| Name | commonroad-clcs JSON |
| Version |
2025.1.0
JSON |
| download |
| home_page | None |
| Summary | Curvilinear (Frenet) Coordinate System for motion planning. |
| upload_time | 2025-02-17 09:08:43 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | <3.12,>=3.9 |
| license | BSD 3-Clause License Copyright (c) 2019, Technical University of Munich, Professorship Cyber-Physical Systems. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: * Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. * Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. |
| keywords |
autonomous vehicles
automated driving
motion planning
frenet coordinates
commonroad
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# CommonRoad Curvilinear Coordinate System (CLCS)
Curvilinear coordinate frames (also Frenet frames) are a widely used representation in motion planning for automated
vehicles. Curvilinear frames are aligned with a reference path and Cartesian points (x, y) are described by
coordinates (s, d), where s is the arc length and d the lateral deviation to the reference path.
The coordinate transformation is performed via a projection on the reference path for which different methods can be
used [1] [2].
<img src="docs/assets/animation.gif" alt="clcs" width="500"/>
This project software for constructing and using curvilinear coordinate frames for reference paths given as polylines.
We offer:
* C++ backend for efficient coordinate transformations
* Python frontend for usage with CommonRoad scenarios
* various utility methods for processing reference path (e.g., smoothing, curvature reduction, resampling etc ...)
## :wrench: Installation
We provide two installation options for CommonRoad-CLCS:
Installation as a Python package (recommended) or building from source.
1. **Python Package**: Install the python package via `pip` in your Conda environment:
```bash
pip install commonroad-clcs
```
2. **Build from source**: To build the project from source and install it in your Conda environment,
please refer to the [README_FOR_DEVS](./README_FOR_DEVS.md).
This option is only recommended for advanced users who would like to use or modify the C++ backend directly.
## :link: System Requirements
The software is written in Python 3.10 and C++17, and was tested on Ubuntu 20.04 and 22.04.
It should be compatible with later versions.
For building the code, the following minimum versions are required:
* **GCC and G++**: version 10 or above
* **CMake**: version 3.28 or above.
* **Pip**: version 24.2 or above
We recommend using [Anaconda](https://www.anaconda.com/) as a package manager for Python.
## :rocket: Getting Started
To get started, please see the minimal example in `./tutorials/clcs_minimal_example.py`.
For more details, please refer to the Jupyter notebook tutorials provided in `./tutorials/`.
## :books: Documentation
A documentation including APIs is available on our website at:
https://cps.pages.gitlab.lrz.de/commonroad/commonroad-clcs/.
To build the documentation locally, please install the optional `docs` listed in [pyproject.toml](pyproject.toml) first.
The documentation can then be built using mkdocs via:
```bash
mkdocs build
```
You can browse the doc by launching `./docs/site/index.html`
## :busts_in_silhouette: Authors
**Contributors** (alphabetic order by last name): Peter Kocsis, Edmond Irani Liu,
Stefanie Manzinger, Tobias Markus, Evald Nexhipi, Vitaliy Rusinov, Daniel Tar, Gerald Würsching
## :speech_balloon: References
If you use our software for research, please cite:
```
@inproceedings{wursching2024robust,
author={W{\"u}rsching, Gerald and Althoff, Matthias},
title={Robust and Efficient Curvilinear Coordinate Transformation with Guaranteed Map Coverage for Motion Planning},
booktitle={Proc. of the IEEE Intelligent Vehicles Symposium},
year={2024}
}
```
#### Additional references:
[1] [Héry, Elwan, Stefano Masi, Philippe Xu, and Philippe Bonnifait. "Map-based curvilinear coordinates for autonomous vehicles." ITSC, 2017](https://ieeexplore.ieee.org/document/8317775)
[2] [Bender, Philipp, Julius Ziegler, and Christoph Stiller. "Lanelets: Efficient map representation for autonomous driving." IV, 2014](https://ieeexplore.ieee.org/document/6856487)
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"description": "# CommonRoad Curvilinear Coordinate System (CLCS)\n\nCurvilinear coordinate frames (also Frenet frames) are a widely used representation in motion planning for automated\nvehicles. Curvilinear frames are aligned with a reference path and Cartesian points (x, y) are described by \ncoordinates (s, d), where s is the arc length and d the lateral deviation to the reference path.\nThe coordinate transformation is performed via a projection on the reference path for which different methods can be \nused [1] [2].\n\n<img src=\"docs/assets/animation.gif\" alt=\"clcs\" width=\"500\"/>\n\nThis project software for constructing and using curvilinear coordinate frames for reference paths given as polylines.\nWe offer:\n* C++ backend for efficient coordinate transformations\n* Python frontend for usage with CommonRoad scenarios\n* various utility methods for processing reference path (e.g., smoothing, curvature reduction, resampling etc ...)\n\n## :wrench: Installation\nWe provide two installation options for CommonRoad-CLCS: \nInstallation as a Python package (recommended) or building from source.\n\n1. **Python Package**: Install the python package via `pip` in your Conda environment:\n ```bash\n pip install commonroad-clcs\n ```\n2. **Build from source**: To build the project from source and install it in your Conda environment, \n please refer to the [README_FOR_DEVS](./README_FOR_DEVS.md).\n This option is only recommended for advanced users who would like to use or modify the C++ backend directly.\n\n\n\n## :link: System Requirements\nThe software is written in Python 3.10 and C++17, and was tested on Ubuntu 20.04 and 22.04.\nIt should be compatible with later versions.\nFor building the code, the following minimum versions are required:\n* **GCC and G++**: version 10 or above\n* **CMake**: version 3.28 or above.\n* **Pip**: version 24.2 or above\n\nWe recommend using [Anaconda](https://www.anaconda.com/) as a package manager for Python.\n\n\n## :rocket: Getting Started\nTo get started, please see the minimal example in `./tutorials/clcs_minimal_example.py`.\n\nFor more details, please refer to the Jupyter notebook tutorials provided in `./tutorials/`. \n\n\n## :books: Documentation\nA documentation including APIs is available on our website at:\nhttps://cps.pages.gitlab.lrz.de/commonroad/commonroad-clcs/.\n\nTo build the documentation locally, please install the optional `docs` listed in [pyproject.toml](pyproject.toml) first.\nThe documentation can then be built using mkdocs via:\n```bash\nmkdocs build\n```\nYou can browse the doc by launching `./docs/site/index.html`\n\n## :busts_in_silhouette: Authors\n**Contributors** (alphabetic order by last name): Peter Kocsis, Edmond Irani Liu,\nStefanie Manzinger, Tobias Markus, Evald Nexhipi, Vitaliy Rusinov, Daniel Tar, Gerald W\u00fcrsching\n\n## :speech_balloon: References\nIf you use our software for research, please cite:\n```\n@inproceedings{wursching2024robust,\n author={W{\\\"u}rsching, Gerald and Althoff, Matthias},\n title={Robust and Efficient Curvilinear Coordinate Transformation with Guaranteed Map Coverage for Motion Planning},\n booktitle={Proc. of the IEEE Intelligent Vehicles Symposium},\n year={2024}\n}\n```\n\n#### Additional references:\n[1] [H\u00e9ry, Elwan, Stefano Masi, Philippe Xu, and Philippe Bonnifait. \"Map-based curvilinear coordinates for autonomous vehicles.\" ITSC, 2017](https://ieeexplore.ieee.org/document/8317775)\n\n[2] [Bender, Philipp, Julius Ziegler, and Christoph Stiller. \"Lanelets: Efficient map representation for autonomous driving.\" IV, 2014](https://ieeexplore.ieee.org/document/6856487)\n",
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