Human–Automation Collaboration in Occluded Trajectory Smoothing

Deciding if and what objects should be engaged in a ballistic missile defense system (BMDS) scenario involves a number of complex issues. The system is large, and the timelines may be on the order of a few minutes, which drives designers to automate these systems. The critical nature of ballistic missile defense engagement decisions however, suggests exploring a human-in-the-loop approach to allow for judgment, knowledge-based decisions, and the ability to override automation decisions. This BMDS problem is reflective of the function allocation conundrum faced in many supervisory control systems, which is how to determine which functions should be mutually exclusive and which should be collaborative between humans and automation. This paper motivates and outlines two experiments that quantitatively investigated human/automation tradeoffs in the specific domain of tracking problems. Participants in both experiments were tested in their ability to smooth trajectories in different scenarios. In the first experiment, they clearly demonstrated an ability to assist an algorithm in more difficult, shorter timeline scenarios. The second experiment combined the strengths of both human and automation in order to produce a collaborative effort. Comparison of the collaborative effort to the algorithm showed that adjusting the criterion for having human participation could significantly improve solutions. Future work should focus on further examination of appropriate criteria.