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VMS: Deep Reinforcement Learning for High-fidelity Vehicle Motion Simulation

Award Information
Agency: Department of Defense
Branch: National Geospatial-Intelligence Agency
Contract: HM047621C0056
Agency Tracking Number: NGA-P2-21-24
Amount: $999,992.00
Phase: Phase II
Program: SBIR
Solicitation Topic Code: NGA192-004
Solicitation Number: 19.2
Solicitation Year: 2019
Award Year: 2021
Award Start Date (Proposal Award Date): 2021-08-27
Award End Date (Contract End Date): 2023-08-31
Small Business Information
15400 Calhoun Drive Suite 190
Rockville, MD 20855-2814
United States
DUNS: 161911532
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Bryan Stewart
 (301) 294-5200
Business Contact
 Mark James
Phone: (301) 294-5221
Research Institution

NGA seeks to incorporate Artificial Intelligence (AI) and Machine Learning (ML) into Intelligence, Surveillance, and Reconnaissance (ISR) missions to capture fleeting targets. As such, a large number of dynamic scenes with accurate target motions and behaviors will be needed for training and performance evaluation. The traditional microscopic model-based approach for vehicle activity simulation is unable to produce enough fidelity for the training and the evaluation of ISR tracking, analytics, or collection strategies. Intelligent Automation Inc. (IAI) proposes to develop a high-fidelity Vehicle Motion Simulation (VMS) system that includes a framework for SUMO (Simulation of Urban Mobility) based traffic simulation and validation with real-world roadway sensor data coupled with a deep reinforcement learning (DRL) model for activity simulation of target moving vehicles. In Phase I, we successfully demonstrated the feasibility of our proposed VMS framework and we have made significant progress towards the ultimate goals of this project. In Phase II, we plan to extend the scope of our simulation and reinforcement learning framework and build a full-fledged prototype software tool that addresses NGA’s needs.

* Information listed above is at the time of submission. *

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