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| Content Provider | ACM Digital Library |
|---|---|
| Author | Hwang, Inseok Leiden, Kenneth Lee, Sangjin |
| Abstract | In this paper, we propose a new framework to detect flight deck human-automation issues using onboard recorded flight data. As aircraft have become increasingly more sophisticated, today's aviation safety challenges include human-automation issues as a core area of focus, especially as the advanced flight deck has been installed into a significant portion of the operational aircraft fleet. The complexity of the advanced flight deck leads to new safety concerns such as dysfunction of the human-automation interaction. The proposed framework is to detect undesirable human-automation interactions from flight data using hybrid system modeling and intent inference. The complex behaviors of the automation and the pilot are modeled as a hybrid system and a discrete event system, respectively. Human-automation issues are then identified by detecting the inconsistencies between the inferred intents of the automation and the pilot. The framework is tested with an illustrative human-automation issue. |
| Starting Page | 1 |
| Ending Page | 8 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781450325608 |
| DOI | 10.1145/2669592.2669690 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2014-07-30 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Intent inference Human-automation interaction Discrete event system Hybrid system |
| Content Type | Text |
| Resource Type | Article |
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