Team: Millenium Vulkan

This is the capstone project for Udacity's Self Driving Car Nanodegree. This code is aimed to run in an actual physical self-drive vehicle. It is the final project of the nanodegree and solved in team work.

Team Members

This repository is maintained by the following:

• George Terzakis: terzakig@hotmail.com
• Martin Herzog: martin.mh.herzog@gmail.com
• Yuda Liu: liu_yuda@163.com
• Atul Acharya: Atul.acharya@gmail.com
• Yoni Azuelos: yonazuel@hotmail.com

The following video shows the code in action:

Usage

1. Clone the project repository

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

2. Clone the team's submodule

cd CarND-Capstone
git submodule init
git submodule update

3. Make sure that the submodule contains the latest updates

git pull origin master

4. Install python dependencies

pip install -r requirements.txt

5. Make and run styx

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

6. Run the simulator

Specification

The team's specification can be found here.

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 127.0.0.1:4567:4567 -v $PWD:/capstone -v /tmp/log:/root/.ros/ --rm -it capstone

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