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In-Depth Information
Real collision avoidance systems have imperfect sensors, which results in state
uncertainty. TCAS currently relies on radar beacon surveillance, which results in some-
what significant uncertainty in the intruder bearing. When state uncertainty is signifi-
cant, the uncertainty must be taken into account when choosing actions. With a sensor
model, the problem may be transformed into a partially observable Markov decision
process (POMDP) and solved approximately using various methods [7,24,17].
Another area of further research involves introducing coordination between aircraft.
If both aircraft have a collision avoidance system on board, then safety can be enhanced
by coordinating their maneuvers. If either the sensor measurements are perfect or the
communication between aircraft is perfect and unlimited, then the problem can be mod-
eled as a larger MDP. Otherwise, the problem turns into a Decentralized POMDP (Dec-
POMDP), which are, in general, impractical to solve exactly [2]. Further research will
investigate the performance of MDP-derived policies and strategies for leveraging the
structure of the problem to reduce the complexity of finding an acceptable solution.
Acknowledgements.
This work is the result of research sponsored by the TCAS Pro-
gram Office at the Federal Aviation Administration. The authors appreciate the support
provided by the TCAS Program Manager, Neal Suchy. This work has benefited from
discussions with Leslie Kaelbling and Tomas Lozano-Perez from the MIT Computer
Science and Artificial Intelligence Laboratory.
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