Real-time Deep Reinforcement Learning for Evacuation under Emergencies
Aviation emergencies pose high risks to humans, when it occurs, it is imperative to evacuate humans to safe places in an efficient manner. Under the high level of time pressure, decision-makers are facing great challenges of developing an optimal evacuation quickly, especially when the threats involved are not static and the environment is unfamiliar. The research team plans to integrate Asynchronous Advantage Actor Critic (A3C) algorithm with the velocity obstacle (VO) models to optimize evacuation in an airport environment under emergencies. A multi-agent collaborative evacuation modeling framework for a complex environment with moving threats will be developed to provide adaptive continuous decision-aid to each agent, in accordance to the changing environments.
Language
- English
Project
- Status: Active
- Funding: $208835
-
Sponsor Organizations:
Center for Advanced Transportation Mobility
North Carolina Agricultural and Technical State University
Greensboro, NC United States 27411Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590 -
Managing Organizations:
North Carolina A&T State University
1601 E. Market Street
Greensboro, NC United States 27411 -
Performing Organizations:
Embry-Riddle Aeronautical University
600 S. Clyde Morris Boulevard
Daytona Beach, Fl United States 32114 -
Principal Investigators:
Liu, Dahai
- Start Date: 20211001
- Expected Completion Date: 20230731
- Actual Completion Date: 0
- USDOT Program: University Transportation Centers Program
Subject/Index Terms
- TRT Terms: Air transportation; Algorithms; Emergencies; Evacuation; Machine learning
- Subject Areas: Aviation; Security and Emergencies;
Filing Info
- Accession Number: 01841348
- Record Type: Research project
- Source Agency: Center for Advanced Transportation Mobility
- Files: UTC, RIP
- Created Date: Apr 2 2022 11:21AM