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Team East Coast's implementation of the System Integration capstone project in the Udacity Self-Driving Car Nanodegree Program.
Python CMake C++ Other
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.gitignore Merged branch 'master' into branch 'dbw_node'. Sep 17, 2017 docs: Update Team Section with Table Oct 9, 2017
requirements.txt Udacity starter code Sep 7, 2017

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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 Neil
Anthony Sarkis Mountain View, CA Anthony
Cahya Ong Sydney, Australia Cahya
Xianan Huang Ann Arbor, MI Xianan
Yasen Hu Warren, MI Yasen




  • 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

  • Dataspeed DBW

  • Download the Udacity Simulator.


  1. Clone the project repository
git clone
  1. Install python dependencies
cd CarND-Capstone
pip install -r requirements.txt
  1. Make and run styx
cd ros
source devel/
roslaunch launch/styx.launch
  1. 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

  1. Play the bag file
rosbag play -l traffic_light_bag_files/loop_with_traffic_light.bag
  1. Launch your project in site mode
cd CarND-Capstone/ros
roslaunch launch/site.launch
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