Metadata-Version: 2.1
Name: graph_ltpl
Version: 0.45
Summary: Multilayer graph-based local trajectory planner.
Home-page: UNKNOWN
Author: Tim Stahl
Author-email: stahl@ftm.mw.tum.de
License: UNKNOWN
Description: # Graph-Based Local Trajectory Planner
        
        ![Title Picture Local Planner](docs/source/figures/Title.png)
        
        The graph-based local trajectory planner is python-based and comes with open interfaces as well as debug, visualization
        and development tools. The local planner is designed in a way to return an action set (e.g. keep straight, pass left,
        pass right), where each action is the globally cost optimal solution for that task. If any of the action primitives is
        not feasible, it is not returned in the set. That way, one can either select available actions based on a priority list
        (e.g. try to pass if possible) or use an own dedicated behaviour planner.
        
        The planner was used on a real race vehicle during the Roborace Season Alpha and achieved speeds above 200kph.
        A video of the performance at the Monteblanco track can be found [here](https://www.youtube.com/watch?v=-vqQBuTQhQw).
        
        ### Disclaimer
        This software is provided *as-is* and has not been subject to a certified safety validation. Autonomous Driving is a
        highly complex and dangerous task. In case you plan to use this software on a vehicle, it is by all means required that
        you assess the overall safety of your project as a whole. By no means is this software a replacement for a valid 
        safety-concept. See the license for more details.
        
        
        ### Documentation
        The documentation of the project can be found [here](https://graphbasedlocaltrajectoryplanner.readthedocs.io/).
        
        
        ### Contributions
        [1] T. Stahl, A. Wischnewski, J. Betz, and M. Lienkamp,
        “Multilayer Graph-Based Trajectory Planning for Race Vehicles in Dynamic Scenarios,”
        in 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Oct. 2019, pp. 3149–3154.\
        [(view pre-print)](https://arxiv.org/pdf/2005.08664>`)
        
        Contact: [Tim Stahl](mailto:stahl@ftm.mw.tum.de).
        
        If you find our work useful in your research, please consider citing: 
        
        ```
           @inproceedings{stahl2019,
             title = {Multilayer Graph-Based Trajectory Planning for Race Vehicles in Dynamic Scenarios},
             booktitle = {2019 IEEE Intelligent Transportation Systems Conference (ITSC)},
             author = {Stahl, Tim and Wischnewski, Alexander and Betz, Johannes and Lienkamp, Markus},
             year = {2019},
             pages = {3149--3154}
           }
        ```
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
