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A Review Of Instruction Methods For Collision Detection And Avoidance

Table 2:

Objectives of the exercises Exercises
The student demonstrates a correct visual scan In the aircraft and in the flight simulatorThe instructor demonstrates the appropriate scanning pattern using written briefing materials (AOPA, 2018; EGAST, 2011), VR or AR technologies. The student performs visual scanning during taxi and flight and receives feedback from the instructor. The time share of scanning the cockpit and the outside scenery, as well as the use of traffic displays should be considered (EGAST, 2011).
The student accurately estimates the time to collision and relative distance In the flight simulator with at least 190° lateral outside visual sceneryThis exercise is particularly important because most of the ab initio students are not used to estimate distance in Nautical Miles and their estimation of time to collision may be biased (Koglbauer, 2015a). The instructor generates traffic in the flight simulator and gives the traffic information as usually provided by the flight information service (e.g., “opposite traffic at 1 o’clock, 2 Nautical Miles, same altitude”). The student flies in the simulator, acknowledges the traffic information, scans the environment and announces “traffic in sight” and tries to estimate the time to collision and relative distance. The instructor freezes the simulation and gives the student feedback about the real time to collision and relative distance. With feedback and repetitions the students improve the accuracy of their estimations (Koglbauer, 2015b). Key elements are students’ understanding of how the relative speed varies in different collision geometries: Higher relative speed in opposite configurations, lower relative speed in overtaking situations.
The student decides and acts according to the rules of the air and within the safety envelope of the aeroplane In the flight simulatorwith at least 190° lateral outside visual sceneryThe instructor generates traffic in the flight simulator and gives the traffic information as usually provided by the flight information service. Exercises can include non-collision and collision scenarios (e.g., overtaking, opposite and crossing traffic with different categories of air vehicles that determine the right-of-way) (Koglbauer, 2015b). The student flies in the simulator, acknowledges the traffic information, scans the environment and announces “traffic in sight”. At this time the instructor can freeze the simulation, checks three elements and gives feedback to the student: (1) Does the students interprets the situation as a conflict? (2) If an avoidance manoeuvre is necessary, does the student select a rule-conforming manoeuvre? (3) Is the avoidance manoeuvre performed correctly (e.g., appropriate bank and return to the planned route). Initially the freezing of the simulator is necessary, but as soon as the students are able to take the right decisions there should be a number of repetitions without freezing. Key elements are students’ understanding of relative kinematics in crossing conflicts because many students erroneously believe that manoeuvring “behind” the other traffic would aggravate the conflict. Common failures are descent instead of turn, but this could lead to a conflict if the other traffic descends, too. Students need to think of coordination with the conflicting traffic and see themselves as a part of a larger system (Koglbauer & Leveson, 2017). Students may also fail to recall that the type of conflicting air vehicle can change the right of way. In the aircraftThese types of exercises can be performed in the aircraft, too, but without real traffic. The instructor gives traffic information (e.g., “opposite traffic at 1 o’clock, 2 Nautical Miles, same altitude”). The instructor checks two elements and gives feedback to the student: (1) Does the student select a rule-conforming manoeuvre? (2) Is the avoidance manoeuvre performed correctly?In a network of flight simulatorsExercises for procedures in the congested traffic pattern are described by Koglbauer and Braunstingl (2018).
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