World's Best Scientists 2026 revealed!

D-Index & Metrics

Engineering and Technology

D-Index
38
Citations
5271
World Ranking
8108
National Ranking
2239

Ching-Yao Chan 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 Ching-Yao Chan 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: 245 publications — 63rd percentile

63% of scientists in this discipline score the same or lower.

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

Ching-Yao Chan 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 Ching-Yao Chan 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: 38 D-Index — 20th percentile

20% of scientists in this discipline score the same or lower.

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

Overview

Ching-Yao Chan is affiliated with the University of California, Berkeley in the United States. Their research spans multiple disciplines with a primary focus in engineering and computer science. Within these fields, they have specialized particularly in automotive engineering, control and systems engineering, and computer vision and pattern recognition, with additional interest in social psychology and artificial intelligence.

Their work covers a range of topics related to autonomous vehicle technology and safety, traffic control and management, and video surveillance and tracking methods. They have also contributed to research in traffic and road safety, human-automation interaction, anomaly detection techniques, and safety warnings and signage.

Several frequent collaborators have worked with Chan, including Pin Wang (9 joint publications), Yanli Ma (5 publications), Biao Yang (4 publications), Xiao Zhou (4 publications), and Yi He (3 publications).

Chan's research output has been published in various venues, with the most frequent outlets including:

  • arXiv (Cornell University)
  • IEEE Transactions on Intelligent Transportation Systems
  • Sensors
  • Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering
  • IEEE Transactions on Intelligent Vehicles

Some of the notable recent papers authored or coauthored by Chan are:

  • "Comfortable and energy-efficient speed control of autonomous vehicles on rough pavements using deep reinforcement learning," 2021, Transportation Research Part C Emerging Technologies
  • "A Novel Direct Trajectory Planning Approach Based on Generative Adversarial Networks and Rapidly-Exploring Random Tree," 2022, IEEE Transactions on Intelligent Transportation Systems
  • "Visualization Analysis of Intelligent Vehicles Research Field Based on Mapping Knowledge Domain," 2020, IEEE Transactions on Intelligent Transportation Systems
  • "Crossing or Not? Context-Based Recognition of Pedestrian Crossing Intention in the Urban Environment," 2021, IEEE Transactions on Intelligent Transportation Systems
  • "Deep reinforcement learning based path tracking controller for autonomous vehicle," 2020, Proceedings of the Institution of Mechanical Engineers Part D Journal of Automobile Engineering

Best Publications

  • Advancements, prospects, and impacts of automated driving systems

    Unknown

  • A Reinforcement Learning Based Approach for Automated Lane Change Maneuvers

    Pin Wang;Ching-Yao Chan;Arnaud de La Fortelle

  • A study of pedestrian group behaviors in crowd evacuation based on an extended floor field cellular automaton model

    Lili Lu;Ching-Yao Chan;Jian Wang;Wei Wang

  • Intention-aware Long Horizon Trajectory Prediction of Surrounding Vehicles using Dual LSTM Networks

    Long Xin;Pin Wang;Ching-Yao Chan;Jianyu Chen

  • A hybrid fusion algorithm for GPS/INS integration during GPS outages

    Unknown

  • Comfortable and energy-efficient speed control of autonomous vehicles on rough pavements using deep reinforcement learning

    Unknown

  • Enhancing Localization Accuracy of MEMS-INS/GPS/In-Vehicle Sensors Integration During GPS Outages

    Qimin Xu;Xu Li;Ching-Yao Chan

  • Formulation of deep reinforcement learning architecture toward autonomous driving for on-ramp merge

    Unknown

  • Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learning

    Fei Ye;Xuxin Cheng;Pin Wang;Ching-Yao Chan

  • Multi-sensor fusion methodology for enhanced land vehicle positioning

    Unknown

  • A Novel Direct Trajectory Planning Approach Based on Generative Adversarial Networks and Rapidly-Exploring Random Tree

    Unknown

  • A non-conservatively defensive strategy for urban autonomous driving

    Wei Zhan;Changliu Liu;Ching-Yao Chan;Masayoshi Tomizuka

  • A cellular automaton simulation model for pedestrian and vehicle interaction behaviors at unsignalized mid-block crosswalks.

