Project: System Integration

Overview

This is the final project of the Udacity Self-Driving Car Nanodegree: Programming a Real Self-Driving Car. In this project we are required to program a Self-Driving Car (Simulation) to work around a lap consisting of Traffic Lights.

Getting Started

The project has been developed on a Linux machine with Python2 and ROS Kinetic. The system was provided by Udacity for this particular project.

Dependencies

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.
Download the Udacity Simulator.
You can check the Udacity Similator repository here

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

Simulator

You can download these from the project intro page in the classroom.

Using the application

Build

cd ros
catkin_make

Run

After building the project, launch the launch file

source devel/setup.sh
find /home/workspace/your/directory -type f -iname "*.py" -exec chmod +x {} \;
roslaunch launch/styx.launch

Wait for Tensorflow to initialize and then start the simulator.

Results

The Traffic Light Detection Model used is described in REFLECTION.md.

Youtube video

SakshayMahna/P9-SystemIntegration