Maximilian Naumann
- Automotive Engineering top 5%
- Computer Vision and Pattern Recognition top 10%
- Control and Systems Engineering top 10%
- Safety, Risk, Reliability and Quality top 10%
- Aerospace Engineering
- Co-authors
- Fabian PoggenhansMatthias MayrStefan OrfFlorian KuhntChristoph StillerMartin LauerHendrik KönigshofLiting Sun
- Topics
- Autonomous Vehicle Technology and Safety (8 papers)Robotic Path Planning Algorithms (5 papers)Formal Methods in Verification (3 papers)
- Cited by
- Automotive EngineeringComputer Vision and Pattern RecognitionSafety, Risk, Reliability and Quality
- Journals
- Repository KITopen (Karlsruhe Institute of Technology)2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
- Partner nations
- GermanyUnited States
In The Last Decade
Maximilian Naumann
9 papers receiving 304 citations
Peers
Comparison fields: 5 of 39
- Automotive Engineering 227
- Computer Vision and Pattern Recognition 126
- Control and Systems Engineering 95
- Safety, Risk, Reliability and Quality 55
- Aerospace Engineering 48
Countries citing papers authored by Maximilian Naumann
This map shows the geographic impact of Maximilian Naumann'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 Maximilian Naumann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Maximilian Naumann more than expected).
Fields of papers citing papers by Maximilian Naumann
This network shows the impact of papers produced by Maximilian Naumann. 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 Maximilian Naumann. The network helps show where Maximilian Naumann may publish in the future.
Co-authorship network of co-authors of Maximilian Naumann
This figure shows the co-authorship network connecting the top 25 collaborators of Maximilian Naumann. A scholar is included among the top collaborators of Maximilian Naumann 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 Maximilian Naumann. Maximilian Naumann is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 12 | |
| 3 | 40 | |
| 4 | 3 | |
| 5 | 9 | |
| 6 | 36 | |
| 7 | 23 | |
| 8 | 191 | |
| 9 | 2 |
About Maximilian Naumann
Maximilian Naumann is a scholar working on Automotive Engineering, Computer Vision and Pattern Recognition and Computational Theory and Mathematics, having authored 9 papers that have together received 317 indexed citations. Recurring topics across this work include Autonomous Vehicle Technology and Safety (8 papers), Robotic Path Planning Algorithms (5 papers) and Formal Methods in Verification (3 papers). The work is most often cited by research in Automotive Engineering (227 citations), Computer Vision and Pattern Recognition (126 citations) and Safety, Risk, Reliability and Quality (55 citations). Maximilian Naumann has collaborated with scholars based in Germany and United States. Frequent co-authors include Fabian Poggenhans, Matthias Mayr, Stefan Orf, Florian Kuhnt, Christoph Stiller, Martin Lauer, Hendrik Königshof, Liting Sun, Wei Zhan and Masayoshi Tomizuka. Their work appears in journals such as Repository KITopen (Karlsruhe Institute of Technology) and 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
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.