Self Driving Vehicle

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.

Team

Name	Github	Email
Carlos Ezequiel	https://github.com/cfezequiel	cafezequiel@gmail.com
Kyuhwan Yeon	https://github.com/KyuhwanYeon	kyuhwanyeon@gmail.com
Santiago Hurtado	https://github.com/hurtadosanti	santiago.hurtado@gmail.com

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

Traffic light classifier models

The traffic light state can be determined using a traffic light classifier, which makes use of TensorFlow models that can be downloaded from here. We reuse trained models developed by Justin Lee, et al. for their previous CarND-Capstone project.

Download the following models:

frozen_inference_graph_sim_tf_v1.4.pb
frozen_inference_graph_real_tf_v1.4.pb

and place them in the following folder: SelfDrivingVehicle/ros/src/tl_detector/light_classification/frozen_models

To enable use of the traffic light classifier models, make sure that the use_model flag is set to True in either the tl_detector/sim_traffic_light_config.yaml or tl_detector/site_traffic_light_config.yaml. If use_model flag is False, the traffic light detector will make use of the light state published on the/vehicle/traffic_lights topic for the closest detected traffic light. For the site or real simulation, the light state is not available, and the light state will be set to UNKNOWN if the use_model flag is False.

If the frozen_models folder does not contain expected model file for the real or simulated track respectively, the behavior would be similar to setting use_modelto False.

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

Clone the project repository
Install python dependencies

cd SelfDrivingVehicle
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 SelfDrivingVehicle/ros
roslaunch launch/site.launch

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.

hurtadosanti / SelfDrivingVehicle