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 RosRunner

• Brent Wylie - bwylieaudio@gmail.com
• Ioseph Martinez - martinez.pelayo@gmail.com
• Joseph Thompson - joseph.thompson@nxp.com

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

1. Install Docker
2. Build the docker container: bash docker build . -t capstone
3. Run the docker file: bash docker run -p 4567:4567 -v $PWD:/capstone -v /tmp/log:/root/.ros/ --rm -it capstone

Windows 10 Powershell

To prevent issues using opencv2 in Windows 10 Subsystem for Linux (bash ubuntu 16.04 in powershell):

• Enable execution stack for opencv: $> sudo apt install execstack $> sudo execstack -c /opt/ros/kinetic/lib/libopencv_* # NOT: /usr/local/lib/libopencv_*

Usage

1. Clone the project repository:
   $> git clone https://github.com/udacity/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: