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Motion planning for self-driving cars using ONCE dataset. Aims to provide framework for ONCE dataset use for self-driving cars and continuous development pipeline for experimental motion planning techniques.

varungiridhar/av-motion-planning

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Motion-planning for Self-Driving Car

Installing / Getting started

A quick introduction of the minimal setup of the environment.

# clone the project into your local repository
# "Open folder in container" on VS code using Remote Containers extendion

This should build and run the Dockerfile on your system. This is essential for working with the latest packages and dependencies required for our project

Developing

Setting up Dev and Building

To begin developing, open the terminal inside the container and run the following:

cd ~/workspaces/cs225FinalProject2022/code/MotionPlanning
mkdir build
cd build
cmake ..

All the source files will be present here

Building

If code is changed within the workspace, to realize the changes, you will have to make the project by doing the following:

cd ~/workspaces/cs225FinalProject2022/code/MotionPlanning/build

# ./main <binary pcd file> <annotation json file> <frame ID>

# example:
./main "/workspaces/cs225FinalProject2022/data/000113/lidar_roof/1616536043201.bin" "/workspaces/cs225FinalProject2022/data/000113.json" "/workspaces/cs225FinalProject2022/data/000113/lidar_roof/ 1616536043201.bin"

This will make the project. The data can be found here (large files): annotations: https://drive.google.com/drive/folders/14cI2vleBokHEtSLjZAgzmjL1cFOkoYwD lidar data: https://drive.google.com/drive/folders/1gUCYhCFIEuRePMIRzzRQGTY1L0XmbsUq

Testing

To test, write the following command:

cd ~/workspaces/cs225FinalProject2022/code/MotionPlanning/build

make test
./test

This will run the test cases present in "~/workspaces/cs225FinalProject2022/code/MotionPlanning/tests/tests.cpp"

Presentation video and report

The link to the final report: https://github.com/stuti-agrawal/cs225FinalProject2022/blob/main/report.md

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Motion planning for self-driving cars using ONCE dataset. Aims to provide framework for ONCE dataset use for self-driving cars and continuous development pipeline for experimental motion planning techniques.

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