Juanwu Lu
Impact in
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- Autonomous Vehicle Technology and Safety
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- Advanced Neural Network Applications
- Video Surveillance and Tracking Methods
- Multimodal Machine Learning Applications
- Robotic Path Planning Algorithms
Papers in
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- Autonomous Vehicle Technology and Safety 4
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- Advanced Neural Network Applications 3
- Human Pose and Action Recognition 1
- Generative Adversarial Networks and Image Synthesis 1
- Co-authors
- Ziran Wang (5 shared papers)Yunsheng Ma (6 shared papers)Can Cui (4 shared papers)Xu Cao (2 shared papers)Wenqian Ye (2 shared papers)Can Cui (2 shared papers)Peiran Liu (1 shared paper)Kyungtae Han (1 shared paper)
- Journals
- IEEE Transactions on Intelligent Vehicles (1 paper)2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (1 paper)
- Partner nations
- United StatesSwitzerlandChina
In The Last Decade
Juanwu Lu
6 papers receiving 61 citations
Peers
Comparison fields: 5 of 23
- Automotive Engineering 26
- Computer Vision and Pattern Recognition 27
- Artificial Intelligence 22
- Building and Construction 8
- Human-Computer Interaction 3
Countries citing papers authored by Juanwu Lu
This map shows the geographic impact of Juanwu Lu'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 Juanwu Lu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Juanwu Lu more than expected).
Fields of papers citing papers by Juanwu Lu
This network shows the impact of papers produced by Juanwu Lu. 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 Juanwu Lu. The network helps show where Juanwu Lu may publish in the future.
Co-authors
The 19 scholars most cited alongside Juanwu Lu, 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 | 2024 | 24 | |
| 2 | 2023 | 14 | |
| 3 | 2024 | 9 | |
| 4 | 2024 | 6 | |
| 5 | 2023 | 5 | |
| 6 | 2022 | 4 | |
| 7 | 2024 | 0 |
About Juanwu Lu
Juanwu Lu is a scholar working on Automotive Engineering, Computer Vision and Pattern Recognition, Artificial Intelligence, Instrumentation and Social Psychology, having authored 7 papers that have together received 62 indexed citations. Recurring topics across this work include Autonomous Vehicle Technology and Safety (4 papers), Advanced Neural Network Applications (3 papers), Topic Modeling (2 papers), Advanced Optical Sensing Technologies (2 papers), Human Pose and Action Recognition (1 paper), Natural Language Processing Techniques (1 paper), Ocular and Laser Science Research (1 paper) and Generative Adversarial Networks and Image Synthesis (1 paper). The work is most often cited by research in Automotive Engineering (26 citations), Computer Vision and Pattern Recognition (27 citations), Artificial Intelligence (22 citations), Building and Construction (8 citations) and Human-Computer Interaction (3 citations). Juanwu Lu has collaborated with scholars based in United States, Switzerland and China. Frequent co-authors include Ziran Wang, Yunsheng Ma, Can Cui, Xu Cao, Wenqian Ye, Can Cui, Peiran Liu, Kyungtae Han, Amr Abdelraouf and Aniket Bera. Their work appears in journals such as IEEE Transactions on Intelligent Vehicles 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.