도로상황 감시 시스템을 위한 차량 에지 컴퓨팅
Vehicular Edge Computing for Road Monitoring Systems
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
교통 시스템의 효율성 및 안전성 향상을 위해 도입된 지능형 교통 시스템(ITS)의 처리용량 한계를 극복하고자 차량 에지 컴퓨팅 기술의 적용 가능성을 연구하였다. 기존 중앙집중식 ITS의 데이터 처리 과정에서 발생하는 네트워크 병목 현상과 처리 지연 문제를 해결하기 위해, CCTV 영상 데이터를 근처의 차량 내 에지 서버에서 처리하는 차량 에지 컴퓨팅 시스템의 기능 구조와 인터페이스를 제시하였으며, DQN 머신러닝 알고리듬을 활용해 최적의 차량을 에지 서버로 선정하는 메커니즘을 개발하였다. CCTV로 수집된 도로 이미지에 객체 탐색(object detection), 분류(classification) 기술을 적용하여 도로상의 장애 상황을 실시간으로 인식하는 프로토타입 시스템을 구현하였다. 또한 도로 환경 시뮬레이터를 통해 시스템 동작을 시각화함으로써 차량 에지 컴퓨팅이 실시간 도로상황 감시 및 대응 능력을 향상시킬 수 있는 효율적 방법임을 검증하였다.
Abstract
We studied the applicability of vehicular edge computing technology to overcome the limitations of processing capacity of the Intelligent Transportation System (ITS), which was introduced to improve the efficiency and safety of the transportation system. In order to solve network bottlenecks and processing delays that occur in the data processing of existing centralized ITS, we presented the functional architecture and interface of a vehicular edge computing system that processes CCTV video data on an in-vehicle edge server nearby. We developed a mechanism to select the optimal vehicle as the edge server using the DQN machine learning algorithm. Our system is able to identify obstacles on the road in real-time by applying object detection and classification technologies to road images collected through CCTV. We also demonstrated the efficiency of vehicular edge computing in improving real-time monitoring of road situation by visualizing system operations through a road environment simulator.
참고문헌 (16)
- P. D. Macheret, "Intelligent Transport Systems : Analysis of the Current State and Prospects of Development", 2021 International Conference on Quality Management, Transport and Information Security, Information Technologies (IT&QM&IS), pp.234~237 (2021), Russian Federation
- Salman Raza, "A Survey on Vehicular Edge Computing : Architecture, Applications, Technical Issues, and Future Directions", Wireless Communications and Mobile Computing, 2019, pp.1~19 (2019)
- Thone Thone Win Maw, "Load Distribution and Service Time Analysis of a Vehicular Edge Computing System", The 38th International Conference on Information Networking (ICOIN 2024) (2024)
- Y. Mao, "A Survey on Mobile Edge Computing : The Communication Perspective", IEEE Communications Surveys & Tutorials, 19(4), pp.2322~2358 (2017)
- Youn Joosang, "A Study on Vehicle Edge Computing Model for Autonomous Vehicle Service", Proceedings of the Korea Information Processing Society Conference, pp.109~110 (2020)
- S. Liu, "Edge Computing for Autonomous Driving : Opportunities and Challenges", Proceedings of the IEEE, 107(8), pp.1697~1716 (2019)
- Z. Zhou, "When Vehicular Fog Computing Meets Autonomous Driving : Computational Resource Management and Task Offloading", IEEE Network, 34(6), pp.70~76 (2020)
- Mohd. Fikri Azli Abdullah, "Edge computing for Vehicle to Everything : a short review", F1000Research, 10, pp.1104~1100 (2021)
- J. Lee, "A Survey on Vehicular Edge Computing Architectures", International Conference on Information and Communication Technology Convergence (ICTC), pp.2198~2200 (2022)
- A. Hammoud, "AI, Blockchain, and Vehicular Edge Computing for Smart and Secure IoV : Challenges and Directions", IEEE Internet of Things Magazine, 3(2), pp.68~73 (2020)
- T. Gong, "Edge Intelligence in Intelligent Transportation Systems : A Survey", IEEE Transactions on Intelligent Transportation Systems, 24(9), pp.8919~8944 (2023)
- A. Moubayed, "Cost-optimal V2X Service Placement in Distributed Cloud", Edge Environment, 2020 (2020), 1~6
- T. Anagnostopoulos, "Challenges and Solutions of Surveillance Systems in IoTEnabled Smart Campus : A Survey", IEEE Access, 9, pp.131926~131954 (2021)
- "What is GoormIDE"[URL]
- "Eclipse SUMO"[URL]
- Ulziitogtokh Dorjnamjil, "Road-Ahead Abnormality Detection", ICCNS (2024)