This is the project repo for the Autonomous Wizards team from the inaugural (July 7th - October 16th, 2017) cohort of the Udacity Self Driving Car Engineer Nanodegree final & capstone project for said Nanodegree, alternately titled "System Integration" & "Programming a Real Self Driving Car". For more information about the project, see the project introduction here--note: massive "paywall", you have to be registered for the 3rd Term of said Nanodegree, total cost for said activity being $2400, plus having passed the prior two terms / 10 projects.
A video showing a complete run of the virtual track in the simulator by our current--as of October 10th, 2017--version of our repo can be found here, at this link to YouTube video--or just <ctrl>click (to open in a new tab) on thumbail below, same link
Introducing Team Autonomous Wizards (Members in alphabetical order):
Juan Carlos Ortiz ortizjuan2@gmail.com
Chuck S. chuck_s_@outlook.com
Ezra J. Schroeder ezra.schroeder@gmail.com
Christian Sousa neocsr@gmail.com
Calvenn Tsuu calvenn.tsuu@gmail.com
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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 capstoneRun the docker file
docker run -p 4567:4567 -v $PWD:/capstone -v /tmp/log:/root/.ros/ --rm -it capstone- Clone the project repository
git clone https://github.com/seneca-wolf/CarND-Capstone - Install python dependencies (Please note: if you do not have ROS installed / experience w/ ROS it may interfere w/ your [e.g. conda] python distributions & environments, which is why Udacity uses a specific preconfigured Virtual Machine for the Capstone project).
cd CarND-Capstone
pip install -r requirements.txt- Make and run styx
cd ros
catkin_make
source devel/setup.bash
roslaunch launch/styx.launch- Run the simulator
- Download training bag that was recorded on the Udacity self-driving car (a bag demonstraing 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- Confirm that traffic light detection works on real life images