In this correspondence, the human factors involved in the design of effective homeland security threat detection systems are described and illustrated for radiation portal monitor (RPM) systems deployed at U.S. ports of entry. Due to the occurrence of nuisance alarms based on naturally occurring radioactive material and the low base rate of nuclear smuggling incidents, it is shown that the probability of a true threat alarm for these systems is extremely low. Receiver operating characteristic analysis of RPM systems illustrates good simple detection capability, but threat classification performance only at the chance level. Application of the human factors concept of the threat likelihood display, based on energy spectrum and cargo commodity data fusion for signal classification, reduces nuisance alarms and increases the probability of a true threat alarm to potentially effective levels. Thus, threat likelihood displays offer an approach for enhancing the effectiveness of homeland security detection and warning systems by raising the credibility of the alerts that are provided.

The number and type of unmanned vehicles sought in military operations continues to grow. A critical consideration in designing these systems is identifying interface types or interaction schemes that enhance an operator's ability to supervise multiple unmanned vehicles. Past research has explored how interface types impact overall performance measures (e.g. mission execution time), but has not extensively examined other human performance factors that might influence human-robot interaction. Within a dynamic military environment, it is particularly important to assess how interfaces impact an operator's ability to quickly adapt and alter the unmanned vehicle's tasking. To assess an operator's ability to confront this changing environment, we explored the impact of interface type on task switching. Research has shown performance costs (i.e. increased time response) when individuals switch between different tasks. Results from this study suggest that this task switching effect is also seen when participants controlling multiple unmanned vehicles switch between different strategies. Results also indicate that when utilizing a flexible delegation interface, participants did not incur as large a switch cost effect as they did when using an interface that allowed only the use of fixed automated control of the unmanned vehicles.

Three experiments and a computational analysis were conducted to investigate the effects of a delegation-type interface on human supervision of simulated multiple unmanned vehicles. Participants supervised up to eight robots using automated behaviors ("plays"), manual (waypoint) control, or both to capture the flag of an opponent with an equal number of robots, using a simple form of a delegation-type interface, Playbook. Experiment 1 showed that the delegation interface increased mission success rate and reduced mission completion time when the opponent "posture" was unpredictably offensive or defensive. Experiment 2 showed that performance was superior when operators could flexibly use both automated behaviors and manual control, although there was a small increase in subjective workload. Experiment 3 investigated additional dimensions of flexibility by comparing delegation interfaces to restricted interfaces. Eight interfaces were tested, varying in the level of abstraction at which robot behavior could be tasked and the level of aggregation (single or multiple robots) to which plays could be assigned. Performance was superior with flexible interfaces for four robots, but this benefit was eliminated when eight robots had to be supervised. Finally, a computational analysis using task-network modeling and Monte Carlo simulation gave results that closely paralleled the empirical data on changes in workload across interface type. The results provide initial empirical evidence for the efficacy of delegation-type interfaces in human supervision of a team of multiple autonomous robots.

This paper reviews literature suggesting that intermediate levels of automation (where tasks or functions are neither fully under the control of the human nor the robot) hold unique advantages for human-robotic interaction. 'Supervisory control' offers this level of human involvement, but little attention has been paid to how to achieve supervisory interactions that approximate levels of flexibility in human-human supervisory relationships. We argue that models of human-human task delegation (with more or less specific instructions on task performance) provide a useful guide to the creation of highly flexible interactions at these intermediate levels. We offer defining characteristics of delegation interaction systems and suggest the benefits that might be obtained from such systems.

Human performance and supervisory control strategies were examined using the RoboFlag simulation environment. In an emulation of a multiple unmanned vehicle mission, a single operator supervised a team of six robots using automated modes or "plays" as well as manual control. Simplified form of a delegation type interface, Playbook, was used. Effects on user performance of two factors, opponent "posture" (offensive, defensive, or mixed) and environmental uncertainty (visual range of the robots: low, medium, or high), were examined in 18 participants who completed five mission trials in each of the nine combinations of these factors. Objective performance measures and subjective assessments of mental workload and situation awareness were obtained from each participant. Opponent posture had a significant effect on the percent of missions successfully completed and the duration of the games. Both opponent posture and visual range also significantly affected the use of manual and automated control strategies. Operator strategy selection and implementation are discussed with regard to performance data and the design of supervisory control interfaces that support flexible task delegation.

Technical developments in computer hardware and software now make it possible to introduce automation into virtually all aspects of human-machine systems. Given these technical capabilities, which system functions should be automated and to what extent? We outline a model for types and levels of automation that provides a framework and an objective basis for making such choices. Appropriate selection is important because automation does not merely supplant but changes human activity and can impose new coordination demands on the human operator. We propose that automation can be applied to four broad classes of functions: 1) information acquisition; 2) information analysis; 3) decision and action selection; and 4) action implementation. Within each of these types, automation can be applied across a continuum of levels from low to high, i.e., from fully manual to fully automatic. A particular system can involve automation of all four types at different levels. The human performance consequences of particular types and levels of automation constitute primary evaluative criteria for automation design using our model. Secondary evaluative criteria include automation reliability and the costs of decision/action consequences, among others. Examples of recommended types and levels of automation are provided to illustrate the application of the model to automation design.