This directory contains all the projects I completed for intro to self driving car nano degree offered by updacity.
- Project_0_Joy Ride
- Project_1_2D_Histogram_Filter_in _Python
- Project_2_Implement_a_Matrix_Class
- Project_3_Translate_Python_to_C++
- Project_4_Optimize_Histogram_Filter
- Project_5_Planning_an_Optimal_Path
- Project_6_Trajectory_Visualizer
- Project_7_Image_Classifier_from_Scratch
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.
