Overview

This is the repo for the final project of the Udacity Self-Driving Car Nanodegree: Programming a Real Self-Driving Car. It is a team project. contributed to this project:

TEAM :

Name	Email	Slack
Shyam Jagannathan	shyam.jagannathan@gmail.com	@shyam.jagannathan
Hubert de Lassus	hubert.delassus@gmail.com	@delassus
Haitham Khedr	haithamkhedr@gmail.com	@haithamkhedr
Ganesh Setty R	setty.ganesh@gmail.com	@gsetty
Nishanth	nishanth.jois@gmail.com	@nishanthjois

System Architecture Diagram

For this project, I will be be writing ROS nodes to implement core functionality of the autonomous vehicle system, including traffic light detection, control, and waypoint following. I will test the code using a simulator, and finally, the project will be run on "Carla" the Udacity Lincoln self driving car.

The following is a system architecture diagram showing the ROS nodes and topics used in the project. The ROS nodes and topics shown in the diagram are described briefly in the Code Structure section below.

Code Structure

Below is a brief overview of the repo structure, along with descriptions of the ROS nodes. The code for the project is contained entirely within the (path_to_project_repo)/ros/src/ directory. Within this directory, you will find the following ROS packages:

Traffic Light Detector

(path_to_project_repo)/ros/src/tl_detector/ This package contains the traffic light detection node: tl_detector.py. This node takes in data from the /image_color, /current_pose, and /base_waypoints topics and publishes the locations to stop for red traffic lights to the /traffic_waypoint topic. The detector is a Mobile Netconvolutional Neural Network that combines in one shot detection of the traffic light in the video stream of the dashboard camera and classification of its color between Green, Yellow, Red.

Current Pose Topic

The /current_pose topic provides the vehicle's current position, and /base_waypoints provides a complete list of waypoints the car will be following.

This project implements both a traffic light detection node and a traffic light classification node. Traffic light detection takes place within tl_detector.py, whereas traffic light classification takes place within ../tl_detector/light_classification_model/tl_classfier.py.

Way Point Updater

(path_to_project_repo)/ros/src/waypoint_updater/ This package contains the waypoint updater node: waypoint_updater.py. The purpose of this node is to update the target velocity property of each waypoint based on traffic light and obstacle detection data. This node subscribes to the /base_waypoints, /current_pose, /obstacle_waypoint, and /traffic_waypoint topics, and publish a list of waypoints ahead of the car with target velocities to the /final_waypoints topic.

(path_to_project_repo)/ros/src/twist_controller/

Drive By Wire

Carla the Udacity Lincoln self driving car is equipped with a drive-by-wire (dbw) system, meaning the throttle, brake, and steering have electronic control. This package contains the files that are responsible for control of the vehicle: the node dbw_node.py and the file twist_controller.py, along with a pid and lowpass filter. The dbw_node subscribes to the /current_velocity topic along with the /twist_cmd topic to receive target linear and angular velocities. Additionally, this node will subscribe to /vehicle/dbw_enabled, which indicates if the car is under dbw or driver control. This node will publish throttle, brake, and steering commands to the /vehicle/throttle_cmd, /vehicle/brake_cmd, and /vehicle/steering_cmd topics.

Additional packages

In addition to these packages you will find the following. The styx and styx_msgs packages are used to provide a link between the simulator and ROS, and to provide custom ROS message types:

(path_to_project_repo)/ros/src/styx/ A package that contains a server for communicating with the simulator, and a bridge to translate and publish simulator messages to ROS topics. (path_to_project_repo)/ros/src/styx_msgs/ A package which includes definitions of the custom ROS message types used in the project. (path_to_project_repo)/ros/src/waypoint_loader/ A package which loads the static waypoint data and publishes to /base_waypoints. (path_to_project_repo)/ros/src/waypoint_follower/ A package containing code from Autoware which subscribes to /final_waypoints and publishes target vehicle linear and angular velocities in the form of twist commands to the /twist_cmd topic.

Implementation

Waypoint Updater Node (partial): Completes a waypoint updater which subscribes to /base_waypoints and /current_pose and publishes to /final_waypoints.

DBW Node: Once the waypoint updater is publishing /final_waypoints, the waypoint_follower node will start publishing messages to the/twist_cmd topic. At this point, we have everything needed to build the dbw_node. After completing this step, the car drives in the simulator, ignoring the traffic lights.

Traffic Light Detection: This is split into 2 parts: Detection: Detect the traffic light and its color from the /image_color. The topic /vehicle/traffic_lights contains the exact location and status of all traffic lights in simulator, so we can test our output.

Waypoint publishing: Once we have correctly identified the traffic light and determined its position, we can convert it to a waypoint index and publish it. Waypoint Updater (Full): we use /traffic_waypoint to change the waypoint target velocities before publishing to /final_waypoints. The car now stops at red traffic lights and moves when they are green.

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.

Usage

1. Clone the project repository

git clone https://github.com/delassus/ROS-System-Integration.git

2. Install python dependencies

cd ROS-System-Integration
pip install -r requirements.txt


3. Make and run styx
```bash
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_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 ROS-System-Integration/ros
roslaunch launch/site.launch