Tim Salzmann

448 citations
9 papers · 207 · 1 hit paper · h-index 5

Impact in

Papers in

Tim Salzmann

8 papers receiving 201 citations

Tim Salzmann's Hit Papers

Real-Time Neural MPC: Deep Learning Model Predictive Control for Quadrotors and Agile Robotic Platforms 2023 · 100 citations
1000+1+2Years since publication255075100

Peers

Tim Salzmann
Comparison fields: 5 of 38
  • Automotive Engineering 73
  • Computer Vision and Pattern Recognition 91
  • Control and Systems Engineering 72
  • Safety, Risk, Reliability and Quality 18
  • Artificial Intelligence 57
Replace Taylor Mordan with:
Taylor Mordan Switzerland
Jonah Philion Canada
Yadong Li China
Yingjuan Tang China
Ali H. Alenezi Saudi Arabia
Oyunchimeg Shagdar France
André B. Reis Portugal
Buu Phan Canada
Qichang Hu Australia
Georg Volk Germany
Tim Salzmann relative to Taylor Mordan Switzerland Taylor Mordan's profile →
Citations per field
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Taylor Mordan · 1×
Citations per year

Countries citing papers authored by Tim Salzmann

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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 19 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.

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

All Works

9 of 9 papers shown
#Work
1
Real-Time Neural MPC: Deep Learning Model Predictive Control for Quadrotors and Agile Robotic Platforms
Hit paper breakdown →
2023100
2
Trajectron++: Multi-Agent Generative Trajectory Forecasting With Heterogeneous Data for Control
202053
3 202227
4 202315
5 20217
6 20193
7 20241
8 20241
9 20180

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 9 papers that have together received 207 indexed citations. Recurring topics across this work include Human Pose and Action Recognition (4 papers), Video Surveillance and Tracking Methods (4 papers), Autonomous Vehicle Technology and Safety (3 papers), Time Series Analysis and Forecasting (2 papers), Advanced Control Systems Optimization (1 paper), Advanced Vision and Imaging (1 paper), Context-Aware Activity Recognition Systems (1 paper) and Real-time simulation and control systems (1 paper). The work is most often cited by research in Automotive Engineering (73 citations), Computer Vision and Pattern Recognition (91 citations), Control and Systems Engineering (72 citations), Safety, Risk, Reliability and Quality (18 citations) and Artificial Intelligence (57 citations). Tim Salzmann has collaborated with scholars based in Germany, United States and United Kingdom. Frequent co-authors include Marco Pavone, Markus Ryll, Elia Kaufmann, Davide Scaramuzza, Punarjay Chakravarty, Boris Ivanovic, Alex Bewley, Dorsa Sadigh, Carolina Parada and Hao-Tien Lewis Chiang. Their work appears in journals such as IEEE Robotics and Automation Letters, The Planetary Science Journal, Lecture notes in computer science, mediaTUM (Technical University of Munich) and 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

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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