Non-intrusive Hazardous Pilot Cognitive State Assessment via Semi-Supervised Deep Learning: CSA-Deep

Award Information
Agency:
National Aeronautics and Space Administration
Branch
n/a
Amount:
$125,000.00
Award Year:
2012
Program:
SBIR
Phase:
Phase I
Contract:
NNX12CF13P
Award Id:
n/a
Agency Tracking Number:
115442
Solicitation Year:
2011
Solicitation Topic Code:
A1.08
Solicitation Number:
n/a
Small Business Information
MD, Suite 400, Rockville, MD, 20855-2737
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
Y
Duns:
161911532
Principal Investigator:
Guangfan Zhang
Principal Investigator
(301) 294-5244
gzhang@i-a-i.com
Business Contact:
Mark James
Business Official
(301) 294-5221
mjames@i-a-i.com
Research Institution:
Stub




Abstract
In aviation history, many crew-related errors are caused by crew members being in hazardous cognitive states, such as overstress, disengagement, high fatigue, and ineffective crew coordination. To improve aviation safety, it is critical to monitor and predict hazardous cognitive states of crew members in a non-intrusive manner for designing mitigation strategies. In Next Generation Air Transportation System (NextGen) flight deck, emerging technologies will enable a transition from ground based navigation infrastructure to satellite based navigation and some control relating to separation of traffic will be delegated to the cockpit from Air Traffic Control (ATC). While the NextGen system will bring tremendous advantages in operational efficiency, the responsibilities of the pilot are expected to dramatically increase, which makes the hazardous cognitive state assessment even more critical.To address the above challenges, Intelligent Automation, Inc. (IAI), along with the Operator Performance Lab (OPL) in University of Iowa and Old Dominion University, proposes a real-time hazardous pilot Cognitive State Assessment system, called CSA-Deep, in all phases of flight for Integrated Crew-System Interaction (ICSI). The key innovation of the proposed research is the modeling and adaptive updating of hazardous cognitive states using a large amount of unlabeled data through semi-supervised deep learning.

* information listed above is at the time of submission.

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