Advanced lane detection using computer vision

Behaviour Cloning - Use Deep Learning to Clone Driving Behaviour. This project involves the development of a five layer Convolutional Neural Network (CNN) pipeline for a self driving car in simulated tracks to be able to learn and mimic driving behaviour of a human (myself).

Creates a Docker image with all the prerequisites needed to run the projects of the Udacity Self-Driving Car Nanodegree.

This repo contains dataset for Udacity CarND final project and also contains keras implementation of a simple neural-net.

Udacity Self Driving Car Engineer Nanodegree

Detecting road lane lines in pictures and videos using OpenCV and Python. (Project 1 of Udacity's Self Driving Car Engineer)

Self-Driving Car Nanodegree Program Starter Code for the Unscented Kalman Filter Project

Udacity CarND Simple Lane Finding Project

Vehicle Detection and Tracking using traditional computer vision and machine learning techniques such as Histogram of Oriented Gradients (HOG) and Support Vector Machines (SVM).

Semantic Image Segmentation - using deep learning and computer vision to identify classes of objects in image. For Self Driving Cars.

Udacity Self-Driving Car Engineer Nanodegree - Term1 Project 3 (Behavior Cloning)

Identify and track lane lines from a vehicle camera stream using computer vision techniques. The vision pipeline consists of camera calibration for distortion correction, perspective transformation, colour and sobel edge masking, lane position tracking by a histogram and windowing technique, polynomial line fitting, and finally inverse transform.

Udacity CarND Path Planning Project

Projects completed in Udacity Self-Driving Car Nanodegree

Udacity CarND Model Predictive Control Project

Udacity CarND Kidnapped Vehicle Project

Udacity CarND PID Controller Project

Udacity Self Driving Car Nanodegree Project 2 - Traffic Sign Classifier

Classify Traffic Signs.