/self-driving-car

Udacity Self-Driving Car Engineer Nanodegree

Primary LanguageC++

self-driving-car

In this repository I will share the source code of all the projects of Udacity Self-Driving Car Engineer Nanodegree.

Hope this might be useful to someone! :-)

Overview

Overview
P1: Basic Lane Finding
(code)

Overview
P2: Traffic Signs
(code)

Overview
P3: Behavioral Cloning
(code)

Overview
P4: Adv. Lane Finding
(code)

Overview
P5: Vehicle Detection
(code)

Overview
P6: Ext. Kalman Filter
(code)

Overview
P7: Unsc. Kalman Filter
(code)

Overview
P8: Kidnapped Vehicle
(code)

Overview
P9: PID Controller
(code)

Table of Contents

P1 - Detecting Lane Lines (basic)

  • Summary: Detected highway lane lines on a video stream. Used OpencV image analysis techniques to identify lines, including Hough Transforms and Canny edge detection.
  • Keywords: Computer Vision

P2 - Traffic Sign Classification

  • Summary: Built and trained a deep neural network to classify traffic signs, using TensorFlow. Experimented with different network architectures. Performed image pre-processing and validation to guard against overfitting.
  • Keywords: Deep Learning, TensorFlow, Computer Vision

P3 - Behavioral Cloning

  • Summary: Built and trained a convolutional neural network for end-to-end driving in a simulator, using TensorFlow and Keras. Used optimization techniques such as regularization and dropout to generalize the network for driving on multiple tracks.
  • Keywords: Deep Learning, Keras, Convolutional Neural Networks

P4 - Advanced Lane Finding

  • Summary: Built an advanced lane-finding algorithm using distortion correction, image rectification, color transforms, and gradient thresholding. Identified lane curvature and vehicle displacement. Overcame environmental challenges such as shadows and pavement changes.
  • Keywords: Computer Vision, OpenCV

P5 - Vehicle Detection and Tracking

  • Summary: Created a vehicle detection and tracking pipeline with OpenCV, histogram of oriented gradients (HOG), and support vector machines (SVM). Implemented the same pipeline using a deep network to perform detection. Optimized and evaluated the model on video data from a automotive camera taken during highway driving.
  • Keywords: Computer Vision, Deep Learning, OpenCV