Date of Award

6-2025

Degree Name

Master of Science

Department

Mechanical and Aerospace Engineering

First Advisor

Zach Asher, Ph.D.

Second Advisor

Richard Meyer, Ph.D.

Third Advisor

Guan Yue Hong, Ph.D.

Keywords

Autonomous vehicles, engineering requirements, lane detection, resilience engineering, snow-covered roads

Access Setting

Masters Thesis-Open Access

Abstract

This thesis investigates two distinct but interrelated challenges in the development of resilient autonomous vehicle (AV) systems: the formalization of engineering requirements for AV perception subsystems and the enhancement of visual lane detection under snow-covered road conditions. In the first study, field experiments were conducted using a campus-deployed autonomous research vehicle to evaluate the impacts of perception related failures including GPS outages, HD map inconsistencies, and weather interference—on vehicle operation. These findings were used to develop a set of qualitative engineering requirements that promote AV resilience through proactive design. In the second study, a custom snow-focused lane detection dataset was developed and used to evaluate the Cross-Layer Refinement Network (CLRNet) across three backbones: ResNet-18, ResNet-34, and ResNet-101. Results showed that all models achieved strong performance (F1 = 0.90), with detection accuracy declining as snow intensity increased. This confirms the value of domain-aligned datasets and backbone selection in winter conditions. All annotated data and source code will be published online to support future research.

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