Udacity Self-Driving Car Engineer Capstone

Project Memebrs

Maciej Czechowski ([email protected])

System architecture

Waypoint updater

The waypoint updater node determines the waypoints vehicle should follow and desired velocity while passing them. It uses traffic light detector as an input: when red light is detected, speed is reduced to 0 in order to stop.

Waypoints are publish at a rate of 20Hz. The numer of waypoints to publish at once was set to 100.

Twist controller

The Twist controller is responsible for calculating steering/throttle/brake values for the vehicle. Yaw controller is used for Steering, while PID controlls the throttle. The velocity input for both controllers is low-pass filtered to remove sensor noise. If controller detects a need for brake (like in order to stop on red light), a linear force is applied fo brake pedal (limited to maximum deceleration rate). When stopped, 700Nm force is applied to keep car stationary.

If DBW module is disengaged, no output is being sent, and PID erros are not being updated.

Traffic light classificator

The Traffic Light Classificator is responsible for detecting red light in front of the car. The detection is based on machine learning, using Single Shot Detection. Models used are different for site and simulation:

1. For Simlation, SSD Inception V2, trained for 45000 steps on Azure virtual machine.
2. For Site, Pre-Trained model from Alex Lenchner

The software installed on Carla (especially TensorFlow) limited the possibilities of exploration. TensorFlow now includes new SSD models which would be very interesting to try.

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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.

Native 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.

• 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.

Docker Installation

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

Port Forwarding

To set up port forwarding, please refer to the "uWebSocketIO Starter Guide" found in the classroom (see Extended Kalman Filter Project lesson).

Usage

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

Real world testing

1. Download training bag that was recorded on the Udacity self-driving car.
2. Unzip the file

unzip traffic_light_bag_file.zip

3. Play the bag file

rosbag play -l traffic_light_bag_file/traffic_light_training.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

Other library/driver information

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.