Kaige Tan

508 citations
21 papers · 303 · h-index 8

Impact in

Papers in

Kaige Tan

17 papers receiving 294 citations

Peers

Kaige Tan
Comparison fields: 5 of 53
  • Automotive Engineering 109
  • Software 26
  • Safety, Risk, Reliability and Quality 35
  • Control and Systems Engineering 84
  • Computer Networks and Communications 53
Replace Lin Shen Liew with:
Lin Shen Liew Singapore
Lúcio F. Vismari Brazil
Fengjun Zhou Singapore
Cheng Chang China
Karsten Lemmer Germany
Vipin Kumar Kukkala United States
Carl Bergenhem Sweden
Maria Chiara Laghi Italy
Sumbal Malik United Arab Emirates
Bassam Alrifaee Germany
Kaige Tan relative to Lin Shen Liew Singapore Lin Shen Liew's profile →
Citations per field
00.5×2.8×
Lin Shen Liew · 1×
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 2022102
2 202236
3 202235
4 202233
5 201921
6 202219
7 202317
8 20238
9 20227
10 20216
11 20235
12 20235
13 20243
14 20222
15 20231
16 20161
17
Finding critical scenarios for automated driving systems : The data extraction form
20211
18
Building verification database and extracting critical scenarios for self-driving car testing on virtual platform
20191
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 303 indexed citations. Recurring topics across this work include Soft Robotics and Applications (4 papers), Petri Nets in System Modeling (3 papers), Electric Vehicles and Infrastructure (3 papers), Autonomous Vehicle Technology and Safety (3 papers), Electric and Hybrid Vehicle Technologies (3 papers), Robotic Locomotion and Control (3 papers), Real-time simulation and control systems (3 papers) and Robot Manipulation and Learning (3 papers). The work is most often cited by research in Automotive Engineering (109 citations), Software (26 citations), Safety, Risk, Reliability and Quality (35 citations), Control and Systems Engineering (84 citations) and Computer Networks and Communications (53 citations). Kaige Tan has collaborated with scholars based in Sweden, China and Switzerland. Frequent co-authors include Lei Feng, Martin Törngren, Muhammad Rusyadi Ramli, Jianbo Tao, Xin Tao, Mihai Nica, György Dán, Franz Wotawa, Qinglei Ji and Per Runeson. Their work appears in journals such as Software Quality Journal, Robotics and Computer-Integrated Manufacturing, IEEE Transactions on Intelligent Vehicles, Applied Sciences and IEEE Robotics and Automation Letters.

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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