Programming a Real Self-Driving Car

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

The goal of this project is to write ROS nodes to implement core functionality of the autonomous vehicle system, including traffic light detection, control, and waypoint following. The code is tested using a simulator and then, the project can be run in a real Self-Driving Car, called Carla.

Team Members

Setup instructions

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

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

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

• 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 "uWebSocketIO Starter Guide" found in the classroom (see Extended Kalman Filter Project lesson).

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

Other library/driver information

Outside of requirements.txt, here is information on other driver/library versions used in the simulator and Carla:

Specific to these libraries, the simulator grader and Carla use the following:

We are working on a fix to line up the OpenCV versions between the two.