Project Overview

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.

System Architecture

The following is a system architecture diagram showing the ROS nodes and topics used in the project.

Rubric Points

The code is built successfully and connects to the simulator: Thanks to code walkthrough lessons, the project is built and runs successfully. No errors observed with catkin_make, source devel/setup.sh and roslaunch launch/styx.launch.

Waypoints are published to plan Carla’s route around the track: The waypoints are published to the /final_waypoint topic in waypoint_update.py (line 82). Acceleration and jerk limits are not exceeded. The top speed of the vehicle is limited to the km/h velocity set by the velocity ros param in waypoint_loader.py.

Controller commands are published to operate Carla’s throttle, brake, and steering: dbw_node.py and twist_controller.py are implemented. The throttle and brake commands are published to the /vehicle/throttle_cmd and /vehicle/brake_cmd topics. The steering command is published to the /vehicle/steering_cmd topic.

Successfully navigate the full track more than once: The vehicle successfully navigates the track more than once. You can find the captured video of the track down below.

Capstone Project Video

Traffic Light Detection

The optional task of traffic light classification is not implemented. I do not have much time since my bundle subscription is almost expired. I will complete the task after graduation.

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.

• 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.