Frederik Diehl

512 total citations
4 papers, 116 citations indexed

About

Frederik Diehl is a scholar working on Automotive Engineering, Artificial Intelligence and Computer Vision and Pattern Recognition. According to data from OpenAlex, Frederik Diehl has authored 4 papers receiving a total of 116 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Automotive Engineering, 3 papers in Artificial Intelligence and 2 papers in Computer Vision and Pattern Recognition. Recurrent topics in Frederik Diehl's work include Autonomous Vehicle Technology and Safety (3 papers), Adversarial Robustness in Machine Learning (3 papers) and Traffic Prediction and Management Techniques (1 paper). Frederik Diehl is often cited by papers focused on Autonomous Vehicle Technology and Safety (3 papers), Adversarial Robustness in Machine Learning (3 papers) and Traffic Prediction and Management Techniques (1 paper). Frederik Diehl collaborates with scholars based in Germany. Frederik Diehl's co-authors include Alois Knoll, David Lenz, Thomas Brunner, Chih‐Hong Cheng, Markus Rickert and Harald Rueß and has published in prestigious journals such as mediaTUM (Technical University of Munich) and mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich).

In The Last Decade

Frederik Diehl

4 papers receiving 108 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Frederik Diehl Germany	4	54	45	45	29	26	4	116
Jonah Philion Canada	5	57 1.1×	67 1.5×	30 0.7×	24 0.8×	17 0.7×	5	143
Hugo Grimmett United Kingdom	6	51 0.9×	69 1.5×	48 1.1×	28 1.0×	7 0.3×	9	128
Matthew O’Kelly United States	7	62 1.1×	14 0.3×	27 0.6×	26 0.9×	17 0.7×	14	128
Chejian Xu United States	5	95 1.8×	26 0.6×	61 1.4×	39 1.3×	24 0.9×	5	174
Hendrik Königshof Germany	7	77 1.4×	104 2.3×	15 0.3×	20 0.7×	18 0.7×	8	168
Blake Wulfe United States	5	74 1.4×	40 0.9×	39 0.9×	46 1.6×	18 0.7×	6	131
Éloi Zablocki France	7	59 1.1×	94 2.1×	107 2.4×	10 0.3×	6 0.2×	11	214
Xiaogang Jia China	7	29 0.5×	32 0.7×	28 0.6×	24 0.8×	9 0.3×	10	111
Walther Wachenfeld Germany	5	111 2.1×	16 0.4×	10 0.2×	42 1.4×	49 1.9×	7	130
Luyao Ye China	4	201 3.7×	101 2.2×	74 1.6×	29 1.0×	55 2.1×	8	256

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

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4 of 4 papers shown

1.

Brunner, Thomas, et al.. (2019). Leveraging Semantic Embeddings for Safety-Critical Applications. mediaTUM (Technical University of Munich). 15. 1389–1394. 3 indexed citations

2.

Diehl, Frederik, et al.. (2018). Uncertainty Estimation for Deep Neural Object Detectors in Safety-Critical Applications. mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich). 44 indexed citations

3.

Cheng, Chih‐Hong, et al.. (2018). Neural networks for safety-critical applications — Challenges, experiments and perspectives. 1005–1006. 17 indexed citations

4.

Lenz, David, et al.. (2017). Deep neural networks for Markovian interactive scene prediction in highway scenarios. 685–692. 52 indexed citations

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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