System Integration on Carla

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

Team Fast Tortoise

Name	Udacity Account Email Address
Srikanth Narayanan ( Team Lead)	srikanth.n.narayanan@gmail.com
Steven De Gryze	sdegryze@gmail.com
Anthony Allison	anthony.w.allison@gmail.com

Vehicle Architecture

For more information about the project, see the project introduction here.

## Note to Tester

There are two convolutional neural network models trained to perform traffic light detection.

sim model which is for simulator traffic light detection
- Download the simulation model graph file from here and move it to model folder name sim in the traffic light node “CarND-Capstone/ros/src/tl_detector/light_classification/model/sim_model”
real model which is used in real world traffic light detection
- Download the real world model graph from here and move it to the model folder name real in the traffic light node “CarND-Capstone/ros/src/tl_detector/light_classification/model/real_model”

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.

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 instructions from term 2

Clone the project repository

git clone https://github.com/udacity/CarND-Capstone.git

Install python dependencies

cd CarND-Capstone
pip install -r requirements.txt

Make and run styx

cd ros
catkin_make
source devel/setup.sh
roslaunch launch/styx.launch

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)
Unzip the file
```
unzip traffic_light_bag_files.zip
```

Play the bag file

rosbag play -l traffic_light_bag_files/loop_with_traffic_light.bag

Launch your project in site mode

cd CarND-Capstone/ros
roslaunch launch/site.launch