Daniel Göhring

438 citations
21 papers · 343 · h-index 9

Impact in

Papers in

Daniel Göhring

18 papers receiving 309 citations

Peers

Daniel Göhring
Comparison fields: 5 of 53
  • Automotive Engineering 79
  • Instrumentation 21
  • Computer Vision and Pattern Recognition 106
  • Aerospace Engineering 118
  • Cognitive Neuroscience 66
Replace Kyhyun Um with:
Kyhyun Um South Korea
Sanqing Qu China
Tolga Özaslan United States
Yaodong Cui Canada
Mohammad Al-Sharman United Arab Emirates
Dong-Sung Pae South Korea
Xavier Clady France
Chunguang Bu China
Soo-Yeong Yi South Korea
Enrique Santiso Spain
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Citations per field
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Citations per year

Countries citing papers authored by Daniel Göhring

Since Specialization
Citations

This map shows the geographic impact of Daniel Göhring's research. 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 Daniel Göhring with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Göhring more than expected).

Fields of papers citing papers by Daniel Göhring

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel Göhring. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Daniel Göhring. The network helps show where Daniel Göhring may publish in the future.

Co-authors

The 11 scholars most cited alongside Daniel Göhring, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Daniel Göhring Line = papers co-authored together Daniel Göhring links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 21 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2011106
2 201279
3 200536
4 200631
5 201115
6 200613
7 202010
8 20059
9 20218
10 20066
11 20095
12 20085
13 20094
14
CooperativeWorld Modeling in Dynamic Multi-Robot Environments
20073
15 20083
16
How to Get from Interpolated Keyframes to Neural Attractor Landscapes and Why.
20073
17 20133
18 20202
19 20071
20 20081

About Daniel Göhring

Daniel Göhring is a scholar working on Artificial Intelligence, Aerospace Engineering, Computer Vision and Pattern Recognition, Computer Networks and Communications and Control and Systems Engineering, having authored 21 papers that have together received 343 indexed citations. Recurring topics across this work include Robotics and Sensor-Based Localization (11 papers), Target Tracking and Data Fusion in Sensor Networks (9 papers), Robotic Path Planning Algorithms (4 papers), Autonomous Vehicle Technology and Safety (3 papers), Data Management and Algorithms (3 papers), AI-based Problem Solving and Planning (3 papers), Indoor and Outdoor Localization Technologies (3 papers) and Constraint Satisfaction and Optimization (3 papers). The work is most often cited by research in Automotive Engineering (79 citations), Instrumentation (21 citations), Computer Vision and Pattern Recognition (106 citations), Aerospace Engineering (118 citations) and Cognitive Neuroscience (66 citations). Daniel Göhring has collaborated with scholars based in Germany and United States. Frequent co-authors include Miao Wang, Raúl Rojas, Hans-Dieter Burkhard, Matthias Jüngel, J. A. Hoffman, Jan Hoffmann, Bingyi Cao, Miao Wang, Michael Spranger and Andreas Philipp. Their work appears in journals such as Springer tracts in advanced robotics, Lecture notes in computer science, Fundamenta Informaticae, KI - Künstliche Intelligenz and Advances in intelligent systems and computing.

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.

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