Lane Detection for Autonomous Vehicle

Overview

The objective of this project was to design a lane detection (with lane turn signal and drive heading output) system for autonomous vehicles/Robots. The proposed lane detection algorithm by using a video feed input of a vehicle driving on the highway, detects the lane position and give drive heading angle. Which in turn can be passed to steering control system to move the vehicle inside the lane. Maintaining the lane on highway is very critical for autonomics vehicles. The system can be also be integrated with the popular Lane departure warning system designed to warn the driver when the vehicle begins to move out of its lane. The system being developed in C++ language provides very good real time performance.

In this step , we find out the peak hough lines, group them into two groups (positive, negative gradients) and extrapolate lines in each group. The lines are classified depending on the value of their slope and where their initial and final points are approximately located with respect to the center of the image.

Plot the lane and drive head

Results

Output of the syste is quite good. The system was able to detect the lane even the part of the road which was whitish.

Output video can be seen at this [link] (https://drive.google.com/drive/u/1/folders/1rqz6ssvReQMQbOU6W-9e-2ThTCKpCEad)

Dependencies

OpenCV 4.1 or higher. This can be downloaded by following the steps of this link
For unit testing this project depends on gtest framework by Google.
CMake version at least 3.2.1

Standard install via command-line

git clone --recursive https://github.com/yclin1209/AcmeLaneDetectionModule
cd <path to repository>
mkdir build
cd build
cmake ..
make

Building for code coverage

sudo apt-get install lcov
cmake -D COVERAGE=ON -D CMAKE_BUILD_TYPE=Debug ../
make
make code_coverage

This generates a index.html page in the build/coverage sub-directory that can be viewed locally in a web browser.

Instructions to run the demo and tests

Once the module is built correctly, to run the demo type the following command:

$ cd <build folder of the module>
$ ./app/main

To run the unit tests, please execute the command given below:

$ ./test/system-test

Dataset

The dataset used for the system evaluation is taken from Advanced Lane Detection dataset from Udacity - Self Driving Nanodegree program. The dataset can be downloaded from the link below :

https://drive.google.com/drive/folders/1XR0v4H73xvUQDT92OO_ud9MVqnTNOXJO?usp=sharing

Doumentation

The douments for this project is already present in docs folder.

To generate documentation install dependencies first

$ sudo apt-get install doxygen
$ sudo apt-get install doxygen-gui
$ doxywizard

It will open gui version. On the top of the gui window, It will require a working directory. Set the directory. Give the source folder path as the repository folder and check the recursive checkbox. Give target directory where you want to save the documentations files.

License

MIT License

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

yclin1209 / AcmeLaneDetectionModule