Cooperative Perception for Connected Vehicles

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.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

Discipline thesis:degree_discipline

Mechanical Engineering

Department dc:contributor.department

Mechanical Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2024

Author dc:creator

• Mehr, Goodarz

Chair dc:contributor.committeechair

• Eskandarian, Azim

Committee members dc:contributor.committeemember

• Stilwell, Daniel J.
• Taheri, Saied
• Akbari Hamed, Kaveh

• cooperative perception
• connected vehicles
• map fusion
• multi-agent reinforcement learning

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:40896

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/119208