Final project for 3-term Udacity-CarND by individualy. Achieve the basic requiremnts that integrating control
, path_planning
and traffic light detection
modules into a Autoware based ROS. The code cand drive the car smoothly following the lane and safely stoping in front of a red traffic light in the sumulator Highway road.
I trained an object detection Neural Network to decide the car using real-world traffic light data. The simulator image seems too different for the model comparing to trainning data. Even though the model could detect the right position and color in the simulator, the confidence score is really low and unstable for the prediction. This is why the whole system mostly depends on the traffic light information rather than the image.
Here is the example video of this project.
- Fine-tune model, make it can work both in real-world and simulator
- Improve the path planning module
- Replace PID and Yaw with MPC
- Add obstacle moduel and sensor fusion moduel
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.
-
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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- 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 "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.