Modeling of Traffic Information and Services for the Traffic Control Center in Autonomous Vehicle-Mixed Traffic Situations

Author:

Yang Dong-Hyuk1,Choi Sung-Soo2,Kang Yong-Shin2

Affiliation:

1. Department of Industrial Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea

2. Advanced Institute of Convergence Technology, Suwon 16229, Republic of Korea

Abstract

Achieving fully autonomous driving requires seamless collaboration between advanced autonomous driving and road infrastructure technologies. As the proportion of autonomous vehicles (AVs) increases, challenges may arise from their insufficient knowledge of the behavior of traffic objects and inability to effectively drive short distances. Therefore, traffic control centers that can proactively control these issues in real time are essential. In this study, first, the terminology is defined and the types of AV-mixed Traffic Information that a traffic control center needs to efficiently collect, store, and analyze to accommodate the coexistence of AVs and conventional vehicles are identified. Second, a generic notation for an AV-mixed Traffic Information model is defined and the results of modeling each AV-mixed Traffic Information type are presented. Third, an AV-mixed Traffic Information services model that included the names, operations, input/output messages, and relationships of all services is suggested. Finally, the importance of the service functionalities is evaluated through a survey. This study will serve as an initial guideline for the design, construction, and operation of traffic control centers and will help proactively address issues that may arise from the interaction between AVs and conventional vehicles on the road. Moreover, it contributes to identifying the types of traffic information and services that traffic control centers must provide in the era of AV-mixed traffic and suggests future directions for analysis and utilization of traffic information.

Funder

Korea Institute of Police Technology

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Reference45 articles.

1. A survey of autonomous driving: Common practices and emerging technologies;Yurtsever;IEEE Access,2020

2. (2014). Taxonomy and Definitions for Terms Related to On-Road Motor Vehicle Automated Driving Systems (Standard No. J3016_201401).

3. A Review On Autonomous Vehicles And Its Components;Naveen;J. Pharm. Negat. Results.,2023

4. Self-driving cars: A survey;Badue;Expert Syst. Appl.,2021

5. Malik, S., Khan, M.A., and El-Sayed, H. (2021). Collaborative autonomous driving—A survey of solution approaches and future challenges. Sensors, 21.

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