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.
| Name | Udacity account email |
|---|---|
| John Moody (Team Lead) | John.Moody at ieee.org |
| Jayasim Jayakumar | jayasim at gmail.com |
| Rajeev Ranjan | rajeev.cse.imps at gmail.com |
| Sven Bone | sven.bone at mail.de |
| Bassam Sayed | bunderscoresayed at icloud.com |
We have created a TwistController class from twist_controller.py which will be used for implementing the necessary controllers. The throttle values passed to publish should be in the range 0 to 1, although a throttle of 1 means the vehicle throttle will be fully engaged. Brake values passed to publish should be in units of torque (N*m). The correct values for brake can be computed using the desired acceleration, weight of the vehicle, and wheel radius.
Here we will subscribe to /twist_cmd message which provides the proposed linear and angular velocities. Yawcontroller is used to convert target linear and angular velocity to steering commands. /current_velocity provides the velocity of the vehicle from simulator.
One thing to keep in mind while building this node and the twist_controller class is the status
of dbw_enabled. While in the simulator, its enabled all the time, in the real car, that will
not be the case. This may cause PID controller to accumulate error because the car could
temporarily be driven by a human instead of your controller.
Once we have the proposed throttle, brake, and steer values, we published it on the various publishers. We have currently set up to publish steering, throttle, and brake commands at 50hz. The DBW system on Carla expects messages at this frequency, and will disengage (reverting control back to the driver) if control messages are published at less than 10hz. This is a safety feature on the car intended to return control to the driver if the software system crashes.
The image export node tl_image_extractor.py can be configured by its launch files. There are basically two launch files, one for
the simulator setup tl_image_extractor.launch and one for the rosbag setup tl_image_extractor_site.launch.
Attention:
If you have resource limitations on your PC, ensure to deactivate the OpenCV image visualization by setting
export_show_image to False in both launch files.
Parameters
<param name="export_directory" type="str" value="/home/student/CarND-Capstone/export"/>
<param name="export_filename" type="str" value="tfl_"/>
<param name="export_rate" type="int" value="1"/>
<param name="export_encoding" type="str" value="bgr8"/>
<param name="export_show_image" type="bool" value="True"/>
Simulator
- Check if the export directory (
export_directory) exists and is empty. The exporter overrides existing images! - Start the image extractor node with styx support by
roslaunch launch/styx_image_extractor.launch - Run the simulator
- Activate camera output in simulator
ROS Bags
- Check if the export directory (
export_directory) exists and is empty. The exporter overrides existing images! - Start the image extractor node by
roslaunch launch/site_image_extractor.launch - Run ROS bag player by
rosbag play ./bags/just_traffic_light.bag
-
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.
-
- 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/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 (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