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Cooperative Automated Vehicle Movement Optimization at Uncontrolled

Intersections using Distributed Multi-Agent System Modeling

Abdallah A. Hassan Mahmoud

Dissertation submitted to the faculty of the Virginia Polytechnic Institute and State University in

partial fulfillment of the requirements for the degree of

Doctor of Philosophy

In

Computer Engineering

Hesham A. Rakha, (Chair)

Cameron Patterson

Jung-Min Park

Haibo Zeng

Hao Chen

Dec 14th 2016

Blacksburg, Virginia

Keywords: ITS, Automated Vehicles, Uncontrolled Intersections, Networked Intersections,

Multi-agent Systems, Distributed Systems, Intelligent Agent Cooperation

Copyright 2016, Abdallah A. Hassan Mahmoud

Cooperative Automated Vehicle Movement Optimization at Uncontrolled

Intersections using Distributed Multi-Agent System Modeling

Abdallah A. Hassan Mahmoud

ABSTRACT

Optimizing connected automated vehicle movements through roadway intersections is a

challenging problem. Traditional traffic control strategies, such as traffic signals are not optimal,

especially for heavy traffic. Alternatively, centralized automated vehicle control strategies are

costly and not scalable given that the ability of a central controller to track and schedule the

movement of hundreds of vehicles in real-time is highly questionable. In this research, a series of

fully distributed heuristic algorithms are proposed where vehicles in the vicinity of an intersection

continuously cooperate with each other to develop a schedule that allows them to safely proceed

through the intersection while incurring minimum delays. An algorithm is proposed for the case

of an isolated intersection then a number of algorithms are proposed for a network of intersections

where neighboring intersections communicate directly or indirectly to help the distributed control

at each intersection makes a better estimation of traffic in the whole network. An algorithm based

on the Godunov scheme outperformed optimized signalized control. The simulated experiments

show significant reductions in the average delay.

The base algorithm is successfully added to the INTEGRATION micro-simulation model

and the results demonstrate improvements in delay, fuel consumption, and emissions when

compared to roundabout, signalized, and stop sign controlled intersections. The study also shows

the capability of the proposed technique to favor emergency vehicles, producing significant

increases in mobility with minimum delays to the other vehicles in the network.

Cooperative Automated Vehicle Movement Optimization at Uncontrolled

Intersections using Distributed Multi-Agent System Modeling

Abdallah A. Hassan Mahmoud

GENERAL AUDIENCE ABSTRACT

Intelligent self-driving cars are getting much closer to reality than fiction. Technological advances

make it feasible to produce such vehicles at low affordable cost. This type of vehicles is also

promising to significantly reduce car accidents saving people lives and health. Moreover, the

congested roads in cities and metropolitan areas especially at rush hours can benefit from this

technology to avoid or at least to reduce the delays experienced by car passengers during their

trips.

One major challenge facing the operation of an intelligent self-driving car is how to pass

an intersection as fast as possible without any collision with cars approaching from other directions

of the intersection. The use of current traffic lights or stop signs is not the best choice to make the

best use of the capabilities of future cars.

In this dissertation, the aim is to study and propose ways to make sure the future

intersections are ready for such self-driving intelligent cars. Assuming that an intersection has no

type of traditional controls such as traffic lights or stop signs, this research effort shows how

vehicles can pass safely with minimum waiting. The proposed techniques focus on providing low-

cost solutions that do not require installation of expensive devices at intersections that makes it

difficult to be approved by authorities. The proposed techniques can be applied to intersections of

various sizes.

The algorithms in this dissertation carefully design a way for vehicles in a network of

intersections to communicate and cooperate while passing an intersection. The algorithms are

extensively compared to the case of using traffic lights, stop signs, and roundabouts. Results show

significant improvement in delay reduction and fuel consumption when the proposed techniques

are used.

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ACKNOWLEDGEMENTS

The research effort presented in this dissertation is funded by the Connected Vehicle Initiative

University Transportation Center (CVI-UTC). This work could not have been completed without

the help of many people in Virginia Tech and Virginia Tech Transportation Institute especially in

the center for sustainable mobility.

I am so grateful to Prof. Hesham Rakha who advised this research for his generous support

throughout the course of this research. I acknowledge the help provided by Yousef Bichiou in the

MATLAB implementation of the vehicle dynamics model and the implementation of the Godunov

scheme equations. I also acknowledge the help provided by Hao Yang in implementing the DIVC

system in the INTEGRATION model. I am also grateful to Mohamed Almannaa for the help with

the simulation experiments of the optimized signalized control in INTEGRATION. I am also

grateful to Mohammed Elhenawy for his help with the statistical analysis and interpretation of

results and for his help while I was preparing some of the figures of this dissertation. I am also

grateful to Ahmed Roman for help with some of the mathematical formulation used in this

dissertation.

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Table of Contents

Chapter 1 ......................................................................................................................................... 1

Introduction and Motivation ........................................................................................................... 1

1.1 Challenges for Intelligent Transportation Systems ............................................................... 1

1.2 Automated Vehicles .............................................................................................................. 2

1.3 Vehicle-To-Vehicle Communication .................................................................................... 4

1.4 Problem Complexity ............................................................................................................. 5

Chapter 2 ......................................................................................................................................... 7

Related Work .................................................................................................................................. 7

2.1 Introduction ........................................................................................................................... 7

2.2 Centralized Methods ............................................................................................................. 7

2.3 Distributed Methods ............................................................................................................ 11

2.4 Multi-intersection Methods ................................................................................................. 13

2.5 Adaptive Signal Control ...................................................................................................... 14

Chapter 3 ....................................................................................................................................... 15

A Fully-Distributed Heuristic Algorithm for Control of Automated Vehicle Movements at

Isolated Intersections .................................................................................................................... 15

Abstract ..................................................................................................................................... 15

3.1 Introduction ......................................................................................................................... 16

3.2 Related Work....................................................................................................................... 18

3.3 Proposed Algorithm ............................................................................................................ 21

3.3.1 Overview ...................................................................................................................... 21

3.3.2 Modeling Agent States ................................................................................................. 24

3.3.3 Scheduling Algorithm................................................................................................... 26

3.3.4 Communication Requirements ................................................................................