Self-Driving Car Engineer Capstone project: Driving Carla!

Team Members

Joseph Januszkiewicz (Team Lead) joe.januszk@gmail.com

Darien Martinez darien.martinez@gmail.com

Alberto Vigata vigata@gmail.com

Moses Gaspard ga_moses@yahoo.com

Original README Below

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

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

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