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.

引用

页数：5

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