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Undergraduate Research - Self-Driving Car Model Development
I conducted research for developing machine learning models for a self-driving car, while redesigning the APSC 258 machine learning course for year 2 students.
Industry
Industry
Programming
Programming
Hours
Hours
23
23
Skills
Skills
Simulator, Colab, Python
Simulator, Colab, Python
Simulator, Colab, Python
More Details →
More Details →
Challenge
As part of my undergraduate research, I worked under Professor Jahangir, a leading expert in machine learning, to develop and test models for a self-driving car. My primary focus was creating and refining algorithms for real-time navigation, object detection, and autonomous decision-making. This required an in-depth understanding of computer vision and sensor integration, as the model needed to interpret and respond to various environmental inputs to ensure safe and efficient autonomous driving.
One of the challenges involved achieving high accuracy and reliability across different scenarios, such as varying lighting conditions, obstacle types, and speeds. I iteratively fine-tuned the model, balancing processing speed with detection accuracy to ensure real-time responsiveness in real-world conditions. Additionally, Professor Jahangir entrusted me with redesigning the structure of the APSC 258 machine learning course for second-year engineering students. This required developing hands-on, practical content that would allow students to gain foundational experience with machine learning tools and concepts, giving them a strong base for further study.
Results
The self-driving car model successfully demonstrated reliable real-time navigation and object detection capabilities. It was able to recognize and respond to obstacles, showcasing the robustness and flexibility of the machine learning algorithms in dynamic environments. The redesigned APSC 258 course structure has been implemented, allowing second-year engineering students to gain hands-on experience with machine learning, bridging theoretical knowledge with real-world applications.
This project provided me with practical skills in machine learning, computer vision, and real-time model deployment. Redesigning the course also strengthened my ability to create educational content, combining technical expertise with a clear understanding of student needs. This experience equipped me with a unique blend of research, development, and curriculum design skills, preparing me for advanced applications in autonomous systems.
Challenge
As part of my undergraduate research, I worked under Professor Jahangir, a leading expert in machine learning, to develop and test models for a self-driving car. My primary focus was creating and refining algorithms for real-time navigation, object detection, and autonomous decision-making. This required an in-depth understanding of computer vision and sensor integration, as the model needed to interpret and respond to various environmental inputs to ensure safe and efficient autonomous driving.
One of the challenges involved achieving high accuracy and reliability across different scenarios, such as varying lighting conditions, obstacle types, and speeds. I iteratively fine-tuned the model, balancing processing speed with detection accuracy to ensure real-time responsiveness in real-world conditions. Additionally, Professor Jahangir entrusted me with redesigning the structure of the APSC 258 machine learning course for second-year engineering students. This required developing hands-on, practical content that would allow students to gain foundational experience with machine learning tools and concepts, giving them a strong base for further study.
Results
The self-driving car model successfully demonstrated reliable real-time navigation and object detection capabilities. It was able to recognize and respond to obstacles, showcasing the robustness and flexibility of the machine learning algorithms in dynamic environments. The redesigned APSC 258 course structure has been implemented, allowing second-year engineering students to gain hands-on experience with machine learning, bridging theoretical knowledge with real-world applications.
This project provided me with practical skills in machine learning, computer vision, and real-time model deployment. Redesigning the course also strengthened my ability to create educational content, combining technical expertise with a clear understanding of student needs. This experience equipped me with a unique blend of research, development, and curriculum design skills, preparing me for advanced applications in autonomous systems.
