Masters Theses

Abstract

"Real-time road traffic information is crucial for intelligent transportation systems (ITS) applications, like traffic navigation or emergency response management, but acquiring such data is tremendously challenging in practice because of the high costs and inefficient placement of sensors. Some modern ITS applications contribute to this problem by equipping vehicles with multiple light detection and ranging (LiDAR) sensors, which are expensive and gather data inefficiently; one solution that avoids vehicle-mounted LiDAR acquisition has been to install elevated LiDAR instruments along roadways, but this approach remains unrefined. The eventual development of sixth-generation (6G) wireless communication will enable new, creative solutions to solve these challenges. One new solution is to deploy multiple multirotor unmanned aerial vehicles (UAVs) outfitted with LiDAR sensors (ULiDs) to acquire data remotely. These ULiDs can capture accurate and real-time road traffic information for ITS applications while maximizing the capabilities of LiDAR sensors, which in turn reduces the number of sensors required. Accordingly, this thesis aims to find the optimal 3D placement of multiple ULiDs to maximize road coverage efficiency for ITS purposes. The formulated optimization problem is constrained by unique ULiD specifications, including field-of-view (FoV), point cloud resolution, geographic information system location, and road segment coverage priorities. A computational intelligent algorithm based on particle swarm optimization is proposed to solve the designed optimization problem. Furthermore, this thesis illustrates the benefits of using the proposed algorithm over existing baselines"--Abstract, p. iii

Advisor(s)

Alsharoa, Ahmad

Committee Member(s)

Corns, Steven
Stanley, R. Joe

Department(s)

Electrical and Computer Engineering

Degree Name

M.S. in Electrical and Computer Engineering

Publisher

Missouri University of Science and Technology

Publication Date

Spring 2023

Pagination

ix, 52 pages

Note about bibliography

Includes_bibliographical_references_(pages 44-51)

Rights

© 2023 Zachary Michael Osterwisch, All Rights Reserved

Document Type

Thesis - Open Access

File Type

text

Language

English

Thesis Number

T 12257

Electronic OCLC #

1426306184

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