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.

• Team name: Rocky
• Team members

It's a great project and also a great challenge. Here is an introduction of all the code.

1. waypointer_updater node

   • For acceleration, simply set all the linear velocity to max velociy read from waypointer_loader parameter velocity.
   • For deceleration, decrement the linear velocity into 0 with deceleration = 0.5(waypointer_updater.py line 160).

2. twister_controller node

   • Since the twiddle parameters for PID is kind of impossible, so we simply use a constant throttle and brake. That works fine in simulator.
   • For throttle, type is PERCENT. We use 1.0 for simulator and 0.025 for Carla retrieved from a rosbag(udacity_succesful_light_detection.bag).
   • For brake, type is TORQUE. It's calculated by

   self.max_brake_value = ( self.vehicle_mass + self.fuel_capacity * GAS_DENSITY ) \
   				* decel * self.wheel_radius
   

   • For steer, we simply used the yaw_controller, nothing changed.

3. tl_detector node

   • We have tried ssd-mobile and ssd-inception models with different datasets(bosch and datasets collected from simulator and bag file from our classmates codeKnight).
   • Bosch dataset is a huge dataset, but didn't solve our problem.
   • We simply chose the dataset directly collected from simulator and bagfiles.
   • For the two models, we found that ssd-mobile made much mistakes in simulator detections, however ssd-inception works better. So we chose ssd-inception.
   • We use Tnsorflow Object Detection API to train the model. After 20k steps, we got loss converged to around 1-2.
   • My computer is ubtuntu 16.04 with GTX 1070, and I tested the speed of the model, turned out it's kind of slow in my local computer, need ~300ms to make detections on one image. Then I tested the same model on AWS(p2.xlarge), detection time down to ~60ms. That's an acceptable speed.
   • We also provide a test node tl_test.py to test the classifier in sim and site. It will publish a image with detections into a topic /detection. We could check the /detection in rqt_image_view.
   • Finally, we trained two seperate models each for sim and site, both based on ssd-inception model.

• For brake, I changed to PERCENT type to test in sim, it turned out not working no matter what value i set.
• ssd-mobilenet is much worse than I thought, however I don't think it's really that worse. I guess the poor dataset would be the reason, so I will work on this model on other datasets to see how it work.
• I have tried set throttle = 1.0 always, but the speed of vehicle are only up to around 7-8mph(sometimes up to 12mph). That's so weird, since max velocity 40km/h = (40 * 1000 / 3600) * 2.24 = ~24mph.
• DBW is too easy to implement for sim, and that's not supposed to be so easy in real world. The CarND team have simplified this node.

Thanks for all our team members, we made a great achievement.
Thanks a lot to our classmate Vatsal Srivastava, Self Driving Vehicles: Traffic Light Detection and Classification with TensorFlow Object Detection API give me a lot of help.
Also thanks to many classmates from slack and udacity-team, you gave me a lot of help.

• 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.

• Dataspeed DBW

  • Use this option to install the SDK on a workstation that already has ROS installed: One Line SDK Install (binary)

• Download the Udacity Simulator.

Install Docker

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

1. Clone the project repository

git clone https://github.com/udacity/CarND-Capstone.git

2. Install python dependencies

cd CarND-Capstone
pip install -r requirements.txt

3. Make and run styx

cd ros
catkin_make
source devel/setup.sh
roslaunch launch/styx.launch

4. Run the simulator

1. 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)
2. Unzip the file

unzip traffic_light_bag_files.zip

3. Play the bag file

rosbag play -l traffic_light_bag_files/loop_with_traffic_light.bag

4. Launch your project in site mode

cd CarND-Capstone/ros
roslaunch launch/site.launch

5. Confirm that traffic light detection works on real life images