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Cross Platform Training of Neural Networks to Enable Object Identification by Autonomous Vehicles

Abstract Autonomous vehicle technology has been evolving for years since the Automated Highway System Project. However, this technology has been under increased scrutiny ever since an autonomous vehicle killed Elaine Herzberg, who was crossing the street in Tempe, Arizona in March 2018. Recent tests of autonomous vehicles on public roads have faced opposition from nearby residents. Before these vehicles are widely deployed, it is imperative that the general public trusts them. For this, the vehicles must be able to identify objects in their surroundings and demonstrate the ability to follow traffic rules while making decisions with human-like moral integrity when confronted with an ethical dilemma, such as an unavoidable crash that will injure eith... (more)
Created Date 2019
Contributor Sankaramangalam Ulhas, Sangeet (Author) / Berman, Spring (Advisor) / Johnson, Kathryn (Committee member) / Yong, Sze Zheng (Committee member) / Arizona State University (Publisher)
Subject Mechanical engineering / Computer science / Autonomous Vehicles / CARLA / Chartopolis / Object Detection / Self Driving / Transfer Learning
Type Masters Thesis
Extent 69 pages
Language English
Note Masters Thesis Mechanical Engineering 2019
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS

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Description Dissertation/Thesis