NASA ULI Aircraft Taxi Dataset
Abstract/Contents
- Abstract
- This artifact provides data to train machine learning models for autonomous aircraft maneuvers. In particular, it provides images from a camera mounted on a simulated aircraft's wing coupled with corresponding state estimation, such as distance along a run-way or angle of deviation from a run-way center-line. Using this data, based on the X-Plane Simulator, we have trained machine learning models for the NASA University Leadership Initiative on Safe Aviation Autonomy under PI Dr. Pavone with multi-institution collaborators.
Description
| Type of resource | software, multimedia |
|---|---|
| Date created | April 19, 2021 |
Creators/Contributors
| Author | Katz, Sydney M. |
|---|---|
| Author | Corso, Anthony |
| Author | Chinchali, Sandeep |
| Author | Elhafsi, Amine |
| Author | Sharma, Apoorva |
| Author | Kochenderfer, Mykel J. |
| Author | Pavone, Marco |
Subjects
| Subject | Machine Learning |
|---|---|
| Subject | Autonomous Systems |
| Subject | Aviation |
| Genre | Dataset |
Bibliographic information
Access conditions
- Use and reproduction
- User agrees that, where applicable, content will not be used to identify or to otherwise infringe the privacy or confidentiality rights of individuals. Content distributed via the Stanford Digital Repository may be subject to additional license and use restrictions applied by the depositor.
- License
- This work is licensed under a Creative Commons Attribution 3.0 Unported license (CC BY).
Preferred citation
- Preferred Citation
- Katz, Sydney M. and Corso, Anthony and Chinchali, Sandeep and Elhafsi, Amine and Sharma, Apoorva and Kochenderfer, Mykel J. and Pavone, Marco. (2021). NASA ULI Aircraft Taxi Dataset. Stanford Digital Repository. Available at: https://purl.stanford.edu/zz143mb4347
Collection
Stanford Research Data
View other items in this collection in SearchWorksContact information
- Contact
- pavone@stanford.edu
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