Data-Driven Modeling and Analysis Framework for HMI
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