Team It's Not Rocket Science Capstone Project

Our team members:

• Will Rifenburgh (wmrifenburgh@gmail.com)
• Tom Jacobs (hatsmagee@gmail.com)
• Michael Person(mp.michael.person@gmail.com )
• Sandor Pogacsas (sandor.pogacsas@gmail.com)
• William Tan (tanyt81@gmail.com)

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.

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

GPU/CUDA support

Install nvidia-docker

Build the docker container

nvidia-docker build . -f Dockerfile.gpu -t capstone-gpu

run-cuda script

You can simply start the container or attach to it using the run-cuda or run-devel-cuda script

./run-cuda.sh

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