(212 words OK)Objective: To explore how a single master alarm system affects drivers' responses when compared to multiple, distinct warnings. Background: Advanced driver warning systems are intended to improve safety, yet inappropriate integration may increase the complexity of driving, especially in high workload situations. This study investigated the effects of auditory alarm scheme, reliability, and collision event-type on driver performance. Method: A 2x2x4 mixed factorial design investigated the impact of two alarm schemes (master vs. individual) and two levels of alarm reliability (high and low) on distracted drivers' performance across four collision event-types (frontal collision warnings, left and right lane departure warnings, and followvehicle fast approach). Results: Participants' reaction times and accuracy rates were significantly affected by the type of collision event and alarm reliability. The use of individual alarms, rather than a single master alarm, did not significantly affect driving performance in terms of reaction time or response accuracy. Conclusion: Even though a master alarm is a relatively uninformative warning, it produced statistically no different reaction times or accuracy results when compared to information-rich auditory icons, some of which were spatially located. In addition, unreliable alarms negatively impacted driver performance, regardless of event type or alarm scheme.Application: These results have important implications for the development and implementation of multiple driver warning systems.

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This paper describes ongoing efforts to address the challenges of supervising teams of heterogeneous unmanned vehicles through the use of demonstrated Ecological Interface Design (EID) principles. We first review the EID framework and discuss how we have applied it to the unmanned systems domain. Then, drawing from specific interface examples, we present several generalizable design strategies for improved supervisory control displays. We discuss how ecological display techniques can be used to increase the transparency and observability of highly automated unmanned systems by enabling operators to efficiently perceive and reason about automated support outcomes and purposefully direct system behavior.

Worker Competencies Analysis (WCA) is the fifth and final phase of the Cognitive Work Analysis (CWA) framework. Unlike the earlier four phases, there is a dearth of published work illustrating how WCA is conducted within the context of CWA. The lack of concrete examples of the application of WCA has both practical and pedagogical ramifications, making it difficult to perform and understand this phase of analysis. This paper attempts to address this gap. Following a review of the CWA framework, WCA is introduced with the Skill, Rules, and Knowledge (SRK) taxonomy. Then, a methodological tool for structuring and capturing the execution of WCA—the SRK Inventory—is presented. Finally, a practical application of the SRK Inventory to a TRACON microworld is discussed. This paper is intended to serve as a resource to future CWA practitioners and researchers, and to stimulate discussion of methods and tools for better supporting WCA activities.