Towards Robust Certification of Computer-Vision-Based Detect and Avoid in UAS

被引：0
|
作者：
Sridhar, Rahul [1 ]
Murphy, Michael [2 ]
Lamprecht, Andreas [3 ]
机构：
[1] Texas A&M Univ, Dept Comp Sci, College Stn, TX 77843 USA
[2] AirMap, Adv Operat Grp, Santa Monica, CA USA
[3] AirMap, Adv Operat Grp, Munich, Germany
来源：
2020 AIAA/IEEE 39TH DIGITAL AVIONICS SYSTEMS CONFERENCE (DASC) PROCEEDINGS | 2020年
关键词：
Detect and Avoid; Unmanned Aircraft Systems; Computer Vision;
D O I：
10.1109/dasc50938.2020.9256697
中图分类号：
V [航空、航天];
学科分类号：
08 ; 0825 ;
摘要：
Computer-vision-based Detect and Avoid (DAA) promises to enable large-scale unmanned BVLoS drone flights in the near future, which would have enormous economic benefits. However, quite a bit of work remains to be done before it can be certified. We describe AirMap's ongoing research into computer-vision-based DAA.
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页数：5
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