dikar / self-driving-car

Udacity Self-Driving Car Engineer Nanodegree

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

Projects

P1: Basic Lane Finding (code)	P2: Traffic Signs (code)	P3: Behavioral Cloning (code)	P4: Adv. Lane Finding (code)
P5: Vehicle Detection (code)	P6: Ext. Kalman Filter (code)	P7: Unsc. Kalman Filter (code)	P8: Kidnapped Vehicle (code)
P9: PID Controller (code)	P10: MPC Controller (code)	P11: Path Planning (code)	P11: Road Segmentation (code)

Capstone

• Traffic light classifier: Simple traffic light classifier to be integrated in the capstone project.

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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