TY - GEN
T1 - Traffic Control Task Scheduling Optimization Approach of Urban Road Intersection Based on Edge Computing
AU - Fu, Xiang
AU - Wang, Fei
AU - Ji, Nan
AU - Ren, Yilong
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - With the development of Connected Automated Vehicles (CAV), there will be a long period of mixed driving, which causes the traffic control task at intersections becomes much more complicated. The control tasks would include not only the traffic signal timing optimization but also the computing tasks for different levels of vehicles. Current control task management methods are proved to be inferior to solve this problem. In this paper, we propose an intersection control task scheduling optimization approach based on edge computing. Firstly, the traffic control edge computing system for intersections is constructed. Then, different types of control tasks are listed. Criticality and timeliness are introduced to calculate the control task's dynamic priority. The control task scheduling optimization model, which considering these two parameters is proposed. At last, the improved Hungarian algorithm is applied to solve the proposed task scheduling optimization model. Simulation results show that our proposed methods can effectively reduce task processing costs and ensure the critical tasks execute firstly.
AB - With the development of Connected Automated Vehicles (CAV), there will be a long period of mixed driving, which causes the traffic control task at intersections becomes much more complicated. The control tasks would include not only the traffic signal timing optimization but also the computing tasks for different levels of vehicles. Current control task management methods are proved to be inferior to solve this problem. In this paper, we propose an intersection control task scheduling optimization approach based on edge computing. Firstly, the traffic control edge computing system for intersections is constructed. Then, different types of control tasks are listed. Criticality and timeliness are introduced to calculate the control task's dynamic priority. The control task scheduling optimization model, which considering these two parameters is proposed. At last, the improved Hungarian algorithm is applied to solve the proposed task scheduling optimization model. Simulation results show that our proposed methods can effectively reduce task processing costs and ensure the critical tasks execute firstly.
KW - Traffic control
KW - criticality and timeliness
KW - edge Computing
KW - task scheduling optimization
UR - https://www.scopus.com/pages/publications/85127530358
U2 - 10.1109/AINIT54228.2021.00117
DO - 10.1109/AINIT54228.2021.00117
M3 - 会议稿件
AN - SCOPUS:85127530358
T3 - Proceedings - 2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2021
SP - 572
EP - 576
BT - Proceedings - 2021 2nd International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Seminar on Artificial Intelligence, Networking and Information Technology, AINIT 2021
Y2 - 15 October 2021 through 17 October 2021
ER -