Term of Award
Master of Science, Mechanical Engineering
Document Type and Release Option
Thesis (open access)
Copyright Statement / License for Reuse
This work is licensed under a Creative Commons Attribution 4.0 License.
Department of Mechanical Engineering
Committee Member 1
Committee Member 2
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.
Obando Ortegon, David S., "Enhancing Traffic Safety in Unpredicted Environments with Integration of ADAS Features with Sensor Fusion in Intelligent Electric Vehicle Platform with Implementation of Environmental Mapping Technology" (2023). Electronic Theses and Dissertations. 2542.
Research Data and Supplementary Material
Controls and Control Theory Commons, Electrical and Electronics Commons, Electro-Mechanical Systems Commons, Hardware Systems Commons, Navigation, Guidance, Control, and Dynamics Commons, Power and Energy Commons, Signal Processing Commons