Cai Xuan | Engineering and Technology | Research Excellence Award

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Mr. Cai Xuan | Engineering and Technology | Research Excellence Award

Beihang University | China

Cai Xuan is a doctoral researcher in transportation engineering with a strong background in mechanical engineering and a research focus on autonomous driving safety, intelligent testing, and AI-driven decision making. He is currently pursuing a PhD at Beihang University after completing his master’s and bachelor’s degrees in Mechanical Engineering at Hunan University. His research experience spans adversarial reinforcement learning, large language model–based scenario generation, energy management for hybrid vehicles, and safety-critical testing frameworks for autonomous vehicles. He has served as lead or co-author on multiple peer-reviewed publications in high-impact journals and top-tier conferences, including IEEE Transactions on Intelligent Vehicles, Energy, Automotive Innovation, and IEEE Intelligent Vehicles Symposium. His scholarly output has resulted in 7 published papers, an h-index of 3, and over 16citations, reflecting growing academic influence in intelligent transportation systems. His work has demonstrated significant improvements in robustness, vulnerability discovery, and real-time performance of autonomous and electrified vehicle systems. He is the recipient of multiple academic scholarships and competitive research awards at both undergraduate and graduate levels. Overall, his research contributes practical and theoretical advances toward safer, more reliable, and intelligent mobility systems.

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Featured Publications


Koma: Knowledge-driven Multi-agent Framework for Autonomous Driving with Large Language Models
K. Jiang, X. Cai, Z. Cui, A. Li, Y. Ren, H. Yu, H. Yang, D. Fu, L. Wen, P. Cai.
IEEE Transactions on Intelligent Vehicles, 2024.


Adversarial Stress Test for Autonomous Vehicle via Series Reinforcement Learning Tasks with Reward Shaping
X. Cai, X. Bai, Z. Cui, P. Hang, H. Yu, Y. Ren.
IEEE Transactions on Intelligent Vehicles, 2024. (Citations: 15)


Text2Scenario: Text-driven Scenario Generation for Autonomous Driving Test
X. Cai, X. Bai, Z. Cui, D. Xie, D. Fu, H. Yu, Y. Ren.
Automotive Innovation, 2026, 1–26. (Citations: 14)

Biomimetic Multi-UAV Swarm Exploration with U2U Communications Under Resource Constraints
Y. Huang, H. Wang, X. Bai, X. Cai, H. Yu, Y. Ren.
IEEE Transactions on Vehicular Technology, 2025. (Citations: 5)

Zohaib Khan | Engineering and Technology | Excellence in Research Award

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Dr. Zohaib Khan | Engineering and Technology | Excellence in Research Award

Jiangsu University | China

Zohaib Khan is a PhD candidate in Control Science and Engineering at Jiangsu University, specializing in machine learning–driven perception and control for intelligent robotic systems. With over six years of research and applied experience, his work bridges deep learning, computer vision, and real-time robotic control, with a strong focus on agricultural robotics and precision farming. He has authored more than 10 high-impact SCI-indexed journal articles, achieving an h-index of 6, with 11 research documents and 121 citations. His research interests include object detection and segmentation (YOLO series, transformer-based models, RCNN), vision-guided navigation, precision spraying, and robust control of autonomous robots in unstructured environments. Zohaib has contributed as both first and co-author to leading journals such as Computers and Electronics in Agriculture, Agronomy, Sensors, and IEEE Transactions on Industrial Electronics. Alongside research, he has extensive experience supervising student projects and developing real-time AI pipelines using Python, PyTorch, OpenCV, ROS, and C/C++. His academic excellence is recognized through multiple national and international awards, including innovation, debate, and research excellence honors. Overall, Zohaib Khan represents a strong blend of theoretical rigor and practical AI deployment, aiming to advance large-scale industrial and agricultural perception systems.

