Daniel W. Carruth

2.5k total citations
93 papers, 1.6k citations indexed

About

Daniel W. Carruth is a scholar working on Social Psychology, Computer Vision and Pattern Recognition and Automotive Engineering. According to data from OpenAlex, Daniel W. Carruth has authored 93 papers receiving a total of 1.6k indexed citations (citations by other indexed papers that have themselves been cited), including 32 papers in Social Psychology, 25 papers in Computer Vision and Pattern Recognition and 20 papers in Automotive Engineering. Recurrent topics in Daniel W. Carruth's work include Human-Automation Interaction and Safety (17 papers), Autonomous Vehicle Technology and Safety (17 papers) and Traffic and Road Safety (13 papers). Daniel W. Carruth is often cited by papers focused on Human-Automation Interaction and Safety (17 papers), Autonomous Vehicle Technology and Safety (17 papers) and Traffic and Road Safety (13 papers). Daniel W. Carruth collaborates with scholars based in United States, Slovakia and Canada. Daniel W. Carruth's co-authors include Shuchisnigdha Deb, Lesley Strawderman, Teena M. Garrison, Christopher R. Hudson, Matthew Doude, C. Goodin, Brian Smith, Janice L. DuBien, Harish Chander and Adam C. Knight and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Access and Sensors.

In The Last Decade

Daniel W. Carruth

86 papers receiving 1.5k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Daniel W. Carruth United States 18 602 429 419 331 212 93 1.6k
Andrew Liu United States 14 415 0.7× 240 0.6× 566 1.4× 664 2.0× 286 1.3× 43 2.0k
Berthold Färber Germany 11 492 0.8× 336 0.8× 616 1.5× 221 0.7× 148 0.7× 50 1.4k
Paul Jennings United Kingdom 26 543 0.9× 568 1.3× 1.5k 3.5× 162 0.5× 104 0.5× 101 2.4k
Errol R. Hoffmann Australia 28 1.0k 1.7× 488 1.1× 269 0.6× 132 0.4× 578 2.7× 136 2.4k
Omer Tsimhoni United States 24 854 1.4× 497 1.2× 325 0.8× 115 0.3× 163 0.8× 85 1.9k
Jwu‐Sheng Hu Taiwan 28 596 1.0× 272 0.6× 143 0.3× 509 1.5× 233 1.1× 217 2.9k
Keiichi Uchimura Japan 11 238 0.4× 97 0.2× 235 0.6× 325 1.0× 109 0.5× 112 1.0k
Zhencheng Hu Japan 11 253 0.4× 109 0.3× 290 0.7× 314 0.9× 125 0.6× 60 961
Mohammed Hossny Australia 24 310 0.5× 168 0.4× 309 0.7× 953 2.9× 309 1.5× 95 2.3k
Stewart Birrell United Kingdom 29 1.2k 2.0× 620 1.4× 735 1.8× 66 0.2× 172 0.8× 95 2.7k

Countries citing papers authored by Daniel W. Carruth

Since Specialization
Citations

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

Fields of papers citing papers by Daniel W. Carruth

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel W. Carruth. 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 Daniel W. Carruth. The network helps show where Daniel W. Carruth may publish in the future.

