Hengbo Ma
- Automotive Engineering top 2%
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Safety, Risk, Reliability and Quality top 5%
- Building and Construction top 10%
- Topics
- Autonomous Vehicle Technology and Safety (11 papers)Video Surveillance and Tracking Methods (6 papers)Anomaly Detection Techniques and Applications (6 papers)
- Cited by
- Automotive EngineeringSafety, Risk, Reliability and QualityComputer Vision and Pattern Recognition
- Journals
- IEEE Transactions on Intelligent Transportation SystemsIEEE Robotics and Automation Letters2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Partner nations
- United StatesChinaHong Kong
In The Last Decade
Hengbo Ma
15 papers receiving 451 citations
Peers
Comparison fields: 5 of 40
- Automotive Engineering 332
- Computer Vision and Pattern Recognition 235
- Artificial Intelligence 159
- Safety, Risk, Reliability and Quality 128
- Building and Construction 91
Countries citing papers authored by Hengbo Ma
This map shows the geographic impact of Hengbo Ma'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 Hengbo Ma with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Hengbo Ma more than expected).
Fields of papers citing papers by Hengbo Ma
This network shows the impact of papers produced by Hengbo Ma. 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 Hengbo Ma. The network helps show where Hengbo Ma may publish in the future.
Co-authorship network of co-authors of Hengbo Ma
This figure shows the co-authorship network connecting the top 25 collaborators of Hengbo Ma. A scholar is included among the top collaborators of Hengbo Ma 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 Hengbo Ma. Hengbo Ma is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 5 | |
| 2 | 7 | |
| 3 | 13 | |
| 4 | 22 | |
| 5 | 14 | |
| 6 | 74 | |
| 7 | 23 | |
| 8 | 19 | |
| 9 | 30 | |
| 10 | 40 | |
| 11 | 7 | |
| 12 | 115 | |
| 13 | 35 | |
| 14 | 28 | |
| 15 | 28 |
About Hengbo Ma
Hengbo Ma is a scholar working on Automotive Engineering, Computer Vision and Pattern Recognition and Artificial Intelligence, having authored 15 papers that have together received 460 indexed citations. Recurring topics across this work include Autonomous Vehicle Technology and Safety (11 papers), Video Surveillance and Tracking Methods (6 papers) and Anomaly Detection Techniques and Applications (6 papers). The work is most often cited by research in Automotive Engineering (332 citations), Safety, Risk, Reliability and Quality (128 citations) and Computer Vision and Pattern Recognition (235 citations). Hengbo Ma has collaborated with scholars based in United States, China and Hong Kong. Frequent co-authors include Masayoshi Tomizuka, Jiachen Li, Chiho Choi, Zhihao Zhang, Wei Zhan, Jiachen Li, Defu Cao, Yaofeng Sun, Fan Yang and Srikanth Malla. Their work appears in journals such as IEEE Transactions on Intelligent Transportation Systems, IEEE Robotics and Automation Letters 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.