GitHub: https://github.com/Alzaib/Autonomous-Self-Driving-Car-GTA-5

Introduction and Project Background

Artificial intelligence is now widely being used to perform several tasks that were previously performed by a human. An artificially created network can be trained to autonomously drive a vehicle, without any human intervention, creating a safer driving environment. These vehicles are equipped with different hardware such as camera, radar, and LIDAR, to collect the data in real-time. The data is then passed into complex neural networks to predict outcomes and control the car. Such networks can be built, train, and tested in a 3D realistic environment, without putting anyone on the risk. A simple self-driving vehicle…


GitHub: https://github.com/Alzaib/FlappyBird_TivaC

Members

Alzaib Karovalia:

LinkedIn
GitHub

Syed Salman:

LinkedIn
GitHub
Portfolio

Peter Kwan:

LinkedIn
GitHub

Project Motivation

The purpose of this project is to understand and practice integrating various hardware, and microcontroller features. Designing a mini video game is the perfect example to perform it usually requires utilizing a lot of features such as button, screen display, analog-digital converter, interrupts, ram registers, and more.

Our objective is to practice the content we learned in MSE450: Real-time embedded control system and have decided that Flappy Bird is the best to perform this as it includes an algorithm to generate poles, user input, output, score storage, ADC usage, and more.

Design of Approach / Methodology


GitHub: https://github.com/Alzaib/Traffic-Signs-Detection-Tensorflow-YOLOv3-YOLOv4

Real-time Detection and Classification using YOLOv4-Tiny

Introduction

Traffic detection and classification is one of the important steps toward building a self-driving vehicle or intelligent autonomous vehicle. It is also important that such detection algorithms must be deployed on embedded computers such as in cars. There are two primary tasks for any recognition system, detection (finding the location and size of the object on the input image), and classification (classifying the detected objects into subclasses). Both tasks are usually done with a single detection/classification model such as YOLO or SSD, where input images are labelled with the bounding boxes and respective classes. However, labelling and training…

Alzaib Karovalia

Mechatronic Systems Engineer

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