Award Date

December 2023

Degree Type

Thesis

Degree Name

Master of Science (MS)

Department

Electrical and Computer Engineering

First Committee Member

Brendan Morris

Second Committee Member

Venkatesan Muthukumar

Third Committee Member

Mei Yang

Fourth Committee Member

Shaikh Arifuzzaman

Number of Pages

93

Abstract

The integration of Advanced Driving Assistance Systems (ADAS) and autonomous driving functionalities into contemporary vehicles has notably surged, driven by the remarkable progress in artificial intelligence (AI). These AI systems, capable of learning from real-world data, now exhibit the capability to perceive their surroundings via a suite of sensors, create optimal routes from source to destination, and execute vehicle control akin to a human driver.

Within the context of this thesis, we undertake a comprehensive exploration of three distinct yet interrelated ADAS and Autonomy projects. Our central objective is the implementation of autonomous driving(AD) technology at UNLV campus, culminating in the introduction of novel enhancements to essential ADAS modules. These innovations encompass a longitudinal control system, an augmented perception system, and a lane detection model. Finally, a full-scale implementation of all modules, specifically tailored for the UNLV campus environment.

The first project involved setting up an emergency braking system using 3D object detection based on monocular vision. We design a simple longitudinal PD controller that considers how close pedestrians are and triggers the brakes, which the car’s drive-by-wire system uses to control the brakes. We also augment autonomous car perception systems by eliminating a blind spot created by roadside obstacles like walls utilizing an infrastructure camera and a trajectory prediction model.

For our second project, we focus on creating a lane detection and classification model that works effectively on the challenging streets of Las Vegas. These streets pose particular challenges for existing deep learning lane detection models, characterized by suboptimal lane-to-road contrast, sparse lane markings, and extreme lighting scenarios. To overcome these obstacles, we collect data from intricate scenarios, fine-tune several advanced models, and develop a model that works better on these unique roads. We also add a lane classification branch that provides lane marking types like solid or dashed. Besides, we investigate the effect of mixed-precision technique in reducing the inference time.

In our final project, we bring self-driving technology to the UNLV campus. We employ the Autoware Universe software stack and adapt it to our specific sensor setup and vehicle interface. Our platform relies on HD maps for localization and navigation. It uses a 3D LiDAR object detection model for pedestrians, vehicles, and other dynamic objects. Finally, it uses a model predictive controller to implement the generated motion command. Our equipped vehicle can navigate autonomously within the campus while adhering to traffic rules.

Keywords

Autonomous Systems; Computer vision; Deep Learning; Machine Learning; ROS; Self-driving vehicles

Disciplines

Artificial Intelligence and Robotics | Computer Engineering | Electrical and Computer Engineering | Robotics

File Format

pdf

File Size

21650 KB

Degree Grantor

University of Nevada, Las Vegas

Language

English

Rights

IN COPYRIGHT. For more information about this rights statement, please visit http://rightsstatements.org/vocab/InC/1.0/

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