Self-Driving Car Nanodegree Capstone Project

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 Members

Name	Udacity account email
Carlos Galvez (Team Lead)	carlosgalvezp (at) gmail.com
Marko Sarkanj	marko.sarkanj (at) gmail.com
Oren Meiri	oren.meiri (at) gmail.com
Ivan Danov	ivan.geo.danov (at) gmail.com

In-car video

Simulator Video

Quick setup using Docker

Follow these steps to setup the development/runtime environment without having to install any dependencies, except for Docker. NOTE: currently supported only on Linux and OSX.

• Install dependencies:

  • docker.
  • nvidia-docker. Only supported on Ubuntu, to get GPU acceleration.

• Build and run code on simulator (CPU only):

  $ ./run.py
  

• Build and run code on simulator, with GPU acceleration (Ubuntu only):

  $ ./run.py --gpu
  

• Build and run code using a rosbag:

  $ ./run.py --rosbag <path_to_rosbag>
  

• Build and run code on Carla (GPU always enabled, Ubuntu only):

  $ ./run.py --carla
  

Native installation

Follow these steps to have a native installation (only supported on Ubuntu).

• 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.

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