Kaige Tan

486 citations
21 papers · 286 · h-index 8

Impact in

Papers in

Kaige Tan

17 papers receiving 277 citations

Peers

Kaige Tan
Comparison fields: 5 of 52
  • Automotive Engineering 105
  • Software 25
  • Safety, Risk, Reliability and Quality 34
  • Control and Systems Engineering 81
  • Computer Networks and Communications 52
Replace Lin Shen Liew with:
Lin Shen Liew Singapore
Lúcio F. Vismari Brazil
Fengjun Zhou Singapore
Cheng Chang China
Karsten Lemmer Germany
Jinpeng Han China
Maria Chiara Laghi Italy
Liping Lu China
Shalin Mehta United States
Kaige Tan relative to Lin Shen Liew Singapore Lin Shen Liew's profile →
Citations per field
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Citations per year

Countries citing papers authored by Kaige Tan

Since Specialization
Citations

This map shows the geographic impact of Kaige Tan'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 Kaige Tan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kaige Tan more than expected).

Fields of papers citing papers by Kaige Tan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kaige Tan. 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 Kaige Tan. The network helps show where Kaige Tan may publish in the future.

Co-authors

The 25 scholars most cited alongside Kaige Tan, 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 Kaige Tan Line = papers co-authored together Kaige Tan links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 21 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202294
2 202236
3 202231
4 202230
5 201920
6 202219
7 202317
8 20227
9 20237
10 20216
11 20235
12 20235
13 20243
14 20222
15 20231
16
Building verification database and extracting critical scenarios for self-driving car testing on virtual platform
20191
17
Finding critical scenarios for automated driving systems : The data extraction form
20211
18 20161
19 20230
20 20250

About Kaige Tan

Kaige Tan is a scholar working on Control and Systems Engineering, Automotive Engineering, Biomedical Engineering, Electrical and Electronic Engineering and Computational Theory and Mathematics, having authored 21 papers that have together received 286 indexed citations. Recurring topics across this work include Soft Robotics and Applications (4 papers), Electric Vehicles and Infrastructure (3 papers), Robot Manipulation and Learning (3 papers), Petri Nets in System Modeling (3 papers), Autonomous Vehicle Technology and Safety (3 papers), Robotic Locomotion and Control (3 papers), Real-time simulation and control systems (3 papers) and Prosthetics and Rehabilitation Robotics (3 papers). The work is most often cited by research in Automotive Engineering (105 citations), Software (25 citations), Safety, Risk, Reliability and Quality (34 citations), Control and Systems Engineering (81 citations) and Computer Networks and Communications (52 citations). Kaige Tan has collaborated with scholars based in Sweden, China and Saudi Arabia. Frequent co-authors include Lei Feng, Martin Törngren, Muhammad Rusyadi Ramli, György Dán, Franz Wotawa, Jianbo Tao, Mihai Nica, Qinglei Ji, Stefan Persson and Per Runeson. Their work appears in journals such as Information Sciences, IEEE Transactions on Industrial Electronics, IEEE Robotics and Automation Letters, IEEE Access and IEEE Transactions on Intelligent Vehicles.

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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