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.

The hardest point

Latency

In my environment, when I turn the camera on, the system can't decipher the message and the car loses control. We were able to clear the test by examining the environment while trying to reduce the camera cycle and allowing the VM to run.

• hint
  • https://github.com/josehoras/Self-Driving-Car-Nanodegree-Capstone#2-workaround-to-avoid-simulator-latency-issue-with-camera-on

Tensorflow version

I used TensorFlow Object Detection API(ssd mobilenet v1 coco) to perform signal recognition. Transfar learning went well. However, I struggled to create a model for use in tensorflow 1.3. As a result, I was able to convert the model generated by tensorflow1.15 to 1.3 using a reference stackoverflow.

• My repository
  • https://github.com/takafashi/CarND-Capstone-Detection
• hint
  • https://github.com/tensorflow/models/tree/master/research/object_detection
  • https://stackoverflow.com/questions/53927700/how-to-use-object-detection-api-with-an-old-version-of-tensorflow-v1-3-0

2lap problem

I just couldn't solve the problem of the car stopping when I entered after the second lap. I suspect this is due to the base_waypoints not being able to be updated. We are running out of time this time, but we would like to see if there is anything we can do to improve this problem in the future.

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

  • 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.