- Hiu Chan - Team Lead (hiu_chan@hotmail.com)
- Yuda Wang (yuda@berkeley.edu)
- Ran Yan (896521722@qq.com)
- Zhening (Sirius) Zhang (zznzhang@ucdavis.edu)
- Praveen Kumar Marothu (praveen.marothu@gmail.com)
Youtube Video Demo: https://www.youtube.com/watch?v=h8KuF9HO1Jw
- https://github.com/leggedrobotics/darknet_ros , darknet_ros, which is used in the project.
- And https://pjreddie.com/darknet/yolo/ , Joseph Redmon, the original author of darknet & YOLO.
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.
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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
Apply the following commands (one at a time)
echo "source /opt/ros/kinetic/setup.bash" >> ~/.bashrc
mkdir -p ~/catkin_ws/src
cd ~/catkin_ws/src
catkin_init_workspace
cd ~/catkin_ws
catkin_make
Apply the following commands (one at a time)
apt update
apt install ros-kinetic-dbw-mkz-msgs
ROS node architecture with the publish and subscribe topics.
- Clone the project repository
git clone https://github.com/hc167/Udacity-SDC-Term-Three-Capstone.git Capstone0
- Install python dependencies
cd Capstone0
pip install -r requirements.txt
- Make and run styx
cd ros
catkin_make -DCMAKE_BUILD_TYPE=Release
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