This is the project repo for the final project of the Udacity Self-Driving Car Nanodegree: Programming a Real Self-Driving Car. For more information about the project, see the project introduction here.

Team Member submission instructions (non Team Lead and individual submissions)
The submission should include:

All code for your project, submitted via a zipped file or through Github. Please use the same directory structure as the project repo, ensuring that all files are included.
A README file in .txt, .md, or .pdf format. This README should contain the names and Udacity account emails of your team members. For additional resources on creating READMEs or using Markdown, see here and here.
Important: Please leave the Notes to reviewer blank when uploading your submission.

"Running out of fuel" Team members

• Lucas Gago [email protected] [Team leader]
• Karol Majek [email protected]
• Aoi Nakanishi [email protected]
• Hristo Vrigazov [email protected]
• Pachi Cartelle [email protected]

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. Install python dependencies

./ros_entrypoint.sh
cd capstone
pip install -r requirements.txt

2. Make and run styx

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

3. 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