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Baz, Dissertation.pdf (2.52 MB)
ETD Abstract Container
Abstract Header
AUTONOMOUS VEHICLE DECISION MAKING AT INTERSECTION USING GAME THEORY
Author Info
BAZ, ABDULLAH
Permalink:
http://rave.ohiolink.edu/etdc/view?acc_num=akron1530541445631072
Abstract Details
Year and Degree
2018, Doctor of Philosophy, University of Akron, Civil Engineering.
Abstract
One of the most critical subjects in Intelligent Transportation System (ITS) nowadays is the autonomous vehicle (AV). It is rapidly improving, and it will have a substantial positive effect on traffic safety and efficiency. Most of auto manufacturer companies and tech industries are spending a lot of money on research for developing autonomous vehicles. AV would have an excellent contribution to managing and controlling intersections. This study introduces a decision-making algorithm for autonomous vehicles at an intersection to optimize the intersection capacity and minimize delay time by using Game Theory mathematical models. This model using vehicle-to-infrastructure (V2I) communication features that will be available in AV so that vehicles are able to communicate with roadside unit (RSU) and with each other to determine which one goes first, depending on different factors such as their speeds and locations, and vehicle size, taking in consideration the safety of the vehicles so we can have collision free intersection. Two different mathematical models were developed; one with %100 autonomous vehicles and the other one is when we have mix traffic, autonomous vehicles, and ordinary vehicles. A simulation model was developed using a standard microscopic simulation platform VISSIM to implement this algorithm. A comparison of the proposed method and two other ordinary intersection control method; traffic lights, and roundabout was made to calculate the total delay of the intersection for each intersection management method. The simulation ran on three different traffic volume, High, moderate, and low volume. Moreover, three different speeds for each traffic volume. The results shows that the proposed system reduces the total delay by more than 65 percent compared with the roundabout, and about 85 percent comparing with a signalized intersection. Another simulation was done for the second scenario, mixed traffic, also a comparison between the proposed methods; roundabout, and the signalized intersection was made for the same cases of various speeds and volume. For model two, results show 30% reduction in delay compared to the roundabout and 89% compared to signalized intersections.
Committee
Ping Yi, Prof. (Advisor)
Yilmaz Sozer, Prof. (Committee Member)
Qindan Huang, Dr. (Committee Member)
Zhe Luo, Dr. (Committee Member)
Jun Ye (Committee Member)
Subject Headings
Civil Engineering
;
Transportation Planning
Keywords
Autonomous Vehicles, Intersection Management, Mixed Traffic, AV intersection control, Autonomous vehicles and ordinary vehicles
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Citations
BAZ, A. (2018).
AUTONOMOUS VEHICLE DECISION MAKING AT INTERSECTION USING GAME THEORY
[Doctoral dissertation, University of Akron]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=akron1530541445631072
APA Style (7th edition)
BAZ, ABDULLAH.
AUTONOMOUS VEHICLE DECISION MAKING AT INTERSECTION USING GAME THEORY .
2018. University of Akron, Doctoral dissertation.
OhioLINK Electronic Theses and Dissertations Center
, http://rave.ohiolink.edu/etdc/view?acc_num=akron1530541445631072.
MLA Style (8th edition)
BAZ, ABDULLAH. "AUTONOMOUS VEHICLE DECISION MAKING AT INTERSECTION USING GAME THEORY ." Doctoral dissertation, University of Akron, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=akron1530541445631072
Chicago Manual of Style (17th edition)
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Document number:
akron1530541445631072
Download Count:
562
Copyright Info
© 2018, some rights reserved.
AUTONOMOUS VEHICLE DECISION MAKING AT INTERSECTION USING GAME THEORY by ABDULLAH BAZ is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by University of Akron and OhioLINK.