Online risk-aware conditional planning with qualitative autonomous driving applications
Name
1036985630-MIT.pdf
Description
Full printable version
Size
9.23 MB
Format
Adobe PDF
Checksum (MD5)
f52b20599e24ea5a8932a8235fc33247
Author(s)
Deyo, Matthew Quinn
Advisor(s)
Brian C. Williams.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
Driving is often stressful and dangerous due to uncertainty in the actions of nearby vehicles. Having the ability to model driving maneuvers qualitatively and guarantee safety bounds in uncertain traffic scenarios are two steps towards building trust in vehicle autonomy. In this thesis, we present an approach to the problem of Qualitative Autonomous Driving (QAD) using risk-bounded conditional planning. First, we present Incremental Risk-aware AO* (iRAO*), an online conditional planning algorithm that builds off of RAO* for use in larger dynamic systems like driving. An illustrative example is included to better explain the behavior and performance of the algorithm. Second, we present a Chance-Constrained Hybrid Multi-Agent MDP as a framework for modeling our autonomous vehicle in traffic scenarios using qualitative driving maneuvers. Third, we extend our driving model by adding variable duration to maneuvers and develop two approaches to the resulting complexity. We present planning results from various driving scenarios, as well as from scaled instances of the illustrative example, that show the potential for further applications. Finally, we propose a QAD system, using the different tools developed in the context of this thesis, and show how it would fit within an autonomous driving architecture.
Description
Thesis: S.M., Massachusetts Institute of Technology, Department of Aeronautics and Astronautics, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 89-91).
Subjects
Aeronautics and Astronautics.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
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