Build your Own Self Driving Car| [Course 1 & Course 2]

Course Curriculum

Chapter 1: Introduction

Lecture 1: Course Curriculum

Lecture 2: Detailed Working

Chapter 2: Build Hardware for Self Driving Car

Lecture 1: Hardware Requirements (hardware Link is provided in Resource Section)

Lecture 2: Assemble Hardware Parts (Robot Chassis)

Lecture 3: How To Build Track for Testing

Chapter 3: Slave Device Setup (Arduino UNO)

Lecture 1: Forward & Backward Functions for Motors

Lecture 2: Left & Right Functions for Motors

Chapter 4: Master Device Setup (Raspberry PI 3 B+)

Lecture 1: How to Flash Raspbian OS on Raspberry Pi 3 B+

Lecture 2: Raspbian Buster Fix

Lecture 3: Connect Raspberry PI to Personal Computer through Ethernet

Lecture 4: Connect Raspberry PI to Personal Computer through WiFi

Lecture 5: Connect Raspberry PI to Personal Computer through VNC Viewer

Chapter 5: Install OpenCV4 on Raspberry PI 3 B+

Lecture 1: Introduction to OpenCV

Lecture 2: Remove Unnecessary Software from Raspberry PI

Lecture 3: Clone OpenCV from GitHub

Lecture 4: Build OpenCV on Raspberry PI with CMake

Lecture 5: Setting Up Libraries in Programming Editor

Lecture 6: Test First Program In Geany Programming Editor

Lecture 7: SD CARD BACKUP

Chapter 6: Camera Setup for Raspberry PI

Lecture 1: Install Raspicam & Wiring PI Libraries on Raspberry PI

Lecture 2: Mount Camera on Robot Car Chassis

Lecture 3: Backup of SD Card

Chapter 7: C++ Code to Capture Images & Videos

Lecture 1: How to Initialize Camera

Lecture 2: C++ Code to Capture Images

Lecture 3: C++ Code to Capture Video

Lecture 4: calculate FPS (Frames Per Second)

Chapter 8: Image Processing Using OpenCV4 & C++

Lecture 1: Convert Image Signature

Lecture 2: Create Region Of Interest

Lecture 3: Perspective Transformation (Bird Eye View)

Lecture 4: Threshold Operations

Lecture 5: Canny Edge Detection

Lecture 6: Troubleshoot Hardware & Software

Lecture 7: How to Find Lanes from Track

Lecture 8: Histogram and Vectors

Lecture 9: Iterators and Pointers

Lecture 10: Calibration

Lecture 11: Final Step

Chapter 9: Master & Slave Device Communication

Lecture 1: Raspberry PI Digital Pins

Lecture 2: Wiring Pi Library Fix (download latest command list in resource)

Lecture 3: Slave Device (Arduino Uno) Programming

Lecture 4: Testing

Lecture 5: Smooth Performance Tweek

Chapter 10: Final Testing & Features (Image Processing)

Lecture 1: Testing on Large Track

Lecture 2: Lane End & UTurn Implementation (Main Device)

Lecture 3: Lane End & UTurn Implementation (Slave Device)

Chapter 11: Introduction to Machine Learning

Lecture 1: Basic Steps & Terminologies

Chapter 12: (Stop Sign) Neural Network Training

Lecture 1: Creating Stop sign

Lecture 2: C++ code to Capture & Save Images

Lecture 3: Capturing Positive Samples for Stop sign

Lecture 4: Capturing Negative Samples

Lecture 5: Cascade Training Software and Image Cropping

Lecture 6: Training of Haar Cascade Model for Stop Sign

Chapter 13: (Stop Sign) Detection on Raspberry Pi3

Lecture 1: Load (.xml) file in C++ Code

Lecture 2: Writing Image Classifier Program in C++

Lecture 3: Stop Sign Detection Testing

Lecture 4: Create Linear Equations to Calculate Distance

Lecture 5: Solve Linear Equations & Distance Testing

Chapter 14: Stop Sign Detection Testing

Lecture 1: C++ Programming in Raspberry Pi

Lecture 2: C++ Programming in Arduino UNO

Lecture 3: Final Testing (Stop Sign)

Chapter 15: (Obstacle) Neural Network Training

Lecture 1: Positive Sample for Object

Lecture 2: Extracting Positive samples for Object

Lecture 3: Cascade Training for Object Detection

Chapter 16: Obstacle Detection on Raspberry Pi3

Lecture 1: C++ Code to Detect Object

Lecture 2: Create Linear Equations to Calculate Distance (for Object)

Lecture 3: Solve Linear Equations & Distance Testing (for object)

Chapter 17: Obstacle Detection Testing

Lecture 1: Arduino Programming

Lecture 2: Lane Change Operation at object Detection

Lecture 3: Final Testing (Object)

Chapter 18: Traffic Light Training

Lecture 1: Traffic Light Model

Lecture 2: Positive Sample for Red Light

Lecture 3: Negative Sample for Red Light

Lecture 4: Training Data

Lecture 5: Cascade Model for Red Light

Chapter 19: Traffic Light Detection

Lecture 1: Load (.xml) file in C++ Code

Lecture 2: Linear Equations With Calibration

Lecture 3: Finding Actual Distance

Chapter 20: Traffic Light Testing

Lecture 1: Arduino Programming & Final Testing

Chapter 21: LED Dynamic Turn Signal Indicator

Lecture 1: Schematic Diagram