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Article Excerpt
INTRODUCTION
Several proposals for future air traffic management (ATM) will change the roles of air" traffic controllers and pilots. For example, under Free Flight (FF; Radio Technical Commission for Aeronautics [RTCA1, 1995) and Distributed Air/Ground Traffic Management (DAG-TM; National Aeronautics and Space Administration [NASA], 1999), pilots would have greater freedom to choose their own heading, altitude, and speed in real time and primary responsibility for maintaining separation from other aircraft in the immediate airspace. Controllers would not be involved in active control of aircraft but would be in a role of "management by exception" (Dekker & Woods, 1999; Wickens, Mavor, Parasuraman, & McGee, 1998). Management by exception refers to a management concept in which managers are notified by staff only if a certain variable (e.g., a budget) exceeds or falls below a certain value (Drucken 1954). In the case of air traffic control (ATC), controllers would manage traffic flow, leaving the detection and resolution of conflicts to the pilots and intervene only if aircraft separation falls below a certain value (e.g., 5 nautical miles laterally and 1000 feet vertically).
The feasibility of the FF and DAG-TM concepts has been tested in studies with pilots in flight simulations (e.g., Dunbar et al., 1999: Lozito, McGann, Mackintosh, & Cashion, 1997: van Gent, Hoekstra, & Ruigrok, 1998). However, all future ATM concepts envisage a role for the controller to step in and intervene to ensure aircraft separation under certain conditions (failure of aircraft systems, bad weather', etc.). It is therefore important to examine how well controllers can detect and resolve conflicts when they are removed from the tactical control loop but then have to reenter it to ensure safety.
Several studies using moderate--to high-fidelity simulators and experienced en route controllers have shown that conflict detection performance and situation awareness were reduced and mental workload increased under such simulated FF conditions (Castano & Parasuraman, 1999; Corker, Fleming, & Lane, 1999: Endsley, Mogford, Allendoerfer, Snyder, & Stein, 1997; Endsley & Rogers, 1998: Galster. Duley, Masalonis, & Parasuraman, 2001: Metzger & Parasuraman, 2001; Willems & Truitt, 1999). (However', no adverse effects were reported by Hilburn & Parasuraman, 1997, who tested British military controllers, or by Remington, Johnston, Ruthruff, Gold, & Romera, 2000, who tested four retired controllers in a simple visual search task that required only conflict detection and no subsidiary tasks.) Air-ground integration studies involving both pilots and controllers have also been conducted. For example. DiMeo et al. (2002) found that controllers reported higher workload and showed more conservative conflict resolution behavior than did pilots, whereas pilots preferred FF scenarios and found them to be safer and to provide greater situation awareness than do current operations.
These investigations provided the first empirical evidence of the effects of future ATM concepts on controller performance and pointed to the lack of aircraft intent information (Castano & Parasuraman, 1999) and the passive monitoring role (Metzger & Parasuraman, 2001) as factors contributing to reduced controller performance. For these ATM concepts to be implemented successfully, therefore, automation support must be provided for controllers (Corker et al., 1999; Parasuraman, Duley, & Smoker, 1998), and the DAG-TM program specifically incorporates controller automation tools (NASA, 1999). One system now in operational use is the User Request Evaluation Tool (URET), developed by the MITRE Corporation. URET assists controllers in detecting potential conflicts between aircraft or between aircraft and restricted airspace and suggests resolutions by continuously checking current flight plan trajectories for strategic conflicts up to 20 min into the future. It includes sophisticated algorithms that analyze and integrate data from different sources (e.g., radar data, flight plans) considering numerous additional parameters (climb rates for different aircraft types, wind, weather models, etc.). Hence URET goes well beyond the capability of simple alerts such as the short-term conflict alert (STCA). For evaluations of the effectiveness of URET in conflict detection, see Brudnicki and McFarland (1997) and Masalonis and Parasuraman (2003).
