World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
41
Citations
7264
World Ranking
6927
National Ranking
1274

Xingda Qu publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Xingda Qu sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 140 publications — 23rd percentile

23% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Xingda Qu D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Xingda Qu sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 41 D-Index — 31st percentile

31% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Xingda Qu is affiliated with Shenzhen University in China and has contributed extensively to research in engineering and psychology, with a particular focus on social psychology and computer vision and pattern recognition. Their work also spans cognitive neuroscience, automotive engineering, and biomedical engineering.

Their recent publications cover a range of topics in autonomous vehicle technology, healthcare wearables, and image processing. Notable papers include:

  • Automated vehicle acceptance in China: Social influence and initial trust are key determinants (2020), published in Transportation Research Part C Emerging Technologies
  • Understanding consumer acceptance of healthcare wearable devices: An integrated model of UTAUT and TTF (2020), published in International Journal of Medical Informatics
  • Decision making of autonomous vehicles in lane change scenarios: Deep reinforcement learning approaches with risk awareness (2021), published in Transportation Research Part C Emerging Technologies
  • An infrared and visible image fusion method based on multi-scale transformation and norm optimization (2021), published in Information Fusion
  • A deep learning based image enhancement approach for autonomous driving at night (2020), published in Knowledge-Based Systems

Xingda Qu frequently collaborates with several researchers, with the most frequent co-authors being:

  • Xinyao Hu
  • Guofa Li
  • Zhong Zhao
  • Da Tao
  • Tingru Zhang

The scientist has published extensively in certain journals, including:

  • Journal of Autism and Developmental Disorders
  • International Journal of Industrial Ergonomics
  • Applied Ergonomics
  • IEEE Sensors Journal
  • International Journal of Environmental Research and Public Health

Their work embraces multiple main topics, such as:

  • Human-Automation Interaction and Safety
  • Autism Spectrum Disorder Research
  • Balance, Gait, and Falls Prevention
  • Autonomous Vehicle Technology and Safety
  • Ergonomics and Musculoskeletal Disorders
  • Musculoskeletal pain and rehabilitation
  • Traffic and Road Safety

Best Publications

  • The roles of initial trust and perceived risk in public’s acceptance of automated vehicles

    Tingru Zhang;Da Tao;Xingda Qu;Xiaoyan Zhang

  • Automated vehicle acceptance in China: social influence and initial trust are key determinants

    Tingru Zhang;Da Tao;Xingda Qu;Xiaoyan Zhang;Xiaoyan Zhang

  • Understanding consumer acceptance of healthcare wearable devices: An integrated model of UTAUT and TTF.

    Hailiang Wang;Da Tao;Na Yu;Xingda Qu

  • Decision making of autonomous vehicles in lane change scenarios: Deep reinforcement learning approaches with risk awareness

    Guofa Li;Yifan Yang;Shen Li;Xingda Qu

  • A Systematic Review of Physiological Measures of Mental Workload.

    Da Tao;Haibo Tan;Hailiang Wang;Xu Zhang

  • An infrared and visible image fusion method based on multi-scale transformation and norm optimization

    Guofa Li;Yongjie Lin;Xingda Qu

  • A systematic review and meta-analysis of user acceptance of consumer-oriented health information technologies

    Da Tao;Tieyan Wang;Tieshan Wang;Tingru Zhang

  • Influence of traffic congestion on driver behavior in post-congestion driving.

    Guofa Li;Weijian Lai;Xiaoxuan Sui;Xiaohang Li

  • A deep learning based image enhancement approach for autonomous driving at night

    Guofa Li;Guofa Li;Yifan Yang;Xingda Qu;Dongpu Cao

  • Effects of load carriage and fatigue on gait characteristics.

    Xingda Qu;Joo Chuan Yeo

  • Risk assessment based collision avoidance decision-making for autonomous vehicles in multi-scenarios

    Guofa Li;Guofa Li;Yifan Yang;Tingru Zhang;Xingda Qu

  • The role of personality traits and driving experience in self-reported risky driving behaviors and accident risk among Chinese drivers.

    Da Tao;Rui Zhang;Xingda Qu

  • Deep Learning Approaches on Pedestrian Detection in Hazy Weather

    Guofa Li;Yifan Yang;Xingda Qu

  • Drivers' visual scanning behavior at signalized and unsignalized intersections: A naturalistic driving study in China.

    Guofa Li;Ying Wang;Fangping Zhu;Xiaoxuan Sui

  • Key characteristics in designing massive open online courses (MOOCs) for user acceptance: an application of the extended technology acceptance model

    Da Tao;Pei Fu;Yunhui Wang;Tingru Zhang

  • A Temporal-Spatial Deep Learning Approach for Driver Distraction Detection Based on EEG Signals

    Guofa Li;Weiquan Yan;Shen Li;Xingda Qu

  • Affect prediction from physiological measures via visual stimuli

    Feng Zhou;Xingda Qu;Martin G. Helander;Jianxin (Roger) Jiao

  • Deep Reinforcement Learning Enabled Decision-Making for Autonomous Driving at Intersections

    Guofa Li;Guofa Li;Shenglong Li;Shen Li;Yechen Qin

  • Effects of external loads on balance control during upright stance: Experimental results and model-based predictions

    Xingda Qu;Maury A. Nussbaum

  • Extraction of descriptive driving patterns from driving data using unsupervised algorithms

    Guofa Li;Guofa Li;Yaoyu Chen;Dongpu Cao;Xingda Qu

  • An individual-specific gait pattern prediction model based on generalized regression neural networks

    Trieu Phat Luu;K.H. Low;Xingda Qu;H.B. Lim

  • Impacts of different types of insoles on postural stability in older adults

    Xingda Qu

  • Emotion Prediction from Physiological Signals: A Comparison Study Between Visual and Auditory Elicitors

    Feng Zhou;Feng Zhou;Xingda Qu;Jianxin Jiao;Martin G. Helander

  • Integrating usability and social cognitive theories with the technology acceptance model to understand young users' acceptance of a health information portal.

    Da Tao;Fenglian Shao;Hailiang Wang;Mian Yan

Frequent Co-Authors

Maury A. Nussbaum
Maury A. Nussbaum Virginia Tech
K.H. Low
K.H. Low Nanyang Technological University
Chun-Hsien Chen
Chun-Hsien Chen Nanyang Technological University
Dongpu Cao
Dongpu Cao University of Waterloo
Feng Zhou
Feng Zhou University of Michigan–Ann Arbor
Christopher D. Wickens
Christopher D. Wickens Colorado State University
Martin G. Helander
Martin G. Helander Nanyang Technological University
Yong Hu
Yong Hu University of Hong Kong
Keqiang Li
Keqiang Li Tsinghua University
Benoît G. Bardy
Benoît G. Bardy University of Montpellier

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