Udacity Project 13: Capstone

Team Members

Name	E-Mail	Slack
Stefan Cyliax	[email protected]	@stefancyliax
Paul Michalik	[email protected]	@paul-michalik
Frank Keidel	[email protected]	@frankk
Patrick Schalast	[email protected]	@patrickschalast

The goal of this project was to implement a complete stack that can drive a vehicle down a street and recognize and respect traffic lights. For this the Robot Operating System was used.

Real Parking Lot at Udacity: (Youtube link)

Highway video: (Youtube link)

Test Lot Video: (Youtube link)

Submission checklist and requirements

• Smoothly follow waypoints in the simulator.
• Respect the target top speed set for the waypoints' twist.twist.linear.x in waypoint_loader.py. Be sure to check that this is working by testing with different values for kph velocity parameter in /ros/src/waypoint_loader/launch/waypoint_loader.launch.
• If your vehicle adheres to the kph target top speed set here, then you have satisfied this requirement.
• Stop at traffic lights when needed.
• Stop and restart PID controllers depending on the state of /vehicle/dbw_enabled.
• Publish throttle, steering, and brake commands at 50hz.
• Launch correctly using the launch files provided in the capstone repo.

rqt_graph

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Udacity README

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

Native Installation

More to read

• 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

Run on Docker for Windows

Clone the project as described in section "Usage".

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

On the first usage or when you modify Dockerfile build the Docker image

cd CarND-Capstone
docker_build.bat

Run the image in a new container with interactive shell terminal: docker_run.bat. Then proceed as described in section "Usage".

Run the image in a new container and log-in via RDP: docker_run_rdp.bat. Log in and proceed as described in section "Usage". To log-in into a running container, open a terminal (e.g. via Kitematic or docker attach <container-id> or docker attach happy-capstone-win. (Work in progress, not yet supported)

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