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Udacity Self Driving Car Nanodegree - Capstone Project

Udacity - Self-Driving Car NanoDegree

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: Yes We Car!

Name Email
Stefan Larsson * stefan at larsson.nu
William Dodson wdodson at epix.net
Stefan Gerlach stefan.gerlach1 at gmx.de
Mahmoud Taha mmitm94 at gmail.com
Juan Venegas jc.venegasrincon at gmail.com

* Team lead


Please use one of the two installation options, either native or docker installation.

Traffic Lights Detection and Classification

To see the tutorial for training a neural network for traffic light detection and classification, please have a look into the traffic_light_training/README.md!

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

  • Dataspeed DBW

  • 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
  1. Install python dependencies
cd CarND-Capstone
pip install -r requirements.txt
  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_file.zip
  1. Play the bag file
rosbag play -l traffic_light_bag_file/traffic_light_training.bag
  1. Launch your project in site mode
cd CarND-Capstone/ros
roslaunch launch/site.launch
  1. Confirm that traffic light detection works on real life images

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Team "Yes we car!" Udacity Self-Driving Car Nano-Degree Capstone Project

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