Udacity Self-Driving Car Engineer Nanodegree Program
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.
The vehicle stop and go properly depening on the traffic light. And also it is able to complete more than one full loop of the track without running off road or any other navigational issues.
This diagram shows the ROS nodes and topices used in the project.
The basic packages are provided and 4 files are mainly updated to complete the project.
This node includes the Traffic Light Detection.
(path_to_project_repo)/ros/src/tl_detector/tl_detector.py
Waypoint Updater Node to publish a fixed number of waypoints ahead of the vehicle with the correct target velocities, depending on traffic lights and obstacles.
(path_to_project_repo)/ros/src/waypoint_updater/waypoint_updater.py
Drive-By-Wire (DBW) node and twist controller is used to publish throttle, brake, and steering.
(path_to_project_repo)/ros/src/twist_controller/dbw_node.py
(path_to_project_repo)/ros/src/twist_controller/twist_controller.py
Below is the original Readme from Udacity
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.
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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 "uWebSocketIO Starter Guide" found in the classroom (see Extended Kalman Filter Project lesson).
- 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
Outside of requirements.txt
, here is information on other driver/library versions used in the simulator and Carla:
Specific to these libraries, the simulator grader and Carla use the following:
Simulator | Carla | |
---|---|---|
Nvidia driver | 384.130 | 384.130 |
CUDA | 8.0.61 | 8.0.61 |
cuDNN | 6.0.21 | 6.0.21 |
TensorRT | N/A | N/A |
OpenCV | 3.2.0-dev | 2.4.8 |
OpenMP | N/A | N/A |
We are working on a fix to line up the OpenCV versions between the two.