Doctor of Philosophy / Self-driving cars promise a future with safer roads and reduced traffic incidents and fatalities. This future hinges on the car's accurate understanding of its surrounding environment; however, the reliability of the algorithms that form this perception is not always guaranteed and adverse traffic and environmental conditions can significantly diminish the performance of these algorithms. To solve this problem, this research builds on the idea that enabling cars to share and exchange information via communication allows them to extend the range and quality of their perception beyond their capability. To that end, this research formulates a robust and flexible framework for cooperative perception, explores how connected vehicles can learn to collaborate to improve their perception, and introduces an affordable, experimental vehicle platform for connected autonomy research.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/119208 |
| Date | 31 May 2024 |
| Creators | Mehr, Goodarz |
| Contributors | Mechanical Engineering, Eskandarian, Azim, Stilwell, Daniel J., Taheri, Saied, Akbari Hamed, Kaveh |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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