handcraftedcode1 / CarND-Capstone

Team "Wow That Was Fast!" Capstone Project for the Udacity Self Driving Car Engineering Nanodegree

This repository contains the results of Team "Wow That Was Fast"'s capstone project for the Udacity Self-Driving Car Engineer Nanodegree Program. The project utilizes Ubuntu Linux 14.04 with Robot Operating System (ROS) Indigo and/or Ubuntu Linux 16.04 with ROS Kinetic, the Udacity System Integration Simulator, and code written in C++ and Python to provide a System Integration solution to the self-driving car problem. The code developed will be tested on Udacity's real-world test vehicle (a Lincoln MKZ that the company has named "Carla") during December 2017.

Check out our results in the Udacity simulator:

[]

Team Members

Name	Location	LinkedIn
Kyle Martin (Team Lead)	Phoenix, Arizona	linkedin.com/in/kylemart
Farrukh Ali	Los Angeles, California	linkedin.com/in/farrukhtech
Michael Matthews	Sydney, Australia	linkedin.com/in/michael-matthews-59378933
Daniel Kröhnert	Duesseldorf, Germany	linkedin.com/in/daniel-kröhnert-411235128
Jordan Lee	Tucson, Arizona	linkedin.com/in/TBD

Native Installation

• Be sure that your workstation is running Ubuntu 16.04 Xenial Xerus or Ubuntu 14.04 Trusty Tahir. Ubuntu downloads can be found here.

• If using a Virtual Machine to install Ubuntu, use the following configuration as minimum:

  • 2 CPU
  • 2 GB system memory
  • 25 GB of free hard drive space

  The Udacity provided virtual machine has ROS and Dataspeed DBW already installed, so you can skip the next two steps if you are using this.

• Follow these instructions to install ROS

  • ROS Kinetic if you have Ubuntu 16.04.
  • ROS Indigo if you have Ubuntu 14.04.

• Dataspeed DBW

  • Use this option to install the SDK on a workstation that already has ROS installed: One Line SDK Install (binary)

• Download the Udacity Simulator.

Docker Installation

Install Docker

Build the docker container

docker build . -t capstone

Run the docker file

docker run -p 4567:4567 -v $PWD:/capstone -v /tmp/log:/root/.ros/ --rm -it capstone

Usage

1. Clone the project repository

git clone https://github.com/kylemartin1/CarND-Capstone.git

2. Install python dependencies

cd CarND-Capstone
pip install -r requirements.txt

3. Make and run styx

cd ros
catkin_make
source devel/setup.sh
roslaunch launch/styx.launch

4. Run the simulator

Real world testing

1. Download training bag that was recorded on the Udacity self-driving car (a bag demonstraing the correct predictions in autonomous mode can be found here)
2. Unzip the file

unzip traffic_light_bag_files.zip

3. Play the bag file

rosbag play -l traffic_light_bag_files/loop_with_traffic_light.bag

4. Launch your project in site mode

cd CarND-Capstone/ros
roslaunch launch/site.launch

5. Confirm that traffic light detection works on real life images