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Description

Autonomous driving is undoubtedly one of the world's most revolutionary technologies, opening the door to a more secure traffic environment. This innovation has led to vehicles being able to drive by themselves without the necessity of a person behind the wheel, as well as cruise control, lane-keeping assist, and automatic emergency braking. Unfortunately, there is still plenty of work before autonomous driving becomes more popular among drivers. While at UNLV as an undergraduate student/research assistant, one of my goals is to learn how these technologies work to bring ideas into the automotive industry by refining solutions to problems within these mechanisms. As for now, my task has been to explore how an autonomous vehicle functions under trajectory prediction. This is done by creating a dataset for algorithm training and testing it by annotating twenty-second sequences using lidar point cloud data. The goal is to evaluate prediction model performance and visualize its results for a more advanced/precise self-drivable vehicle experience. Ultimately, wouldn't it be plausible to reduce the amount of stress linked with the dangers presented due to how distracted people behave on the road nowadays? Therefore, autonomous driving provides the possibility for public roads to minimize it.

Publisher Location

Las Vegas (Nev.)

Publication Date

Fall 11-22-2024

Publisher

University of Nevada, Las Vegas

Language

English

Keywords

Algorithms; Traffic; Assistance; Driving; Issues

Disciplines

Artificial Intelligence and Robotics | Computer-Aided Engineering and Design

File Format

PDF

File Size

2130 KB

Comments

Mentor: Brendan Morris

Rights

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

Autonomous Driving Trajectory Prediction


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