Co-authorship network of co-authors of Daniel W. Carruth

This figure shows the co-authorship network connecting the top 25 collaborators of Daniel W. Carruth. A scholar is included among the top collaborators of Daniel W. Carruth 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 Daniel W. Carruth. Daniel W. Carruth 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
1.
Hicks, Matthew, Tingjun Lei, Chaomin Luo, Daniel W. Carruth, & Zhuming Bi. (2025). A Bio-Inspired Goal-Directed Cognitive Map Model for Robot Navigation and Exploration. IEEE Transactions on Cognitive and Developmental Systems. 17(5). 1125–1140. 2 indexed citations
2.
Carruth, Daniel W., et al.. (2024). Comparing real and simulated performance for an off‐road autonomous ground vehicle in obstacle avoidance. Journal of Field Robotics. 41(3). 798–810. 1 indexed citations
3.
Lei, Tingjun, et al.. (2024). Optimal Multi-target Navigation via Graph-based Algorithms in Complex Environments. 1–6. 2 indexed citations
4.
Goodin, C., et al.. (2024). An empirical vehicle speed model for tuning throttle controller parameters. International Journal of Vehicle Performance. 10(2). 196–214. 1 indexed citations
5.
Lei, Tingjun, et al.. (2023). Sensor-based multi-waypoint autonomous robot navigation with graph-based models. 6–6. 5 indexed citations
6.
Goodin, C., et al.. (2023). Fidelity requirements for simulating sensor performance in autonomous ground vehicles. 7–7. 1 indexed citations
7.
Goodin, C., et al.. (2023). Object detection in synthetic aerial imagery using deep learning. 1–1. 2 indexed citations
8.
Goodin, C., et al.. (2023). A Simulation Framework for Evaluating the Cybersecurity of Autonomous Ground Vehicles. SAE technical papers on CD-ROM/SAE technical paper series. 1.
9.
Mason, George L., Daniel W. Carruth, Matthew Doude, et al.. (2022). CaT: CAVS Traversability Dataset for Off-Road Autonomous Driving. IEEE Access. 10. 24759–24768. 25 indexed citations
10.
Jones, J. Adam, et al.. (2022). Virtual Reality Induced Symptoms and Effects: Concerns, Causes, Assessment & Mitigation. MDPI (MDPI AG). 1(2). 130–146. 20 indexed citations
11.
Patel, V. L., et al.. (2022). DEVELOPING A MODEL OF DRIVER PERFORMANCE, SITUATION AWARENESS, AND COGNITIVE LOAD CONSIDERING DIFFERENT LEVELS OF PARTIAL VEHICLE AUTONOMY. SAE technical papers on CD-ROM/SAE technical paper series. 1.
12.
Hudson, Christopher R., et al.. (2021). Traversability mapping in off-road environment using semantic segmentation. 11–11. 9 indexed citations
13.
Tang, Bo, et al.. (2020). Recursive Multi-Scale Image Deraining With Sub-Pixel Convolution Based Feature Fusion and Context Aggregation. IEEE Access. 8. 177495–177505. 4 indexed citations
14.
Ball, John E., et al.. (2019). Semantic Segmentation with Transfer Learning for Off-Road Autonomous Driving. Sensors. 19(11). 2577–2577. 47 indexed citations
15.
Li, Xingyu, Bo Tang, John E. Ball, Matthew Doude, & Daniel W. Carruth. (2019). Rollover-Free Path Planning for Off-Road Autonomous Driving. Electronics. 8(6). 614–614. 13 indexed citations
16.
Deb, Shuchisnigdha, Lesley Strawderman, & Daniel W. Carruth. (2018). Investigating pedestrian suggestions for external features on fully autonomous vehicles: A virtual reality experiment. Transportation Research Part F Traffic Psychology and Behaviour. 59. 135–149. 166 indexed citations
17.
Deb, Shuchisnigdha, Lesley Strawderman, Janice L. DuBien, et al.. (2017). Evaluating pedestrian behavior at crosswalks: Validation of a pedestrian behavior questionnaire for the U.S. population. Accident Analysis & Prevention. 106. 191–201. 93 indexed citations
18.
Hudson, Christopher R., et al.. (2015). ANVEL-ROS: THE INTEGRATION OF THE ROBOT OPERATING SYSTEM WITH A HIGH-FIDELITY SIMULATOR. SAE technical papers on CD-ROM/SAE technical paper series. 1.
19.
Bethel, Cindy L., et al.. (2013). Eyewitnesses are misled by human but not robot interviewers. Human-Robot Interaction. 25–32. 8 indexed citations
20.
Carruth, Daniel W., et al.. (2009). Effects of Body Armor Design on Upper Body Range of Motion. Proceedings of the Human Factors and Ergonomics Society Annual Meeting. 53(14). 907–911. 2 indexed citations

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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