Andcircle/Final_Proj_Capstone

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 instructions from term 2

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

Architecture Introduction

This method is using 2 seperate DNNs, responsible for detecting traffic lights and recognizing the color of traffic lights respectively.

A pre-trained model from model-zoo is used for traffic ligts detection, since there's no need to train the model, we can simply switch to any model we wanna try. The implementation is in lights_detector.py

A second DNN is built to recognize the color of traffic lights, it's relatively small, so the training process is very fast. Training data can be found here. The implemetation is in lights_classifier.py, training part is in lights_classifier_trainning.py

Finally, perception_pipeline.py is a complete pipeline to recognize the color directly from an image.

The advantages of this architecture is:

1. The training process is very fast, less 5 second on GTX 1080 card

2. Easy to get training data and label, and not too many data are needed.

3. Can easily change to other object recognition model from model zoo (use sdd mobilenet v1 currently)