This is the project repo for Programming a Real Self-Driving Car, using TensorFlow, ROS and OpenCV as part of the final project of the Udacity Self-Driving Car Nanodegree. For more information about the project, see the Udacity project repository here.

This project is the combined work of;

• Andre Meeusen - pmeeusen@hotmail.com
• Christian Marzahl - christian.marzahl@gmail.com
• Haidyn Mcleod - haidyn.mcleod@gmail.com
• Kamil Kindziuk - kamil.kindziuk@gmail.com
• Punnu Phairatt - luke.phairatt@gmail.com

A video of the project in action using the simulator can be viewed here.

Stop Light Detection Notebook

An ipython Notebook of the traffic light detection pipeline can be found in /ros/src/tl_detector/training/TrafficLightPipeline.ipynb here.

System Dependencies

Ubuntu

The code requires a workstation 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 a minimum:
  • 2 CPU
  • 2 GB system memory
  • 25 GB of free hard drive space

ROS

Follow these instructions to install ROS

• ROS Kinetic if you have Ubuntu 16.04.
• ROS Indigo if you have Ubuntu 14.04.

Vehicle ROS Model

The Simulator uses the Dataspeed DBW model which can be found here here

• Use this option to install the SDK on a workstation that already has ROS installed: One Line SDK Install (binary)

Simulator

Download the Udacity Simulator for your host operating system from here.

If using a Virtual Machine, it is recommended to downloading the simulator for your host operating system and using this outside of the VM. You will be able to run code within the VM while running the simulator natively on the host using port forwarding on port 4567 for both the Host and Guest ports. For more information on how to set up port forwarding, see here.

Installation and setup

Docker Installation

Not required, but if you would prefer to use Docker to run the system, Install Docker from here

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

Build Instructions

1. Clone the project repository

git clone https://github.com/Heych88/Udacity-CarND-Capstone.git

2. Install python dependencies

cd Udacity-CarND-Capstone
pip install -r requirements.txt

3. Make and build the code

cd ros
catkin_make
source devel/setup.sh

Running Code

1. Run styx

roslaunch launch/styx.launch

2. Run the simulator and untick the manual box in the top left of the screen. The car will now drive around the track, stopping at red lights, and you should arrive at a result similar to the below.

Real world testing

1. Download training bag that was recorded on the Udacity self-driving car (a bag demonstrating 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