• Ryan Arya Pratama, ryan.arya.pratama@gmail.com
• Zhifan Sang, zfsang@gmail.com
• Sabari Rajan, sabari.rajan90@gmail.com
• Ching Nian Wong, drah2easy@gmail.com

Starter code from Udacity

Traffic light detector-classifier network as implemented by CN Wong

• input:
  • base waypoint topic (the ground truth of the track)
  • current pose topic (the current position / speed of the car)
  • traffic waypoint topic
• output:
  • final waypoint topic
• method:
  • KD tree (used to find closest waypoint for the car to follow)

• input:
  • current velocity topic
  • twist cmd topic
  • dbw enabled topi
• output:
  • throttle, brake, steering of the car (published by Controller class imported from twist_controller.py)
• method:
  • PID filter (pid.py)
  • low pass filter (lowpass.py)
  • yaw controller (convert target linear and angular velocity to steering commands.)

• input:
  • base waypoint topic
  • current pose topic
  • image color topic
  • traffic light topic (3D position of the traffic light)
• output:
  • traffic waypoint topic
• method:
  • object detection with CNN (Tensorflow)

Requires dbw_mkz_msgs. If you are on the Udacity workspace, copy over from the provided starter code. Otherwise, install ros-{melodic, kinetic}-dbw-mkz-msgs with apt.

Use ros/launch/styx.launch if you have ROS Kinetic (or using Udacity workspace), use ros/launch/styx_melodic.launch if you have ROS melodic.

Original README from starter below

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.

• 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.

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

To set up port forwarding, please refer to the "uWebSocketIO Starter Guide" found in the classroom (see Extended Kalman Filter Project lesson).

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

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

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.