Intro to Self Driving Cars

This nanodegree is more focusd towards practical implementation of the self driving framworks. I have learned to apply control techniques combined with motion planning and computer vision.

Bayesian Thinking and probability

A mathematical introduction of probability and baye's theorm based on which self driving rejects errors in their sensors.

Project0: Joy Ride

This project is for simulation environment introduction from udacity. This helps to understand dynamics with Unity engine based simulation in chrome.

Project 1: Implementation of histogram filter

This project is basically the probabilty visualization after car moves in particular direction. This is a jupyter notebook homework.

Project 2: Matrix class implementation and Kalman filter

Built a python file that allows user to do addition,subtraction,multiplication,transpose and inverse like normal matrix.This has allowed to predict next states of vehicle using Kalman Filter.

Project 3:Histogram Filter(C++)

This is the project where I have to convert the python files from histogram project to c++ with high performance.

Project 4:Implementaing A*

Implemented A* algorithm used in the Google maps and verified results with trajectory generated.

Project 5:Reconstructing Trajectories from Sensor data

A notebook file to merge sensor data from various sensor to reduce errors and make sense out of it.

Project 6: Traffic light classifier

This is the computer vision project to classify a traffic sign with color prediction where I got 99% accuracy.

Godcreatebugs/Udacity-Intro-to-Self-Driving-Car