zhouwubai/CarND-Capstone

The final integration project for self-driving car nano program, https://github.com/udacity/CarND-Capstone

Term 3: Capstone Project : System Integrations

In this project we develop a system which integrates multiple components to drive Carla, the Udacity self-driving car, around a test track.

Team Members (RoboTaxi)

Name	Udacity Account Email Address
Wubai Zhou	zhouwubai@gmail.com
Cheryl Anne E. de la Cruz	cherylestacio@gmail.com
Michael Butler	michaelcbutler@gmail.com
Sunil S Nandihalli	sunil.nandihalli@gmail.com
Cory Yee	corknelius@gmail.com

How to use

Download two models for simulation and real test site seperately

• model on simulation
• model on real site

Rename both models to frozen_inference_graph.pb and put them in folder models/ssd_sim or models/ssd_real seperately. (please create the folder if it does not exist)

Self-driving Car System Components

ROS System Components in Capstone Project

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.

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.

• 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

sudo docker build . -t capstone

Run the docker file

sudo 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

2. Install python dependencies

cd CarND-Capstone
pip install -r requirements.txt

CAEd NOTE Make sure you're using Python2. Run which python to see what was set. You may need to edit your ./bashrc file to see if a miniconda or anaconda environment is forcing you to use a specific version of python. Also see which folder your /usr/bin/python is pointing.

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