Intent-Based CD&R: A Showcase and Evaluation of the Use of Intent in Orthogonal Constrained Urban Airspace

Abstract

The usage of drones in urban environments is expected to grow rapidly in the coming decades. To ensure the safe operations of drones, conflict detection and resolution are vital. Currently, a lot of research has gone into state-based CD&R, which has proven effective in unconstrained airspace but suffers from a large number of false positive conflicts in constrained airspace. The use of intent in constrained CD&R has the potential to reduce the number of false positive conflicts and improve the safety of drone operations significantly. In this paper, an intent-based detection and resolution method for orthogonal constrained very low-level urban airspace is presented and evaluated against a state-based method. The intent-based method calculates the future position along the trajectory at a time interval of 3 seconds for each aircraft, and conflicts are then detected by comparing these positions. The conflicts are solved utilizing a rule-based algorithm. The results show that the intent-based method has a much lower false positive rate for all traffic densities, as well as a higher average detection time before conflict for larger look-ahead times compared to the state-based method. The resolution of the state-based method, however, shows better performance with fewer losses of separation occurrences. With improvements, the intent-based method's low false positive rate, combined with the use of a larger look-ahead time, allows conflicts to be detected more reliably and earlier than the state-based method, thereby facilitating earlier conflict resolution and enhancing safety.