laventura / CarND-Capstone-1

Team CarND-Capstone: Udacity Self Driving Car Nanodegree

Team: Millenium Vulkan

🤖🚙 This is the capstone project for Udacity's Self Driving Car Nanodegree.

The gif below shows our code deployed on Udacity's (actual) self driving car nicknamed Carla. Carla was tested in a small parking lot, and was supervised by a trained engineer from Udacity. I sat shotgun and filmed the sequence. 👍The car drives autonomously and properly stops at a (fake) traffic light when it turns red.

Update (Oct 2017) I graduated the Self-Driving Car Nanodegree 🎉🍾. Here's a brief I wrote on my blog atul.fyi

Here's the certificate 🍾🎉

Team Members

This repository is maintained by the following:

• George Terzakis
• Martin Herzog
• Yuda Liu
• Atul Acharya
• Yoni Azuelos

The following video shows the code in action:

Usage

1. Clone the project repository

git clone https://github.com/herzogmartin/CarND-Capstone.git

2. Clone the team's submodule

cd CarND-Capstone
git submodule init
git submodule update

git pull origin master

3. Install python dependencies

pip install -r requirements.txt

4. Make and run styx

cd ros
catkin_make
source devel/setup.sh
roslaunch launch/styx.launch

5. Run the simulator

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 127.0.0.1:4567:4567 -v $PWD:/capstone -v /tmp/log:/root/.ros/ --rm -it capstone

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