/CarND-Capstone

Primary LanguageCMakeMIT LicenseMIT

Udacity - Self-Driving CarNanoDegree

Self-Driving-Car Capstone Project

image

Team: Who Needs Driver?:

Name E-Mail GitHub
Team Lead Xi Hu chris_huxi@163.com Xi
Yuanhui Li viglyh@163.com Yuanhui
Zyuanhua zzyuanhua@163.com Zyuanhua
Maharshi Patel patelmaharshi94@gmail.com Maharshi
Aniket Satbhai anks@tuta.io Aniket

How to run test?

1. test on simulator

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

wait until we get log info:

[INFO] [1564778567.666529]: loaded ssd detector!

then, open the simulator, check the "Camera" and uncheck the "Manual" you will see the car runs like video here.

optional: if you want to show how the camera output looks like, you can run in another terminal:

rosrun rviz rviz

then add topic: image_color/raw.

optional: if you want to run 2nd test lot, you need to modify ros/src/waypoint_loader/launch/waypoint_loader.launch like this:

#<param name="path" value="$(find styx)../../../data/wp_yaw_const.csv" />
<param name="path" value="$(find styx)../../../data/churchlot_with_cars.csv"/>       
<param name="velocity" value="5" />

then launch styx.launch and run simulator. you will see the car runs like video here

2. test on real world test bag:

Firstly download the bag file then run in terminal :

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

And wait until we get log info:

[INFO] [1564778567.666529]: loaded ssd detector!

then run in other terminal:

rosbag play -l XXX.bag

if you want to see the image given by bag file, open one another terminal and run:

rosrun rviz rviz

add topic: image_color/raw, you will see result as this video: just_traffic_light.bag, video: loop_with_traffic_light.bag, video: udacity_succesful_light_detection.bag

System Architecture

The project has following architecture:

ROS Graph

Waypointer Updater Node

Waypointer Updater node helps updating the target velocity of each waypoints based on traffic light and obstacle detection data.

  • It subscribes to the /base_waypoints, /current_pose and /traffic_waypoint topics.
  • It publishes a list of waypoints ahead of the car with target velocities to the /final_waypoints topic.

Subscribed Topics:

/base_waypoints

Msg Type: styx_msgs/Lane

This topic provides the waypoints along the driveway path. Waypoint Loader node publishes the list of waypoints to this topic at the starting.

/current_pose

Msg Type: geometry_msgs/PoseStamped

This topic provides the current position of the vehicle. The position is published by the Car/Simulator.

/traffic_waypoint

Msg Type: std_msgs/Int32

This topic provides the waypoints at which the car is expected to halt. Traffic Light Detection node publishes to this topic.

Published topics:

/final_waypoints

Msg Type: styx_msgs/Lane

Final waypoints are published to this topic. The vehicle is supposed to follow these waypoints.

Traffic Light Detection Node

Traffic Light Detection Node detects the traffic light and publishes it's location.

  • It subscribes to the /base_waypoints, /current_pose, /vehicle/traffic_lights and /image_raw topics.
  • It publishes waypoint index of upcoming traffic light position to /traffic_waypoint.

Subscribed Topics:

/base_waypoints

Msg Type: styx_msgs/Lane

This topic provides the waypoints along the driveway path. These are the same list of waypoints used in Waypoint Updater node.

/current_pose

Msg Type: geometry_msgs/PoseStamped

This topic provides the current position of the vehicle. The position is published by the Car/Simulator.

/image_raw

Msg Type: sensor_msgs/Image

This topic provides raw image from the vehicle sensor. The image helps identify red lights in the incoming camera image.

/vehicle/traffic_lights

Msg Type: styx_msgs/TrafficLightArray

This topic is only used while using the simulator for testing the vehicle path without the use of classifier. This topic provides the location of the traffic light in 3D map space and helps acquire an accurate ground truth data source for the traffic light classifier by sending the current color state of all traffic lights in the simulator.

Published Topics:

/traffic_waypoint

Msg Type: std_msgs/Int32

This topic provides the waypoints at which the car is expected to halt.

DBW Node

Drive-By-Wire Node uses the final waypoints to apply required brake, steering and throttle values to drive the vehicle.

  • It subscribes to the /twist_cmd, /vehicle/dbw_enabled and /current_velocity topics.
  • It publishes /vehicle/brake_cmd, /vehicle/steering_cmd and /vehicle/throttle_cmd topics.

Subscribed Topics:

/twist_cmd

Msg Type: geometry_msgs/TwistStamped

This topic provides the proposed linear and angular velocities. Wapoint Follower Node publishes the message to this topic.

/vehicle/dbw_enabled

Msg Type: std_msgs/Bool

This topic indicates if the car is under dbw or driver control. In the simulator, it'll always be True. But, for the actual vehicle we have to make sure if dbw_enabled is True for driving autonomously.

/current_velocity

Msg Type: geometry_msgs/TwistStamped

This topic provides target linear and angular velocities the car should follow.

Published Topics:

/vehicle/brake_cmd

Msg Type: dbw_mkz_msgs/BrakeCmd

Required percent of throttle is published to this topic.

/vehicle/steering_cmd

Msg Type: dbw_mkz_msgs/SteeringCmd

Required steering angle is published to this topic.

/vehicle/throttle_cmd

Msg Type: dbw_mkz_msgs/ThrottleCmd

Required amount of torque is applied using this topic.

About this project:

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.

Installation:

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

  • Dataspeed DBW

  • 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
  1. Install python dependencies
cd CarND-Capstone
pip install -r requirements.txt
  1. Make and run styx
cd ros
catkin_make
source devel/setup.sh
roslaunch launch/styx.launch
  1. 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
  1. Play the bag file
rosbag play -l traffic_light_bag_file/traffic_light_training.bag
  1. Launch your project in site mode
cd CarND-Capstone/ros
roslaunch launch/site.launch
  1. 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.