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README.md

System Integration Project

This is the system integration project submission of team "darwin". The team members are:

Name Udacity account email
Pierluigi Ferrari pierluigi.ferrari@gmx.com
Wolfram Gerschütz woges@go4more.de
Jimmy Hammenstedt hammenst@gmail.com
Juan Ignacio Forcén Carvalho jiforcen@gmail.com
Daniel Lopez dlopezma@jaguarlandrover.com

Important note:

Please download the traffic light detection model linked below (SSD-Inception TensorFlow) and place it in the following directory:

./ros/src/tl_detector/light_classification/frozen_graph/

Download link:

SSD-Inception traffic light detector

Further information about our implementation can be found here.

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.

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

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 (a bag demonstraing the correct predictions in autonomous mode can be found here)
  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
  1. Confirm that traffic light detection works on real life images

Results

The resulting video from the simulator is in the repo, if you are interested.

Contributing

No further updates nor contributions are requested. This project is static.

License

Term3_capstone_project results are released under the MIT License