Assistant Professor, Institute of Science Tokyo, Japan
Dr. A. S. M. Bakibillah is a distinguished researcher in Mechatronics Engineering, specializing in Intelligent Transportation Systems (ITS). With a strong academic foundation and a passion for sustainable mobility solutions, he has contributed extensively to eco-driving strategies, intelligent vehicle control, and cyber-physical frameworks for connected and automated vehicles. His research integrates machine learning, optimization, and control strategies to enhance energy efficiency and safety in transportation.
Publication Profile
🎓 Education
Dr. Bakibillah earned his Ph.D. in Mechatronics Engineering from Monash University, Australia, in collaboration with Tokyo Institute of Technology, Japan, focusing on intelligent vehicle control strategies for cooperative eco-driving 🚗🌿. He completed his M.Sc. in Information Technology (INFOTECH) from the University of Stuttgart, Germany, specializing in Micro and Optoelectronics, where he worked on silicon tunnel field-effect transistors 🏭🔬. His academic journey began with a B.Sc. in Electrical and Electronic Engineering from Rajshahi University of Engineering and Technology (RUET), Bangladesh, where he specialized in control systems and designed a temperature sensor-based speed controller for induction motors ⚡🛠️.
💼 Experience
Dr. Bakibillah has a dynamic research background in intelligent transportation and control systems, with extensive experience in academia and industry. His work has focused on eco-driving optimization, vehicle trajectory planning, smart parking solutions, and cyber-physical systems for transportation 🚦🔍. He has collaborated with top institutions in Australia, Japan, and Germany, contributing to cutting-edge innovations in automated driving and vehicle intelligence.
🏆 Awards and Honors
Dr. Bakibillah has received numerous prestigious awards, including the Monash International Postgraduate Research Scholarship (MIPRS) 🏅, the Monash Merit Certificate for Engineering Entrepreneurship 🏆, and the Monash School of Engineering Publication Award (SEPA) 🎖️. He was also honored with the Society of Instrument and Control Engineers (SICE) Student Travel Grant and International Award ✈️📜.
🔬 Research Focus
His research primarily revolves around sustainable transportation and intelligent mobility solutions 🚘💡. He specializes in energy-efficient eco-driving, cooperative automated vehicle control, and cyber-physical frameworks for traffic optimization. His work bridges the gap between artificial intelligence, control systems, and sustainable mobility, significantly impacting the future of intelligent transportation.
🔚 Conclusion
Dr. A. S. M. Bakibillah is a dedicated researcher in ITS, advancing the frontiers of smart mobility and eco-friendly driving solutions 🌍🚦. His innovative research, academic excellence, and multiple high-impact publications make him a valuable contributor to the field of intelligent vehicle technologies.
📚 Publications
Advancements and Challenges of Visible Light Communication in Intelligent Transportation Systems: A Comprehensive Review – Photonics, 2025, Cited by 📑
Cooperative Look-ahead Lane Change System for Improving Driving Intelligence of Automated Vehicles in Critical Scenarios – IEEE Transactions on Intelligent Vehicles, 2024, Cited by 📑
Electric Vehicle Eco-Driving Strategy at Signalized Intersections Based on Optimal Energy Consumption – Journal of Environmental Management, 2024, Cited by 📑
Optimal Eco-Driving Scheme for Reducing Energy Consumption and Carbon Emissions on Curved Roads – Heliyon, 2024, Cited by 📑
Eco-Driving on Hilly Roads in a Mixed Traffic Environment: A Model Predictive Control Approach – Actuators, 2024, Cited by 📑
Eco-Friendly Smart Car Parking Management System with Enhanced Sustainability – Sustainability, 2024, Cited by 📑
Robust Vehicle Mass Estimation Using Recursive Least M-Squares Algorithm for Intelligent Vehicles – IEEE Transactions on Intelligent Vehicles, 2023, Cited by 📑
A Cyber-Physical Framework for Optimal Coordination of Connected and Automated Vehicles on Multi-Lane Freeways – Sensors, 2023, Cited by 📑
Robust Estimation of Traffic Density with Missing Data using an Adaptive-R Extended Kalman Filter – Applied Mathematics and Computation, 2022, Cited by 📑
Autonomous Vehicle Overtaking: Modeling and an Optimal Trajectory Generation Scheme – Sustainability, 2022, Cited by 📑