An unscented Kalman Filter implementation for fusing lidar and radar sensor measurements.

A software pipeline using the Model Predictive Control method to drive a car around a virtual track.

An extended Kalman Filter implementation in C++ for fusing lidar and radar sensor measurements.

Google Colab Notebooks for Udacity CS344 - Intro to Parallel Programming

C++ implementation of Model Predictive Control(MPC)

This repository contains code and writeups for projects and labs completed as a part of UDACITY's first of it's kind self driving car nanodegree program.

Sensor Fusion and Localization related projects of Udacity's Self-driving Car Nanodegree Program:

Udacity Self-Driving Car Nanodegree - Model Predictive Control (MPC) Project

My path-planning pipeline to navigate a car safely around a virtual highway with other traffic.

Real-time public transport monitor for Berlin

C++/CUDA solutions, slides, and resources for Udacity's Intro to Computer Vision (UD810) course.

Udacity Self-Driving Car Nanodegree - Extended Kalman Filter implementation

🛩️⚙️ 3D Planning, PID Control, Extended Kalman Filter for the Udacity Flying Car Nanodegree // FCND-Term1

RGB-D based SLAM via RTAB-Map

Unscented Kalman Filter (in C++) for Self-Driving Car (AV) Project. Using Sensor Fusion, combines noisy data from Radar and LIDAR sensors on a self-driving car to predict a smooth position for seen objects.

Szilard Bessenyei's submitted projects for the Udacity Self-Driving Car Engineer course.

Create a path planner that is able to navigate a car safely around a virtual highway

Implementation of Kalman Filters - EKF and UKF

Udacity Intro to Self-Driving Cars Nanodegree Project : 2D Histogram Filter in C++

🏎️ Model Predictive Control (MPC) Project using C++, Eigen, Ipopt and CppAD for the Self-Driving Car Nanodegree at Udacity