valleyceo / carnd-system-integration

The Hacker Force SDCND Capstone Repository

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	Contact	Location
Eric Kim	[e-mail]	Santa Clara, CA
Don MacMillen	[e-mail]	San Mateo, CA
Grigory Makarevich	[e-mail]	Seattle, WA
Stefan Gantner	[e-mail]	Munich, Germany
Karsten Schwinne	[e-mail]	Dortmund, Germany

Installation Guide

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 and run

docker build . -t capstone
./run.sh

NVIDIA-Docker Installation

Install NVIDIA-Docker

Build and run

nvidia-docker build . -f Dockerfile.gpu -t capstone-gpu
./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
rosm (=catkin_make && source devel/setup.sh)

# for highway simulation
rosl (=roslaunch launch/styx.launch)

# for test lot simulation
rosl2 (=roslaunch launch/church.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