Introduction

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 Mail
Emin Oguz Inci (Team Leader)	eoguzinci@gmail.com
Nick Caldwell	nick.caldwell94@gmail.com
Tim Papenfuss	tim_papenfuss@yahoo.com
MingSheng Xu	xums.cn@qq.com
Aleksei Shpilman	alexey@shpilman.com

Documentation

This readme explains the installation and usage steps.

Documentation for the implemented Traffic Light Detection and Classification methods can be found here.

Documentation for the implemented Waypoint Updater methods can be found here.

Documentation for the implemented Drive-by-wire method can be found here.

Installation

Please use one of the two installation options, either native or docker installation.

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

Port Forwarding

To set up port forwarding, please refer to the instructions from term 2

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.
2. Unzip the file

unzip traffic_light_bag_file.zip

3. Play the bag file

rosbag play -l traffic_light_bag_file/traffic_light_training.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

Workspace Setup

If you are using the Udacity workspace environment, follow these steps:

git clone https://github.com/eoguzinci/autonomous_ros
apt-get update
cd autonomous_ros
pip install -r requirements.txt
apt-get install ros-kinetic-dbw-mkz
pip install --upgrade catkin_pkg_modules
cd ros
catkin_make
source devel/setup.sh
roslaunch launch/styx.launch

or:

git clone https://github.com/eoguzinci/autonomous_ros
cd autonomous_ros
chmod +x workspace.sh
./workspace.sh

Note This project might not run as intended on the Udacity workspace environment since the hardware available for the workspace somewhat lacks in computational power.