DroNet: Learning to Fly by Driving
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doi.org/10.1109/lra.2018.2795643 →Countries where authors are citing DroNet: Learning to Fly by Driving
This map shows the geographic impact of DroNet: Learning to Fly by Driving. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by DroNet: Learning to Fly by Driving with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites DroNet: Learning to Fly by Driving more than expected).
Fields of papers citing DroNet: Learning to Fly by Driving
This network shows the impact of DroNet: Learning to Fly by Driving. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the DroNet: Learning to Fly by Driving.
About DroNet: Learning to Fly by Driving
This paper, published in 2018, received 385 indexed citations . Written by Antonio Loquercio, Ana I. Maqueda, Carlos Roberto Del-Blanco and Davide Scaramuzza covering the research area of Artificial Intelligence, Automotive Engineering and Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (268 citations), Aerospace Engineering (216 citations), Artificial Intelligence (90 citations), Automotive Engineering (48 citations) and Electrical and Electronic Engineering (35 citations). Published in IEEE Robotics and Automation Letters.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/10.1109/lra.2018.2795643.