Trajectory forecasting in the modern robotic autonomy stack
Abstract/Contents
- Abstract
- Autonomous systems are increasingly nearing widespread adoption, with new robotic platforms constantly being tested and deployed alongside humans in domains such as autonomous driving, service robotics, and surveillance. Accordingly, human-robot interaction will soon be present in many everyday scenarios. However, there are still many challenges preventing autonomous systems from safely and smoothly navigating interactions with humans. For example, while merging into traffic is one of the most common day-to-day maneuvers we perform as drivers, it poses a major problem for state-of-the-art self-driving vehicles. The reason humans can naturally navigate through many social interaction scenarios, such as merging in traffic, is that humans have an intrinsic capacity to reason about other people's intents, beliefs, and desires, applying this reasoning to predict what might happen in the future and make corresponding decisions. As a result, imbuing autonomous systems with the ability to reason about other agents' potential future actions is critical to enabling informed decision making and proactive actions to be taken in human-robot interaction scenarios. Indeed, the ability to predict other agents' behaviors (also known as "trajectory forecasting") has already become a core component of modern robotic systems, especially so in safety-critical applications such as autonomous vehicles. Towards this end, this dissertation tackles the development of trajectory forecasting methods, their effective integration within the robotic autonomy stack, and the injection of task-awareness in their performance evaluation.
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 | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Ivanovic, Boris | |
|---|---|---|
| Degree supervisor | Pavone, Marco, 1980- | |
| Thesis advisor | Pavone, Marco, 1980- | |
| Thesis advisor | Sadigh, Dorsa | |
| Thesis advisor | Schwager, Mac | |
| Degree committee member | Sadigh, Dorsa | |
| Degree committee member | Schwager, Mac | |
| Associated with | Stanford University, Department of Aeronautics and Astronautics |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Boris Ivanovic. |
|---|---|
| Note | Submitted to the Department of Aeronautics and Astronautics. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/nw436bv8593 |
Access conditions
- Copyright
- © 2021 by Boris Ivanovic
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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