The System Integration project is the final project of the Udacity Self-Driving Car Engineer Nanodegree. In this project We have built ROS nodes to implement traffic light detection, PID control, and waypoint following. Initially this software system will be tested on a simulator, later, this will be deployed on Carla to autonomously drive it around a test track.
For more information about the project, see the project introduction [here](https://classroom.udacity.com/nanodegrees/nd013/parts/6047fe34-d93c-4f 50-8336-b70ef10cb4b2/modules/e1a23b06-329a-4684-a717-ad476f0d8dff/lessons/462c933d-9f24-42d3-8bdc-a08a5fc866e4/concepts/5ab4b122-83e6-436d-850f-9f4d26627fd9).
- Bajrang Chapola
(Project Contribution: Team Lead, PID Controller)
- dev.chapola@gmail.com - Nishant Rana
(Project Contribution: Traffic Light Detection)
- nishantcop@gmail.com - Jian Kang
(Project Contribution: Train CNN model for traffic light detection)
- kangjiankarl@163.com - Muhammad Al-Digeil
(Project Contribution: Train CNN model for traffic detection)
- digeil@acm.org - Melanie Schmidt-Wolf
(Project Contribution: Waypoint updater)
- melanie.schmidt-wolf@web.de
Please use one of the two installation options, either native or docker installation.
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Be sure that your workstation is running Ubuntu 16.04 Xenial Xerus or Ubuntu 14.04 Trusty Tahir. Ubuntu downloads can be found here.
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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.
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Follow these instructions to install ROS
- ROS Kinetic if you have Ubuntu 16.04.
- ROS Indigo if you have Ubuntu 14.04.
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- Use this option to install the SDK on a workstation that already has ROS installed: One Line SDK Install (binary)
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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
- Run the simulator
- Download training bag that was recorded on the Udacity self-driving car.
- Unzip the file
unzip traffic_light_bag_file.zip
- Play the bag file
rosbag play -l traffic_light_bag_file/traffic_light_training.bag
- Launch your project in site mode
cd CarND-Capstone/ros
roslaunch launch/site.launch
- Confirm that traffic light detection works on real life images
The traffic light detection subsystem is based on a trained SSD-MobileNetV2 CNN model. The implementation details are under the link.