Example city simulation for autonomous vehicles in Gazebo Classic.

Second Project of the Udacity Self-Driving Car Nanodegree Program

Traffic Sign Recognition for autonomous vehicles applications using ROS to control a turtlebot.

Deep neural networks and convolutional neural networks to classify German traffic signs.

Visualise your data in virtual reality with ImmersivePoints

📐 Personal GitHub web page. Based on the minimal-mistakes Jekyll theme.

A website documenting my self-driving RC car project/Master's thesis and my ML/DL cookbooks. Built with Hugo, Grav and the Learn theme. Contains video demos, code, schematics, 3D models and Baby Yoda pics drawn by Van Gogh and Picasso.

Udacity Self Driving Car Nanodegree - Finding Lane Lines in a Video Stream

Learning resources for autonomous robot and self-driving vehicle systems

2D/ 3D object detection, segmentation, depth estimation for self-driving car

German Traffic Sign Classification Project for Self-Driving Car Nano Degree Term 1. A CNN is designed and trained to detect the traffic signs using the German Traffic Sign Dataset. The system is also tested on German traffic signs to measure its performance.

A Flask Application deployed over Google Cloud Platfrom to perform Advanced Lane Detection on Road Images which includes advanced image processing to detect lanes irrespective of the road texture, brightness, contrast, curves etc. Used Image warping and sliding window approach to find and plot the lane lines.

P4-Advanced-Lane-Finding

software pipeline to identify the lane boundaries and cars in a video for autonomous self-driving cars

Deep Neural Network for behavior cloning on Udacity SDC simulator

VIA Project - 3D-printed self-driving car. This repo contains our website and project documentation.

Build a Convolutional Neural Network (CNN) that recognizes traffic signs.

Real-Time Deep Learning Inference for End-To-End Self-Driving Cars

This is a computer vision project for solving the problem of lane detection in autonomous driving vehicles. The project uses simple thresholding based techniques in L*a*b color space. Programming has been done in C++ using OpenCV library.