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Introduction
Introduction
Objective: to design a method to intelligently classify intent conflict events and enable detailed analysis by subject matter experts (SMEs)
Consensus-based Multi-step Clustering and Model Creation
Consensus-based Multi-step Clustering and Model Creation
Intent conflicts (or mode confusions) detected by the intent-based mode-confusion detection algorithm are taken as inputs.
Similar types of HMI issues (or mode confusions) are automatically grouped into several consensus clusters
Each consensus cluster can be succinctly represented by ‘representative’ event
HMI Issue Detection Results
HMI Issue Detection Results
Data reduction capability of consensus-based multi-step clustering technique
Example: Cluster 32
Feature consistency: 83%
No. primary conflicts: 11
No. intent conflict events: 8,593
Intent conflicts only in the vertical dimension
Long-duration delays in autopilot beginning descent after a new MCP target is selected
Intent: -1: Descend, 0: Constant Altitude, 1: Climb
VMODE: 1: Vertical Auto, 2: Altitude Hold
TMODE: 9: Mach off
Flight Phase: 5: Cruise, 6: Descent
Related Publication
Related Publication
A. Vaidya, S. Lee, and I. Hwang, “Data-Driven Modeling and Analysis Framework for Cockpit Human–Machine Interaction Issues ,” AIAA Journal of Aerospace Information Systems,October 2016, (online) http://dx.doi.org/10.2514/1.I01046
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