Katherine Driggs-Campbell

1.5k citations
53 papers · 829 indexed · h-index 15
Topics
Autonomous Vehicle Technology and Safety (25 papers)Anomaly Detection Techniques and Applications (11 papers)Reinforcement Learning in Robotics (10 papers)

In The Last Decade

Katherine Driggs-Campbell

44 papers receiving 809 citations

Peers

Katherine Driggs-Campbell
Comparison fields: 5 of 71
  • Automotive Engineering 441
  • Control and Systems Engineering 267
  • Artificial Intelligence 259
  • Computer Vision and Pattern Recognition 237
  • Social Psychology 142
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Citations per year

Countries citing papers authored by Katherine Driggs-Campbell

Since Specialization
Citations

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

Fields of papers citing papers by Katherine Driggs-Campbell

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Katherine Driggs-Campbell. 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 Katherine Driggs-Campbell. The network helps show where Katherine Driggs-Campbell may publish in the future.

Co-authorship network of co-authors of Katherine Driggs-Campbell

This figure shows the co-authorship network connecting the top 25 collaborators of Katherine Driggs-Campbell. A scholar is included among the top collaborators of Katherine Driggs-Campbell 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 Katherine Driggs-Campbell. Katherine Driggs-Campbell is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
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7 13
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11 46
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14 11
15
Modeling and Prediction of Human Driver Behavior: A Survey.
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16 4
17 18
18 24
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Data-Driven Probabilistic Modeling and Verification of Human Driver Behavior
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About Katherine Driggs-Campbell

Katherine Driggs-Campbell is a scholar working on Automotive Engineering, Computer Vision and Pattern Recognition and Safety, Risk, Reliability and Quality, having authored 53 papers that have together received 829 indexed citations. Recurring topics across this work include Autonomous Vehicle Technology and Safety (25 papers), Anomaly Detection Techniques and Applications (11 papers) and Reinforcement Learning in Robotics (10 papers). The work is most often cited by research in Automotive Engineering (441 citations), Safety, Risk, Reliability and Quality (119 citations) and Computer Vision and Pattern Recognition (237 citations). Katherine Driggs-Campbell has collaborated with scholars based in United States, China and Bangladesh. Frequent co-authors include Mykel J. Kochenderfer, Růžena Bajcsy, Leo Laine, Krister Wolff, Shuijing Liu, Vijay Govindarajan, S. Shankar Sastry, Zhe Huang, Victor Shia and Girish Chowdhary. Their work appears in journals such as IEEE Transactions on Automatic Control, IEEE Access and The International Journal of Robotics Research.

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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