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Featured Publications

Ibrahim Khalil Kabir | Engineering and Technology | Best Researcher Award

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Mr. Ibrahim Khalil Kabir | Engineering and Technology | Best Researcher Award

King Fahd University of Petroleum and Minerals | Saudi Arabia

Ibrahim Khalil Kabir is a control and robotics researcher working at the intersection of control theory and artificial intelligence, with a strong focus on learning-based robotics, socially aware navigation, and human–robot interaction. He holds an MSc in Systems and Control Engineering and a BEng in Mechatronics Engineering, with a solid academic record and advanced training in autonomous systems. His research experience spans graduate teaching and research assistantships, where he contributed to robot path planning, navigation, and hands-on laboratory instruction using real robotic platforms. His scholarly output includes peer-reviewed journal and conference publications covering UAV control, mobile robot navigation, deep reinforcement learning, and socially aware robotic systems. According to Google Scholar, his research profile reflects an emerging h-index supported by multiple indexed documents and a steadily growing citation count, indicating increasing impact in robotics and intelligent control research. His work has appeared in reputable venues such as IEEE Access, Machine Learning and Knowledge Extraction, and IEEE conferences. He has received several academic honors, including national merit scholarships and highest GPA awards. Overall, his research trajectory demonstrates a strong foundation and growing influence in intelligent robotics, positioning him well for advanced doctoral research in learning-enabled autonomous systems.

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Featured Publications

Marius Šumanas | Computer Vision | Best Researcher Award

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Assist. Prof. Dr Marius Šumanas | Computer Vision | Best Researcher Award

Vilnius Tech | Lithuania

Dr. Marius Šumanas is an Associate Professor in the Department of Mechatronics, Robotics, and Digital Manufacturing at Vilnius Gediminas Technical University (VILNIUS TECH).  His research interests encompass robotics, machine learning, and digital manufacturing. Dr. Šumanas has authored several publications, including a study on improving positioning accuracy of articulated robots using deep Q-learning algorithms. He has also participated in various conferences, presenting topics related to machine learning applications in robotics. In addition to his academic roles, Dr. Šumanas has practical experience in the industry. Since 2018, he has been working as a Process Engineer and ERP Systems Business Analyst at MB Pramones algoritmas. He has also held positions in marketing and sales management in previous years. Dr. Šumanas’ work bridges the gap between theoretical research and practical application, contributing to advancements in robotics and digital manufacturing.

Profile : Orcid

Featured Publications

Andrijauskas, I., Šumanas, M., Dzedzickis, A., Tanaś, W., & Bučinskas, V. (2025). Computer vision-based optical odometry sensors: A comparative study of classical tracking methods for non-contact surface measurement. Sensors.

Šumanas, M., Treciokaite, V., Čerškus, A., Dzedzickis, A., Bučinskas, V., & Morkvenaite-Vilkonciene, I. (2022). Sitting posture monitoring using Velostat based pressure sensors matrix. In Smart Sensor Systems (pp. 1–12). Springer.

Bučinskas, V., Dzedzickis, A., Šumanas, M., Sutinys, E., Petkevicius, S., Butkiene, J., Virzonis, D., & Morkvenaite-Vilkonciene, I. (2022). Improving industrial robot positioning accuracy to the microscale using machine learning method. Machines, 10(10), 940.

Šumanas, M., Urbonis, D., Petronis, A., Stankaitis, S., Januškevičius, T., Iljin, I., & Dzedzickis, A. (2021). Evaluation of motion characteristics using absolute sensors. In Advances in Mechatronics and Intelligent Robotics (pp. 1–12). Springer.

Šumanas, M., Petronis, A., Urbonis, D., Januskevicius, T., Rasimavicius, T., Morkvenaite-Vilkonciene, I., Dzedzickis, A., & Bučinskas, V. (2021, April 22). Determination of excavator tool position using absolute sensors. In 2021 Open Conference of Electrical, Electronic and Information Sciences (eStream). IEEE.