Term of Award

Spring 2023

Degree Name

Master of Science, Mechanical Engineering

Document Type and Release Option

Thesis (open access)

Copyright Statement / License for Reuse

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Department

Department of Mechanical Engineering

Committee Chair

Valentin Soloiu

Committee Member 1

Marcel Ilie

Committee Member 2

Jinki Kim

Abstract

A major objective on society is to reduce the number of accidents and fatalities on the road for drivers, and pedestrians. Therefore, the automotive engineering field is working on this problem through the development and integration of safety technologies such as advanced driving assistance systems. For this reason, this work was intended to develop and evaluate the performance of different ADAS features and IV technologies under unexpected scenarios. This by the development of safety algorithms applied to the intelligent electric vehicle designed and built in this work, through the use of ADAS sensors based on sensor fusion. Evaluation of AEB, PA, steering by wire, and machine learning based distance predictions, has been studied in this work bringing a contribution to driver safety and the well-being of pedestrians. Based on this work, the enhancement of distance precision of ADAS features with a percentage error of 3.89% compared to average of raw sensors data was found as well as an study of impact of color in LiDAR data quality.

OCLC Number

1404455153

Research Data and Supplementary Material

No

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