Udacity's Self-Driving Car Engineer Nanodegree Program

Team: YDriving

• Name: Yu Ishikawa
• Email: yuu.ishikawa@gmail.com
• Time Zone: UTC-07:00 (California)
• GitHub repo: https://github.com/yu-iskw/CarND-Capstone

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

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 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

Launch with eventlet.monkey_patch()

If the CPU resource is short, it would help us with running it.

EVENTLET_MONKEY_PATCH=true roslaunch launch/styx.launch

4. Run the simulator

### Real world testing
1. Download [training bag](https://s3-us-west-1.amazonaws.com/udacity-selfdrivingcar/traffic_light_bag_file.zip) that was recorded on the Udacity self-driving car.
2. Unzip the file
```bash
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