Probabilistic modeling of air and ground vehicle trajectories
Abstract/Contents
- Abstract
- Safe integration of autonomous vehicles into their traffic environments requires modeling human behavior. This thesis presents techniques to model vehicle trajectories that are representative of human driver and pilot behavior in the real world. These models can be used to build simulations for developing and verifying safety-critical traffic systems. Modeling vehicle trajectories can be challenging due to several reasons specific to the context. This thesis presents modeling approaches that address the challenges in each context. For autonomous cars, modeling trajectories can be challenging due to the inherent uncertainty in human behavior and complex interactions among drivers. While a driver model can be learned directly from data, we generally want our models to be interpretable. This thesis addresses this challenge by proposing a framework that combines physics-based driver models with a data-driven learning approach. For conventional types of aircraft, trajectories depend on the flight procedures and instructions from air traffic controllers. In dense airspace, however, aircraft behavior may vary due to factors including the preferences of air traffic controllers and the correlations among multiple aircraft. This thesis proposes a probabilistic model that learns the relation between aircraft and procedures in each flight stage. The trained model then can be used for generating trajectories given unseen traffic environment with procedural information. Further, the thesis proposes a method to incorporate correlations among multiple aircraft. For new types of aircraft such as urban air mobility systems, the major challenge for building models is the lack of available data. This thesis introduces an interactive software package called CONTRAIL, which provides an interface for building an aircraft encounter model by geometrically defining the nominal encounter paths and specifying a probability distribution over the nominal paths. Instead of learning aircraft behavior in a data-driven way, field experts can use this tool to create encounter models and generate realistic trajectories for new categories of aircraft.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2023; ©2023 |
| Publication date | 2023; 2023 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Jung, Soyeon |
|---|---|
| Degree supervisor | Kochenderfer, Mykel J, 1980- |
| Thesis advisor | Kochenderfer, Mykel J, 1980- |
| Thesis advisor | Alonso, Juan José, 1968- |
| Thesis advisor | Schwager, Mac |
| Degree committee member | Alonso, Juan José, 1968- |
| Degree committee member | Schwager, Mac |
| Associated with | Stanford University, School of Engineering |
| Associated with | Stanford University, Department of Aeronautics and Astronautics |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Soyeon Jung. |
|---|---|
| Note | Submitted to the Department of Aeronautics and Astronautics. |
| Thesis | Thesis Ph.D. Stanford University 2023. |
| Location | https://purl.stanford.edu/st759xy4267 |
Access conditions
- Copyright
- © 2023 by Soyeon Jung
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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