G. Ogasawara
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Automotive Engineering top 10%
- Safety, Risk, Reliability and Quality top 10%
- Aerospace Engineering
- Co-authors
- Stuart RussellJitendra MalikB. V. RaoDaniela KollerJoseph WeberT. HuangT.S. HuangSastri Kota
- Topics
- Bayesian Modeling and Causal Inference (3 papers)Autonomous Vehicle Technology and Safety (2 papers)Anomaly Detection Techniques and Applications (2 papers)
- Cited by
- Computer Vision and Pattern RecognitionAutomotive EngineeringSafety, Risk, Reliability and Quality
- Journals
- International Joint Conference on Artificial IntelligenceNational Conference on Artificial IntelligenceACM SIGART Bulletin
- Partner nations
- United States
In The Last Decade
G. Ogasawara
7 papers receiving 342 citations
Peers
Comparison fields: 5 of 54
- Computer Vision and Pattern Recognition 306
- Artificial Intelligence 106
- Automotive Engineering 63
- Safety, Risk, Reliability and Quality 34
- Aerospace Engineering 32
Countries citing papers authored by G. Ogasawara
This map shows the geographic impact of G. Ogasawara'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 G. Ogasawara with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites G. Ogasawara more than expected).
Fields of papers citing papers by G. Ogasawara
This network shows the impact of papers produced by G. Ogasawara. 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 G. Ogasawara. The network helps show where G. Ogasawara may publish in the future.
Co-authorship network of co-authors of G. Ogasawara
This figure shows the co-authorship network connecting the top 25 collaborators of G. Ogasawara. A scholar is included among the top collaborators of G. Ogasawara 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 G. Ogasawara. G. Ogasawara is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 266 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | Automatic symbolic traffic scene analysis using belief networks | 85 |
| 5 | Planning Using Multiple Execution Architectures | 7 |
| 6 | Symbolic Traffic Scene Analysis Using Dynamic Belief Networks | 10 |
| 7 | 9 |
About G. Ogasawara
G. Ogasawara is a scholar working on Automotive Engineering, Computer Networks and Communications and Artificial Intelligence, having authored 7 papers that have together received 379 indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (3 papers), Autonomous Vehicle Technology and Safety (2 papers) and Anomaly Detection Techniques and Applications (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (306 citations), Automotive Engineering (63 citations) and Safety, Risk, Reliability and Quality (34 citations). G. Ogasawara has collaborated with scholars based in United States. Frequent co-authors include Stuart Russell, Jitendra Malik, B. V. Rao, Daniela Koller, Joseph Weber, T. Huang, T.S. Huang and Sastri Kota. Their work appears in journals such as International Joint Conference on Artificial Intelligence, National Conference on Artificial Intelligence and ACM SIGART Bulletin.
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.