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Development of Detection and Tracking Systems for Autonomous Vehicles Using Machine Learning.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Development of Detection and Tracking Systems for Autonomous Vehicles Using Machine Learning./
Author:	Ward, Tyler.
Description:	1 online resource (76 pages)
Notes:	Source: Masters Abstracts International, Volume: 84-11.
Contained By:	Masters Abstracts International84-11.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9798379443221

Development of Detection and Tracking Systems for Autonomous Vehicles Using Machine Learning.
Ward, Tyler.

Development of Detection and Tracking Systems for Autonomous Vehicles Using Machine Learning. - 1 online resource (76 pages)

Source: Masters Abstracts International, Volume: 84-11.

Thesis (M.S.)--Morehead State University, 2023.

Includes bibliographical references

Human activity recognition and prediction systems are crucial to the safety of autonomous vehicles. While much research has been conducted to improve these systems, very little has been done to address the important task of differentiating between adult and child pedestrians. Failure to correctly identify the type of pedestrian can lead to accidents. In this thesis, a novel multiple object tracking system for autonomous vehicles is proposed that overcomes the challenges of differentiating between adult and child pedestrians. To increase the system's robustness, it is also capable of identifying and tracking 51 different animal types that are commonly encountered on roads around the world. The proposed system uses modern machine learning methods for object detection and tracking to identify the type of pedestrian or animal, and also measure various characteristics of their behavior, such as speed and trajectory. Experimental results indicate effectiveness in accomplishing these tasks, demonstrating the potential of the multiple object tracking system to improve the safety and performance of autonomous vehicles.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379443221Subjects--Topical Terms:

573171
Computer science.
Subjects--Index Terms:

Autonomous vehiclesIndex Terms--Genre/Form:

554714
Electronic books.

Development of Detection and Tracking Systems for Autonomous Vehicles Using Machine Learning.
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