View the Project on GitHub MarkBroerkens/CarND-Extended-Kalman-Filter-Project

Udacity - Self-Driving Car NanoDegree

The Project

In this project a kalman filter is used to estimate the state of a moving object of interest with noisy lidar and radar measurements.


This project involves the Term 2 Simulator which can be downloaded here

Required tools are:

This repository includes two files that can be used to set up and install uWebSocketIO for either Linux or Mac systems. For windows you can use either Docker, VMware, or even Windows 10 Bash on Ubuntu to install uWebSocketIO. Please see this concept in the classroom for the required version and installation scripts.

Build and Install

Once the install for uWebSocketIO is complete, the main program can be built and run by doing the following from the project top directory.

  1. mkdir build
  2. cd build
  3. cmake ..
  4. make
  5. ./ExtendedKF

Interaction with the Term 2 Simulator

Here is the main protcol that main.cpp uses for uWebSocketIO in communicating with the simulator.

INPUT: values provided by the simulator to the c++ program

OUTPUT: values provided by the c++ program to the simulator

Code Style

I tried to stick to the Google’s C++ style guide. In order to check the guidelines I installed cpplint using pip install cpplint


The simulator provides noisy lidar and radar measurements which are shown as blue and red dots. The position that is calculated by the kalman filter is displayed as green dots.

The results for dataset 1 are shown in the following image alt text

The results for dataset 2 are shown in the following image alt text

Pro Tips