This repository contains the results of Team East Coast's capstone project for the Udacity Self-Driving Car Engineer Nanodegree Program. The project utilizes Ubuntu Linux 14.04 with Robot Operating System (ROS) Indigo and/or Ubuntu Linux 16.04 with ROS Kinetic, the Udacity System Integration Simulator, and code written in C++ and Python to provide a System Integration solution to the self-driving car problem. The code developed will be tested on Udacity's real-world test vehicle (a Lincoln MKZ that the company has named "Carla") during the month of October. More information on the results of those tests will be made available in the Updates section of this README.

For now, check out our latest Udacity simulator results on YouTube:

Team Members

Name	Location	LinkedIn
Neil Hiddink (Team Lead)	Dearborn, MI	linkedin.com/in/neilhiddink
Anthony Sarkis	Mountain View, CA	linkedin.com/in/anthonysarkis
Cahya Ong	Sydney, Australia	linkedin.com/in/cahyaong
Xianan Huang	Ann Arbor, MI	linkedin.com/in/xianan-huang
Yasen Hu	Warren, MI	linkedin.com/in/yasenhu

Updates

COMING SOON - STAY TUNED!

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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.

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

Be sure to follow these steps prior to running the traffic light detection model:

• cd \ros\src\tl_detector\light_classification
• mkdir graphs
• download and unzip model weights (Simulator)
• download and unzip model weights (Real)
• download label_map.pbtxt
• in \ros\src\tl_detector\light_classification change the flag in line 21, self.simulation to "False" for real world testing. Default model will work in both, but real trained one will work better.

1. Download training bag that was recorded on the Udacity self-driving car

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