https://github.com/TUMFTM/GraphBasedLocalTrajectoryPlanner

Local trajectory planner based on a multilayer graph framework for autonomous race vehicles.
https://github.com/TUMFTM/GraphBasedLocalTrajectoryPlanner

graph planner

Last synced: about 2 months ago
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Local trajectory planner based on a multilayer graph framework for autonomous race vehicles.

• Host: GitHub
• URL: https://github.com/TUMFTM/GraphBasedLocalTrajectoryPlanner
• Owner: TUMFTM
• License: lgpl-3.0
• Created: 2020-05-15T08:16:44.000Z (about 4 years ago)
• Default Branch: master
• Last Pushed: 2023-07-06T21:54:20.000Z (12 months ago)
• Last Synced: 2024-02-04T15:54:34.182Z (5 months ago)
• Topics: graph, planner
• Language: Python
• Homepage:
• Size: 12.5 MB
• Stars: 217
• Watchers: 7
• Forks: 52
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Lists

• awesome-robotic-tooling - GraphBasedLocalTrajectoryPlanner - Was used on a real race vehicle during the Roborace Season Alpha and achieved speeds above 200km/h. (Planning and Control / Vector Map)
• awesome-robotic-tooling - GraphBasedLocalTrajectoryPlanner - Was used on a real race vehicle during the Roborace Season Alpha and achieved speeds above 200km/h. (Planning and Control / Vector Map)
• Awesome-Autonomous-Driving - GraphBasedLocalTrajectoryPlanner - Was used on a real race vehicle during the Roborace Season Alpha and achieved speeds above 200km/h. (6. Planning / 3.4 High Performance Inference)

README

        
# 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:[email protected]).

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}

   }

```