Frederik Diehl

512 citations
4 papers · 116 indexed · h-index 4
Topics
Autonomous Vehicle Technology and Safety (3 papers)Adversarial Robustness in Machine Learning (3 papers)Traffic Prediction and Management Techniques (1 paper)
Journals
mediaTUM (Technical University of Munich)mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich)
Partner nations
Germany

In The Last Decade

Frederik Diehl

4 papers receiving 108 citations

Peers

Frederik Diehl
Comparison fields: 5 of 33
  • Automotive Engineering 54
  • Computer Vision and Pattern Recognition 45
  • Artificial Intelligence 45
  • Control and Systems Engineering 29
  • Safety, Risk, Reliability and Quality 26
Replace Jonah Philion with:
Jonah Philion Canada
Hugo Grimmett United Kingdom
Hendrik Königshof Germany
Chejian Xu United States
Matthew O’Kelly United States
Kuo-Hao Zeng United States
Blake Wulfe United States
Xiaogang Jia China
Walther Wachenfeld Germany
Błażej Osiński Poland
Frederik Diehl relative to Jonah Philion Canada Jonah Philion's profile →
Citations per field
00.5×1.7×
Jonah Philion · 1×
Citations per year

Countries citing papers authored by Frederik Diehl

Since Specialization
Citations

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

Fields of papers citing papers by Frederik Diehl

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Frederik Diehl. 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 Frederik Diehl. The network helps show where Frederik Diehl may publish in the future.

Co-authorship network of co-authors of Frederik Diehl

This figure shows the co-authorship network connecting the top 25 collaborators of Frederik Diehl. A scholar is included among the top collaborators of Frederik Diehl based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Frederik Diehl. Frederik Diehl is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

4 of 4 papers shown
#WorkIndexed citations
1 3
2 44
3 17
4 52

About Frederik Diehl

Frederik Diehl is a scholar working on Automotive Engineering, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 4 papers that have together received 116 indexed citations. Recurring topics across this work include Autonomous Vehicle Technology and Safety (3 papers), Adversarial Robustness in Machine Learning (3 papers) and Traffic Prediction and Management Techniques (1 paper). The work is most often cited by research in Automotive Engineering (54 citations), Safety, Risk, Reliability and Quality (26 citations) and Computer Vision and Pattern Recognition (45 citations). Frederik Diehl has collaborated with scholars based in Germany. Frequent co-authors include Alois Knoll, David Lenz, Thomas Brunner, Harald Rueß, Markus Rickert and Chih‐Hong Cheng. Their work appears in journals such as mediaTUM (Technical University of Munich) and mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich).

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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