2sbsbsb/CarND-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. We are submitting this project as a team effort. Here is the team composition.

Team Name: teamCarla

Num	Name	Email	Role
1	Jan Rosicky	janrosicky@centrum.cz	Member
2	Komuraiah Poodari	poodari@yahoo.com	Member
3	Rakesh Chittineni	rakeshch.net@gmail.com	Member
4	Santosh Bhushan	2sbsbsb@gmail.com	Leader

System Architecture

This project is based on the architecture framework provided by Udacity as per the following system architecture diagram.

source: Udacity Project Lesson section 4.

Perception Subsystem

We implemented traffic light detection by processing the camera image on need basis. For example processig image with minimum time gap (few milliseconds) and only when the traffic light ahead is few hundred waypoints ahead of current car position. The objective is to optimize the processing on the platform.

We used closest distance measure between car waypoint and traffic light waypoint to identify the closest light ahead of the car. We used a pre-trained convolutional neural network model for inferencing the traffic light classification. We used two models, one each for site inferencing and simulator inferencing.

Planning Subsystem

Planning system has two nodes, viz., waypoint loader and waypoint updater nodes.
The node waypoint loader loads the complete set of traffic waypoints for the entire track. The node waypoint updates updates next set of waypoints ahead of the car. We used only 50 look ahead waypoints to optimize processing. We implemented to have the car stopped 2 waypoints ahead of the traffic light waypoint ahead, the signal warrants a stop.

Control Subsystem

This module contains the following nodes. The node, DBW (Drive By Wire) computes control inputs for actuators, throttle, brake and steering angle based on the desired velocities. We implemented the main control logic in twist_controller.py using low pass filter, yaw_controller and PID cotroller given in the project. We had to play with various parameters.

Reflection

We had to spend good amount of bandwidth on making environment setup stable, as there are quite a few options and the project needs high-end platform with GPU. We look forward to running our implementation on real car, Carla and get feedback.

The rest of the sections are unmodified sections came with the project baseline.

Please use one of the two installation options, either native or docker installation. As a team some of us used native installation, some of us used docker based 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

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

Real world testing

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

2sbsbsb / CarND-Capstone