Affiliation:
1. Department of Smart Vehicle Engineering, Konkuk University, Seoul 05029, Republic of Korea
Abstract
This study proposes and implements an intelligent transportation system (ITS)-based system that collects road event information via a vehicle’s on-board unit (OBU) and roadside unit (RSU), integrates and processes information based on the database and the road network, and provides information to vehicles. This system enables the collection of road unit information without calculating the exact location of event information recognized by the vehicle and facilitates duplicate processing of information recognized from multiple sources. This information can then be provided to other vehicles or road operators via apps or the web, enabling immediate response to emergency situations or changes in road conditions. To verify the practical applicability of this system, we developed a prototype and validated its functions through experiments. Using this system and methods, general drivers, autonomous vehicles, and infrastructure can cooperate in an ITS environment to recognize and propagate various road situations, contributing to the creation of safer and more efficient roads.
Funder
the Korea Institute for Advancement of Technology (KIAT) grant funded by the Korean Government
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Hardware and Architecture,Signal Processing,Control and Systems Engineering
Reference35 articles.
1. ETSI (2023, May 24). TR 102 v1.1.1 2011-06 Intelligent Transport Systems (Its); Vehicular Communications; Basic Set of Applications; Local Dynamic Map (ldm); Rationale for and Guidance on Standardization. Available online: https://www.etsi.org.
2. Chakraborty, P., Sharma, A., and Hegde, C. (2018, January 4–7). Freeway traffic incident detection from cameras: A semi-supervised learning approach. Proceedings of the 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA.
3. (2023, May 24). An Unexpected Accident on the Road, AI CCTV Finds It and Informs You in 1 Minute [Website]. Available online: https://www.yna.co.kr/view/AKR20200427164700003.
4. Gokasar, I., Timurogullari, A., Özkan, S.S., Deveci, M., and Lv, Z. (2022). MSND: Modified standard normal deviate incident detection algorithm for connected autonomous and human-driven vehicles in mixed traffic. IEEE Trans. Intell. Transp. Syst., 1–10.
5. Shin, S., Kim, J., and Moon, C. (2021). Road dynamic object mapping system based on edge-fog-cloud computing. Electronics, 10.
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献