  This paper develops and evaluates the performance of an allocation agent to
be potentially integrated into the onboard Detect and Avoid (DAA) computer of
an Unmanned Aircraft System (UAS). We consider a UAS that can be fully
controlled by the onboard DAA system and by a remote human pilot. With a
communication channel prone to latency, we consider a mixed initiative
interaction environment, where the control authority of the UAS is dynamically
allocated by the allocation agent. In an encounter with a dynamic intruder, the
probability of collision may increase in the absence of pilot commands in the
presence of latency. Moreover, a delayed pilot command may not result in safe
resolution of the current scenario and need to be improvised. We design an
optimization algorithm to reduce collision risk and refine delayed pilot
commands. Towards this end, a Markov Decision Process (MDP)and its solution are
employed to create a wait time map. The map consists of estimated times that
the UAS can wait for the remote pilot commands at each state. A command
blending algorithm is designed to select an avoidance maneuver that prioritizes
the pilot intention extracted from the pilot commands. The wait time map and
the command blending algorithm are implemented and integrated into a
closed-loop simulator. We conduct ten thousands fast-time Monte Carlo
simulations and compare the performance of the integrated setup with a
standalone DAA setup. The simulation results show that the allocation agent
enables the UAS to wait without inducing any near mid air collision (NMAC) and
severe loss of well clear (LoWC) while positively improve pilot involvement in
the encounter resolution.