How will these and other automated systems influence controller performance, and will they enhance or reduce safety under FF? The advent of these technologies has stimulated much research on automation and human performance (Parasuraman & Byrne, 2003: Parasuraman & Mouloua, 1996; Sheridan, 2002). A major conclusion is that automation fundamentally changes the nature of the cognitive demands and responsibilities of human operators, often in ways that were unintended or unanticipated by the designers (Bainbridge, 1983: Billings, 1997: Parasuraman & Riley, 1997: Sarter & Amalberti, 2000: Wiener & Curry; 1980). Previous research and experience shows that automation leads to both benefits and costs. Among the human performance costs of certain automation designs are unbalanced mental workload, complacency, reduced situation awareness, cognitive skill loss, and poorly calibrated trust (Parasuraman & Riley). Such effects have been discussed in design guidelines for automation for future ATM (Ahlstrom, Longo, & Truitt, 2002), but these have been derived mostly from research on cockpit automation. There is as yet little empirical work on controller performance with automation. particularly in relation to ATM concepts in which controllers share their decisions with other members of the system and assume a rather passive role.
The current study focused on two aspects of controller-automation interaction: (a) whether automation can reduce controller workload and (b) how automation reliability affects controller performance and workload (Wickens, 2000).
Automation is often implemented in an attempt to reduce the operator's workload during peak periods of task load. However, this does not always occur. For example, cockpit automation has sometimes reduced mental workload in phases of flight when workload was already low (e.g., autopilot during the cruise phase) and increased mental workload in phases of flight when workload was already high (e.g., reprogramming the flight management system during final approach), a phenomenon referred to as clumsy automation (Wiener & Curry, 1980). In addition, automation often changes manual control tasks to monitoring tasks, leaving the human to supervise the automation (Sheridan, 2002), which can impose considerable workload (Warm, Dember, & Hancock, 1996).
The second area of concern is the ability of human operators to manage a system when automation fails or malfunctions in some way. This has been referred to as the out-of-the-loop unfamiliarity (OOTLUF) problem (Wickens, 1992). In addition, several studies have examined the effects of imperfect or unreliable automation on operator performance in target detection and complex decision-making tasks (Galster, Bolia, Roe, & Parasuraman, 2001; Rovira, McGarry, & Parasuraman, 2002; Wickens, Gempler, & Morphew, 2000). The results generally showed that operators have difficulties in detecting targets or making effective decisions if the automation incorrectly highlights a low-priority target or gives incorrect advice. The OOTLUF problem also results in operators requiring more time to intervene under automated control than under manual control because they have to first regain awareness of the state of the system. Operators have a better mental model or awareness of the system state when they are actively involved in creating the state of the system than when they are passively monitoring the actions of another agent or automation (Endsley, 1996; Endsley & Kiris, 1995), particularly if the automation interface does not support the operator in gathering the raw information on which the automation bases its decisions (Lorenz, Di Nocera, Rottger, & Parasuraman, 2002).
This problem seems particularly relevant to the problem of automation in future ATM concepts because the shared decision making can already take the controller out of the control loop and limit the controller's access to the information (e.g., pilot intent) relevant to conflict detection and resolution. If automation is then introduced to compensate for the effects of reduced situation awareness induced by the transfer of decision-making authority away from the controller to the pilot or dispatchei; the OOTLUF problem might be further aggravated with imperfect automation when the controller is expected to detect and resolve conflicts despite being initially "remote" from the control loop.
Although some decision aids may improve performance under current ATC conditions (e.g., Hilburn, 1996; Schick & Volckers, 1991), no em pirical data are available on the effects of automation on controller performance and mental workload under FF and other future ATM systems. Automating the decision-making process in a dynamic environment such as ATC is not a trivial task, especially under conditions of shared decision making. A powerful decision aid will have to accurately predict pilot intentions, weather; and wind. Under traditional ATC conditions, pilots always had to follow the direction of the controller and, typically, stay on assigned airways. Therefore, pilot intent was relatively easy to predict. If the pilots indeed followed ATC instructions, they did whatever the controller told them to do. With the introduction of the National Route Program in the late 1990s, these restrictions were loosened, and under FF conditions pilots...
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