Journal article
Modelling of unmanned aircraft visibility for see-and-avoid operations
Journal of unmanned vehicle systems, Vol.8(4), pp.1-20
06/24/2020
DOI: 10.1139/juvs-2020-0011
Abstract
With more unmanned aircraft (UA) becoming airborne each day, an already high manned aircraft to UA exposure rate continues to grow. Pilots and rulemaking authorities realize that UA visibility is a real, but unquantified, threat to operations under the see-and-avoid concept. To finally quantify the threat, a novel contrast-based UA visibility model is constructed here using collected empirical data as well as previous work on the factors affecting visibility. This work showed that UA visibility <1300 m makes a midair collision a serious threat if a manned aircraft and a UA are on a collision course while operating under the see-and-avoid concept. Similarly, this work also showed that a midair collision may be unavoidable when UA visibility is <400 m. Validating pilot and rulemaking authority concerns, this work demonstrated that UA visibility distances <1300 and <400 m occur often in the real world. Finally, the model produced UA visibility lookup tables that may prove useful to rulemaking authorities such as the U.S. Federal Aviation Administration and International Civil Aviation Organization for future work in the proof of equivalency of detect and avoid operations. Until then, pilots flying at slower airspeeds in the vicinity of UA may improve safety margins.
Details
- Title: Subtitle
- Modelling of unmanned aircraft visibility for see-and-avoid operations
- Creators
- P Highland - University of IowaJ Williams - University of IowaM Yazvec - University of IowaA Dideriksen - University of IowaN Corcoran - University of IowaK Woodruff - University of IowaC Thompson - University of IowaL Kirby - University of IowaE Chun - University of IowaH Kousheh - University of IowaJ Stoltz - University of IowaT Schnell - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Journal of unmanned vehicle systems, Vol.8(4), pp.1-20
- DOI
- 10.1139/juvs-2020-0011
- ISSN
- 2291-3467
- eISSN
- 2291-3467
- Language
- English
- Date published
- 06/24/2020
- Academic Unit
- Public Policy Center (Archive); Occupational and Environmental Health; Electrical and Computer Engineering; Industrial and Systems Engineering; Neurology
- Record Identifier
- 9984187051602771
Metrics
7 Record Views