This directory contains all the projects I completed for intro to self driving car nano degree offered by updacity.
Table of Contents
Project0 - Joy Ride
Project_1 - Histogram Filter in Python
Project_2 - Implement a Matrix Class
Project_3 - Translate Python to Cpp
Project_4 - Optimize Histogram Filter
Project_5 - Planning an Optimal Path
Project_6 - Reconstructing Trajectory
Project_7 - Traffic light Classifier
Jump into writing code that controls a simulated vehicle. Send throttle and steering commands to the car to try and get it to navigate around a test track.
In this first project, you will write the sense and move functions for a 2-dimensional histogram filter in Python
In this project you’ll practice using your object oriented programming and matrix math skills by filling out the methods in a partially-completed Matrix class.
In this project you’ll apply your knowledge of C++ syntax by translating the Histogram Filter code from the first course into C++.
A self-driving car can’t afford to waste any cycles or memory unnecessarily. In this project you’ll take some functioning (but inefficient) C++ code and optimize it.
You turn on your self-driving car, buckle up, and enter a destination. Navigating from A → B is not an easy problem. In this project you’ll use your knowledge of data structures (in particular, graph data structures) and search algorithms to write an algorithm which uses a map and traffic information to find the quickest route between two points.
Use raw acceleration, displacement, and angular rotation data from a vehicle's accelerometer, odometer, and rate gyros to reconstruct a vehicle's X, Y trajectory.
Buid a classification pipleline that takes an image of a traffic and outputs a label that classifies the image as a :red, green, or yellow traffic.