather,” 2022 IEEE Intelligent Vehicles Symposium (IV), pp. 212-217, June 2022.
X. Ding, Z. Wang, L. Zhang, and C. Wang, “Longitudinal vehicle speed estimation for four-wheel-independently-actuated electric vehicles based on multi-sensor fusion,” IEEE Transactions on Vehicular Technology, vol. 69, no. 11, pp. 12797-12806, 2020.
M.S. Kim, G. Eoh, and T.H. Park, “reinforcement learning with data augmentation for lane change decision-making,” Journal of Institute of Control, Robotics and Systems (in Korean), vol. 27, no. 8, pp. 572-577, 2021.
J. S. Kim and C. C. Chung, “Data-driven modeling of automated vehicles: Koopman operator approach and its application,” Journal of Institute of Control, Robotics and Systems (in Korean), vol. 28, no. 11, pp. 1038-1044, 2022.
W. Hong, P. Liang, L. Jun, Y. Wenhao, and X. Xiong, “Safety decision of running speed based on real-time weather,” 2022 IEEE Intelligent Vehicles Symposium (IV), pp. 212-217, June 2022.
X. Ding, Z. Wang, L. Zhang, and C. Wang, “Longitudinal vehicle speed estimation for four-wheel-independently-actuated electric vehicles based on multi-sensor fusion,” IEEE Transactions on Vehicular Technology, vol. 69, no. 11, pp. 12797-12806, 2020.
M.S. Kim, G. Eoh, and T.H. Park, “reinforcement learning with data augmentation for lane change decision-making,” Journal of Institute of Control, Robotics and Systems (in Korean), vol. 27, no. 8, pp. 572-577, 2021.
J. S. Kim and C. C. Chung, “Data-driven modeling of automated vehicles: Koopman operator approach and its application,” Journal of Institute of Control, Robotics and Systems (in Korean), vol. 28, no. 11, pp. 1038-1044, 2022.
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