Tim Salzmann
Impact in
- Automotive Engineering top 10%
- Autonomous Vehicle Technology and Safety
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- Human Pose and Action Recognition
- Video Surveillance and Tracking Methods
- Robotic Path Planning Algorithms
Papers in
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- Video Surveillance and Tracking Methods 4
- Human Pose and Action Recognition 3
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- Autonomous Vehicle Technology and Safety 3
- Co-authors
- Marco Pavone (3 shared papers)Markus Ryll (3 shared papers)Elia Kaufmann (1 shared paper)Davide Scaramuzza (1 shared paper)Boris Ivanovic (1 shared paper)Punarjay Chakravarty (1 shared paper)Alex Bewley (1 shared paper)Carolina Parada (1 shared paper)
- Journals
- IEEE Robotics and Automation Letters (2 papers)The Planetary Science Journal (1 paper)2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (1 paper)mediaTUM (Technical University of Munich) (2 papers)
- Partner nations
- GermanyUnited StatesSwitzerland
In The Last Decade
Tim Salzmann
7 papers receiving 177 citations
Tim Salzmann's Hit Papers
Peers
Comparison fields: 5 of 37
- Automotive Engineering 69
- Computer Vision and Pattern Recognition 78
- Control and Systems Engineering 62
- Safety, Risk, Reliability and Quality 17
- Artificial Intelligence 51
Countries citing papers authored by Tim Salzmann
This map shows the geographic impact of Tim Salzmann'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 Tim Salzmann with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tim Salzmann more than expected).
Fields of papers citing papers by Tim Salzmann
This network shows the impact of papers produced by Tim Salzmann. 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 Tim Salzmann. The network helps show where Tim Salzmann may publish in the future.
Co-authors
The 18 scholars most cited alongside Tim Salzmann, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | Real-Time Neural MPC: Deep Learning Model Predictive Control for Quadrotors and Agile Robotic Platforms Hit paper breakdown → | 2023 | 86 |
| 2 | Trajectron++: Multi-Agent Generative Trajectory Forecasting With Heterogeneous Data for Control | 2020 | 53 |
| 3 | 2022 | 21 | |
| 4 | 2023 | 11 | |
| 5 | 2021 | 7 | |
| 6 | 2019 | 2 | |
| 7 | 2024 | 1 | |
| 8 | 2018 | 0 |
About Tim Salzmann
Tim Salzmann is a scholar working on Computer Vision and Pattern Recognition, Automotive Engineering, Signal Processing, Astronomy and Astrophysics and Control and Systems Engineering, having authored 8 papers that have together received 181 indexed citations. Recurring topics across this work include Video Surveillance and Tracking Methods (4 papers), Human Pose and Action Recognition (3 papers), Autonomous Vehicle Technology and Safety (3 papers), Time Series Analysis and Forecasting (2 papers), 3D Shape Modeling and Analysis (1 paper), Advanced Control Systems Optimization (1 paper), Geology and Paleoclimatology Research (1 paper) and Fault Detection and Control Systems (1 paper). The work is most often cited by research in Automotive Engineering (69 citations), Computer Vision and Pattern Recognition (78 citations), Control and Systems Engineering (62 citations), Safety, Risk, Reliability and Quality (17 citations) and Artificial Intelligence (51 citations). Tim Salzmann has collaborated with scholars based in Germany, United States and Switzerland. Frequent co-authors include Marco Pavone, Markus Ryll, Elia Kaufmann, Davide Scaramuzza, Boris Ivanovic, Punarjay Chakravarty, Alex Bewley, Carolina Parada, Hao-Tien Lewis Chiang and Dorsa Sadigh. Their work appears in journals such as IEEE Robotics and Automation Letters, The Planetary Science Journal, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) and mediaTUM (Technical University of 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.