alex-delacruz / CarND-Capstone

This is the project repo for the final project of the Udacity Self-Driving Car Nanodegree: Programming a Real Self-Driving Car.

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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.

Team FinishLine

Video

You may see a video of our car driving the second lap in the simulator here: https://youtu.be/u-9C46mq8wY

Installation

Bare Metal

  • 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.

Docker container

Install Docker on your host machine.

Usage

Bare Metal

  1. Clone the project repository
git clone https://github.com/udacity/CarND-Capstone.git
  1. Install python dependencies
cd CarND-Capstone
pip install -r requirements.txt
  1. Install dbw_mkz ros package
bash <(wget -q -O - https://bitbucket.org/DataspeedInc/dbw_mkz_ros/raw/default/dbw_mkz/scripts/sdk_update.bash)
  1. Make and run styx
cd ros
catkin_make
source devel/setup.sh
roslaunch launch/styx.launch
  1. Run the simulator

Docker container

  1. Clone the project repository
git clone https://github.com/udacity/CarND-Capstone.git
  1. Start the container
./run.sh
  1. Make and run styx
cd ros
catkin_make
source devel/setup.sh
roslaunch launch/styx.launch
  1. 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_files.zip
  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

Credits

Thanks to the Kung Fu Panda Automotive team for their dummy traffic light detector we used during testing. It bought us some time before we had a working traffic light detector.

About

This is the project repo for the final project of the Udacity Self-Driving Car Nanodegree: Programming a Real Self-Driving Car.


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Language:Jupyter Notebook 97.2%Language:Python 1.2%Language:CMake 1.0%Language:C++ 0.6%Language:Shell 0.0%