    Unknown

  • A Reliable Fusion Methodology for Simultaneous Estimation of Vehicle Sideslip and Yaw Angles

    Unknown

  • A Survey of Deep Reinforcement Learning Algorithms for Motion Planning and Control of Autonomous Vehicles

    Fei Ye;Shen Zhang;Pin Wang;Ching-Yao Chan

  • A Reinforcement Learning Approach for Intelligent Traffic Signal Control at Urban Intersections

    Mengyu Guo;Pin Wang;Ching-Yao Chan;Sid Askary

  • Visualization Analysis of Intelligent Vehicles Research Field Based on Mapping Knowledge Domain

    Yi He;Shuo Yang;Ching-Yao Chan;Long Chen

  • Crossing or Not? Context-Based Recognition of Pedestrian Crossing Intention in the Urban Environment

    Biao Yang;Weiqin Zhan;Pin Wang;Chingyao Chan

  • Driving Decision and Control for Automated Lane Change Behavior based on Deep Reinforcement Learning

    Tianyu Shi;Pin Wang;Xuxin Cheng;Ching-Yao Chan

  • Estimating level of service of mid-block bicycle lanes considering mixed traffic flow

    Lu Bai;Pan Liu;Ching-Yao Chan;Zhibin Li

  • Continuous Control for Automated Lane Change Behavior Based on Deep Deterministic Policy Gradient Algorithm

    Pin Wang;Hanhan Li;Ching-Yao Chan

  • Spatially-partitioned environmental representation and planning architecture for on-road autonomous driving

    Wei Zhan;Jianyu Chen;Ching-Yao Chan;Changliu Liu

  • Acceptance of Full Driving Automation: Personally Owned and Shared-Use Concepts:

    Sanaz Motamedi;Pei Wang;Tingting Zhang;Ching-Yao Chan

  • Deep reinforcement learning based path tracking controller for autonomous vehicle

    I-Ming Chen;Ching-Yao Chan

  • A Cooperative Lane Change Model for Connected and Automated Vehicles

    Tingting Li;Jianping Wu;Ching-Yao Chan;Mingyu Liu

  • A Cost-Effective Vehicle Localization Solution Using an Interacting Multiple Model−Unscented Kalman Filters (IMM-UKF) Algorithm and Grey Neural Network

    Qimin Xu;Xu Li;Ching-Yao Chan

  • A Novel Graph based Trajectory Predictor with Pseudo Oracle

    Biao Yang;Guocheng Yan;Pin Wang;Ching-Yao Chan

  • Probabilistic Prediction from Planning Perspective: Problem Formulation, Representation Simplification and Evaluation Metric

    Wei Zhan;Arnaud La de Fortelle;Yi-Ting Chen;Ching-Yao Chan

  • Decision Making for Autonomous Driving via Augmented Adversarial Inverse Reinforcement Learning

    Pin Wang;Dapeng Liu;Jiayu Chen;Hanhan Li

Frequent Co-Authors

Masayoshi Tomizuka
Masayoshi Tomizuka University of California, Berkeley
Kanok Boriboonsomsin
Kanok Boriboonsomsin University of California, Riverside
Steven E Shladover
Steven E Shladover University of California, Berkeley
Mang Ye
Mang Ye Wuhan University
Shengbo Eben Li
Shengbo Eben Li Tsinghua University
David Whitney
David Whitney University of California, Berkeley
Stella X. Yu
Stella X. Yu University of Michigan–Ann Arbor
Yingfeng Cai
Yingfeng Cai Tongji University
Aaron Steinfeld
Aaron Steinfeld Carnegie Mellon University

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