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Deep Reinforcement Learning for High-fidelity Vehicle Motion Simulation

Award Information
Agency: Department of Defense
Branch: National Geospatial-Intelligence Agency
Contract: HM047620C0041
Agency Tracking Number: NGA-P1-20-13
Amount: $100,000.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: NGA192-004
Solicitation Number: 19.2
Solicitation Year: 2019
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-05-11
Award End Date (Contract End Date): 2021-02-17
Small Business Information
15400 Calhoun Drive Suite 190
Rockville, MD 20855
United States
DUNS: 161911532
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 Roger Xu
 Vice President, Research and Development/Senior Director, Signals, Analysis and Controls
 (301) 294-5242
Business Contact
 Mark James
Phone: (301) 294-5200
Research Institution

NGA seeks to incorporate Artificial Intelligence (AI) and Machine Learning (ML) to Intelligence, Surveillance and Reconnaissance (ISR) missions in the aim to capture fleeting targets, thus a large amount of dynamic scenes with accurate target motions and behaviors will be needed for training and performance evaluation. 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 Deep Reinforcement Learning (DRL) based approach to address the NGA’s critical needs. In our proposed approach, IAI innovatively incorporates a deep reinforcement learning process into a virtual traffic simulation environment, makes the vehicle activity simulation and data generation task becoming autonomously driving a virtual vehicle on any road conditions with the flexibilities to change various parameters and constraints on the fly. The proposed approach is built on top of IAI’s previous success and experience with deep learning predictive analytics and traffic simulation in various ONR, DARPA, AFRL and FHWA programs.

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

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