Theses and Dissertations

Issuing Body

Mississippi State University

Advisor

Ball, John E.

Committee Member

Follet, Randolph F.

Committee Member

Luo, Chaomin

Date of Degree

5-13-2022

Document Type

Graduate Thesis - Open Access

Major

Electrical and Computer Engineering

Degree Name

Master of Science (M.S.)

College

James Worth Bagley College of Engineering

Department

Department of Electrical and Computer Engineering

Abstract

The autonomous driving (AD) industry is advancing at a rapid pace. New sensing technology for tracking vehicles, controlling vehicle behavior, and communicating with infrastructure are being added to commercial vehicles. These new automotive technologies reduce on road fatalities, improve ride quality, and improve vehicle fuel economy. This research explores two types of automotive sensor fusion systems: a novel radar/camera sensor fusion system using a long shortterm memory (LSTM) neural network (NN) to perform data fusion improving tracking capabilities in a simulated environment and a traditional radar/camera sensor fusion system that is deployed in Mississippi State’s entry in the EcoCAR Mobility Challenge (2019 Chevrolet Blazer) for an adaptive cruise control system (ACC) which functions in on-road applications. Along with vehicles, pedestrians, and cyclists, the sensor fusion system deployed in the 2019 Chevrolet Blazer uses vehicle-to-everything (V2X) communication to communicate with infrastructure such as traffic lights to optimize and autonomously control vehicle acceleration through a connected corridor

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