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Department of Electrical Engineering and Computer Science
Autonomous Robotics and Control Laboratory
1. Journal Articles
Autonomous and Connected Intersection Crossing Traffic Management using Discrete-Time Occupancies Trajectory
Lu, Qiang
;
Kim, Kyoung Dae
Department of Electrical Engineering and Computer Science
Autonomous Robotics and Control Laboratory
1. Journal Articles
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Title
Autonomous and Connected Intersection Crossing Traffic Management using Discrete-Time Occupancies Trajectory
DGIST Authors
Lu, Qiang
;
Kim, Kyoung Dae
Issued Date
2019-05
Citation
Lu, Qiang. (2019-05). Autonomous and Connected Intersection Crossing Traffic Management using Discrete-Time Occupancies Trajectory. doi: 10.1007/s10489-018-1357-1
Type
Article
Article Type
Article
Author Keywords
Autonomous vehicles
;
Computational complexity
;
Discrete-time occupancies trajectory (DTOT)
;
Intelligent intersection management
Keywords
MODEL
ISSN
0924-669X
Abstract
In this paper, we address the problem of safe and efficient intersection crossing traffic management of autonomous and connected ground traffic. Toward this objective, we propose an algorithm called the discrete-time occupancies trajectory based intersection traffic coordination algorithm (DICA). We show that the basic DICA has a computational complexity of O(n2Lm3) where n is the number of vehicles granted to cross an intersection and Lm is the maximum length of intersection crossing routes. To improve the overall computational efficiency of the algorithm, the basic DICA is enhanced by several computational approaches that are proposed in this paper. The enhanced algorithm has the computational complexity of O(n2Lmlog2Lm). The improved computational efficiency of the enhanced algorithm is validated through simulations using an open source traffic simulator called the simulation of urban mobility (SUMO). The overall throughput, as well as the computational efficiency of the enhanced algorithm, are also compared with those of an optimized traffic light control algorithm. © 2018, Springer Science+Business Media, LLC, part of Springer Nature.
URI
http://hdl.handle.net/20.500.11750/9510
https://arxiv.org/abs/1705.05231
DOI
10.1007/s10489-018-1357-1
Publisher
Springer New York LLC
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