Mingyu Wang
- Automotive Engineering top 10%
- Control and Systems Engineering top 10%
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence
- Computer Networks and Communications
- Co-authors
- Mac SchwagerZijian WangJohn M. TalbotJ. Christian GerdesNegar MehrGennaro NotomistaAdrien GaidonMagnus Egerstedt
- Topics
- Traffic control and management (4 papers)Autonomous Vehicle Technology and Safety (3 papers)Robotic Path Planning Algorithms (3 papers)
- Cited by
- Automotive EngineeringComputer Vision and Pattern RecognitionControl and Systems Engineering
- Journals
- IEEE Transactions on RoboticsJournal of Fixed Point Theory and Applications2022 International Conference on Robotics and Automation (ICRA)
- Partner nations
- United StatesChina
In The Last Decade
Mingyu Wang
11 papers receiving 245 citations
Peers
Comparison fields: 5 of 40
- Automotive Engineering 118
- Control and Systems Engineering 98
- Computer Vision and Pattern Recognition 96
- Artificial Intelligence 75
- Computer Networks and Communications 42
Countries citing papers authored by Mingyu Wang
This map shows the geographic impact of Mingyu Wang'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 Mingyu Wang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mingyu Wang more than expected).
Fields of papers citing papers by Mingyu Wang
This network shows the impact of papers produced by Mingyu Wang. 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 Mingyu Wang. The network helps show where Mingyu Wang may publish in the future.
Co-authorship network of co-authors of Mingyu Wang
This figure shows the co-authorship network connecting the top 25 collaborators of Mingyu Wang. A scholar is included among the top collaborators of Mingyu Wang 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 Mingyu Wang. Mingyu Wang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 25 | |
| 2 | 4 | |
| 3 | 86 | |
| 4 | 25 | |
| 5 | 22 | |
| 6 | 5 | |
| 7 | 27 | |
| 8 | 30 | |
| 9 | 26 | |
| 10 | 2 | |
| 11 | 1 |
About Mingyu Wang
Mingyu Wang is a scholar working on Automotive Engineering, Management Science and Operations Research and Control and Systems Engineering, having authored 11 papers that have together received 253 indexed citations. Recurring topics across this work include Traffic control and management (4 papers), Autonomous Vehicle Technology and Safety (3 papers) and Robotic Path Planning Algorithms (3 papers). The work is most often cited by research in Automotive Engineering (118 citations), Computer Vision and Pattern Recognition (96 citations) and Control and Systems Engineering (98 citations). Mingyu Wang has collaborated with scholars based in United States and China. Frequent co-authors include Mac Schwager, Zijian Wang, John M. Talbot, J. Christian Gerdes, Negar Mehr, Gennaro Notomista, Adrien Gaidon, Magnus Egerstedt, Jiuqiang Liu and Haoming Wang. Their work appears in journals such as IEEE Transactions on Robotics, Journal of Fixed Point Theory and Applications and 2022 International Conference on Robotics and Automation (ICRA).
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.