Cai Xuan | Engineering and Technology | Research Excellence Award

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)

Ibrahim Khalil Kabir | Engineering and Technology | Best Researcher Award

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