Autonomly.io Capstone Project

This is a team effort that involved the following members (alphabetical order)

1. Kinshuk Chandra (kinshuk.ram@gmail.com) - Traffic Light detector package)
2. Lance Lee (blitzbeat@gmail.com) - Classification model training)
3. Vikalp Mirsha (vikalp.mishra@gmail.com) - Twist controller package)
4. Pantelis Monogioudis (monogioudis@yahoo.com) - Team Lead and Path Planner / Waypoint updater package)

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.

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

The End Result

Here’s a link to our video demosnrating the car moving in the track with the full traffic
light classification.

Development Environment Setup

Our setup involves remote debugging of the code running in the ROS Ubuntu VM from a host OS that also runs the simulator.
Detailed instructions for PyCharm are in https://github.com/pantelis/CarND-Capstone-Project/blob/master/dev_env.md.