Traditional and Advanced Technologies in Intelligent Transportation Systems
DOI:
https://doi.org/10.54097/fv1m0f60Keywords:
Communication technologies, Sensor, Reinforcement learning, Intelligent Transportation Systems.Abstract
In the past decade, rapid urbanization and the increase in vehicles have worsened traffic conditions. Intelligent Transportation Systems (ITS) are considered an ideal solution to optimize urban transportation and tackle traffic challenges. This article comprehensively discusses the current development of basic and new technologies used in ITS. It includes specific subdivisions for these technologies, their application in ITS, as well as current and potential future challenges. The review indicates that vehicle-to-everything (V2X) communication technology is increasingly focused on the advancement of Cellular V2X (C-V2X). Sensor technology research and development has reached a high level of maturity. Reinforcement learning as a robust machine learning algorithm, is being extensively applied in traffic management. The article also provides examples of recent research-based applications using these technologies and explores potential directions for their future development. These reviews and analyses can serve as practical references for traffic managers and researchers working on further advancements in ITS.
Downloads
References
S.A. Shaheen., R. Finson. Intelligent Transportation Systems. Reference Module in Earth Systems and Environmental Sciences, Elsevier. 2016.
Genders. Wade., Razavi. Saiedeh. Using a Deep Reinforcement Learning Agent for Traffic Signal Control. arXiv. 2016: 1611.01142.
Ma, J., Hu, J., Hale, D. K., & Bared, J. Dynamic Hard Shoulder Running for Traffic Incident Management. Transportation Research Record, 2016, 2554 (1), 120 – 128.
Federal Communications Commission. FCC Modernizes 5.9 GHz Band to Improve Wi-Fi and Automotive Safety. https://www.fcc.gov/document/fcc-modernizes-59-ghz-band-improve-wi-fi-and-automotive-safety. 2020.
G. K. Munasinghe., & M. Murtaza. Analyzing Vehicle-to-Everything Communication for Intelligent Transportation System: Journey from IEEE 802.11p to 5G and Finally Towards 6G. 2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA), Sydney, Australia. 2020, 1 - 7.
Deng, R., Di, B., & Song, L. Cooperative Collision Avoidance for Overtaking Maneuvers in Cellular V2X-Based Autonomous Driving. IEEE Transactions on Vehicular Technology, 2019, 68 (5), 4434 - 4446.
Jin, Y., & Liu, X., & Zhu, Q. DSRC & C-V2X Comparison for Connected and Automated Vehicles in Different Traffic Scenarios. arXiv. 2022, 2203. 12553.
Cao, Y., Xiao, C., Benjamin. Cyr., Zhou, Y., Park, W., Sara. Rampazzi., Chen. Q. A., Fu. K., and Mao, Z. M. Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving. In Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security (CCS '19). Association for Computing Machinery, New York, NY, USA. 2019, 2267 – 2281.
E. Odat., J. S. Shamma., & C. Claudel. Vehicle Classification and Speed Estimation Using Combined Passive Infrared/Ultrasonic Sensors. IEEE Transactions on Intelligent Transportation Systems, 2018, 19 (5), 1593 - 1606.
Wei, H., Zheng, G., Yao, H., & Li, Z. IntelliLight: A Reinforcement Learning Approach for Intelligent Traffic Light Control. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD '18). Association for Computing Machinery, New York, NY, USA, 2018, 2496 – 2505.
R. Valiente., B. Toghi., R. Pedarsani., & Y. P. Fallah. Robustness and Adaptability of Reinforcement Learning-Based Cooperative Autonomous Driving in Mixed-Autonomy Traffic. IEEE Open Journal of Intelligent Transportation Systems, 2022, 3, 397 - 410.
Yang, J., Zhang, J., & Wang, H. Urban Traffic Control in Software Defined Internet of Things via a Multi-Agent Deep Reinforcement Learning Approach. IEEE Transactions on Intelligent Transportation Systems, 2021, 22 (6), 3742 - 3754.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Highlights in Science, Engineering and Technology
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
