Seyhan Ucar | Connected Vehicle | Best Researcher Award

Posted on by Academic achievements

Dr. Seyhan Ucar | Connected Vehicle | Best Researcher Award

Dr. Seyhan Ucar | Connected Vehicle – Associate Professor at InfoTech Labs, Toyota Motor North America R&D

Dr. Seyhan Ucar is a pioneering researcher in the field of intelligent transportation systems and connected vehicle technologies, with a focus on vehicular networking, communication protocols, and human-machine interaction. With over 10 years of professional experience, Dr. Ucar has led groundbreaking projects that bridge the gap between advanced research and real-world applications. His expertise encompasses developing connected vehicle services for future transportation technologies and improving vehicular communication and safety systems. Dr. Ucar’s work has earned him multiple accolades, and he has been recognized as a finalist for prestigious awards, such as the IEEE Intelligent Transportation Society Young Researcher/Engineer Award.

Professional Profile

Google Scholar

Education

Dr. Ucar holds a Ph.D. in Computer Science and Engineering from Koç University in Istanbul, Turkey, where he explored visible light communication and its applications in secure vehicular networks. His thesis, titled “Visible Light Communication Assisted Secure/Efficient Architecture for Platoon,” reflects his commitment to exploring innovative solutions for enhancing vehicular communication. He also earned a Master’s degree in the same field, where he researched multi-hop cluster architectures for vehicular ad hoc networks. His academic foundation is supported by a Bachelor’s degree in Computer Science and Engineering from Izmir Institute of Technology.

Experience

Dr. Ucar’s professional journey spans both academia and industry, demonstrating his versatility as a researcher and a leader. He currently serves as a Principal Researcher at InfoTech Labs, Toyota Motor North America R&D, where he oversees several research projects, including Vehicular Micro Clouds, Anomaly Driving Detection, and the development of human-machine interaction models. His role involves managing teams of engineers, researchers, and collaborating with university partners. Prior to this, Dr. Ucar was a researcher at Toyota InfoTechnology Center, U.S.A., and a postdoctoral researcher at Koç University. His experience also includes founding and leading a start-up, Mobifique LTD, which developed award-winning mobile applications for fitness tracking.

Research Interests

Dr. Ucar’s research interests focus on connected vehicle technologies, intelligent transportation systems, vehicular networking, and communication protocols. His work includes the development of innovative solutions for safe driving, real-time anomaly detection, and improving human factors in vehicle interactions. Dr. Ucar has pioneered research into vehicular micro clouds and edge-assisted driving behavior detection, with applications ranging from driver safety to vehicle-to-vehicle communication. His focus on practical solutions for real-time deployment distinguishes his approach from purely theoretical studies in the field.

Awards

Dr. Ucar’s work has been widely recognized in the academic and professional communities. He has received several Best Paper, Best Demo, Best Poster, and Best Mobile Application awards throughout his career. In 2022, he was awarded the ITS World Congress Best Scientific Paper Award and the IEEE Vehicular Networking Conference Best Demo Award. His accomplishments have earned him recognition as a finalist for the IEEE Intelligent Transportation Society Young Researcher/Engineer Award. Additionally, Dr. Ucar has been promoted to Editor-At-Large for the IEEE Open Journal of the Communications Society, a testament to his contributions to the field of vehicular, aerial, and satellite communications.

Publications

“Human Factor Study: Understand Driver Behavior in Response to Unsafe Follower Vehicles” (ITS World Congress, 2025)
“Aggressive Driving Detection: From Simulation and Data Analysis to Proof-of-Concept Trials” (IEEE Vehicular Networking Conference, 2025)
“Tailgating Behavior Detection On Rear Vehicles” (IEEE International Conference on Intelligent Transportation Systems, 2023) 🚗
“Field Experiments: Rear Vehicle Behavior Awareness to Avoid Rear-End Collisions” (IEEE International Conference on Sensing, Communication, and Networking, 2023) 🛣️
“Proactive Management of Drivers When Impacted by Aggressive Driving” (IEEE Vehicular Networking Conference, 2025) 🚓

Conclusion

Dr. Seyhan Ucar is an exceptional researcher whose work continues to shape the future of connected vehicles and intelligent transportation systems. His leadership in both academic and industrial research, combined with his numerous awards and publications, positions him as a strong contender for the Best Researcher Award. Dr. Ucar’s ability to bridge theoretical research with practical applications, as demonstrated in his real-time field trials and patented technologies, makes his contributions invaluable to the advancement of automotive and transportation technologies. He remains a thought leader in his field, with a commitment to safety, innovation, and human-centered design in vehicular technology.

Mate Fazekas | Autonomous Vehicles | Best Researcher Award

Posted on by Academic achievements

Mr. Mate Fazekas | Autonomous Vehicles | Best Researcher Award

Ph.D. student at HUN-REN Institute for Computer Science and Control (SZTAKI), Hungary

Máté Fazekas is a dedicated researcher and senior software developer based in Budapest, Hungary. Since 2017, he has contributed significantly as a research associate at the HUN-REN Institute for Computer Science and Control, focusing on state estimation and model identification for autonomous vehicles. From 2022 to 2024, he expanded his expertise as a senior software developer at HUMDA Lab Kft., specializing in developing control systems for autonomous racecars. Currently pursuing a PhD in Robotics at the Budapest University of Technology and Economics, Máté’s research centers on model calibration for autonomous vehicles integrating machine learning techniques. He holds an MSc in Electrical Engineering and a BSc in Mechatronics Engineering from the same institution, both awarded with highest honors. Proficient in languages including Hungarian and English, he excels in programming languages like Matlab, Python, C/C++, and LabVIEW, complemented by strong skills in CAD software and Microsoft Office applications.

Professional Profiles

Education

Máté Fazekas pursued his academic journey at the Budapest University of Technology and Economics, achieving academic excellence throughout. He completed his Bachelor of Science in Mechatronics Engineering from the Faculty of Mechanical Engineering, graduating summa cum laude in 2017. His undergraduate thesis focused on the development of an automated parking system. Subsequently, he earned his Master of Science in Electrical Engineering and Informatics from the Faculty of Electrical Engineering and Informatics, also graduating summa cum laude in 2019. His master’s thesis centered on state and parameter estimation for car-like robots. Currently, he is continuing his academic pursuits as a PhD student in Robotics at the Faculty of Vehicle Engineering, where his research involves model calibration for autonomous vehicles integrating machine learning techniques.

Professional Experience

Máté Fazekas has gained valuable experience in both research and software development roles. Since December 2017, he has served as a Research Associate at the HUN-REN Institute for Computer Science and Control in Budapest, focusing on state estimation and model identification for autonomous vehicles. Concurrently, from June 2022 to June 2024, he worked as a Senior Software Developer at HUMDA Lab Kft., specializing in the development of control systems for autonomous racecars. His expertise includes integrating GNSS, IMU, and odometry for vehicle localization, along with developing advanced software solutions for autonomous systems.

Research Interest

Máté Fazekas, a Budapest-based researcher and Senior Software Developer at HUMDA Lab Kft., specializes in robotics and autonomous systems. Since 2017, he has contributed significantly as a research associate at the HUN-REN Institute for Computer Science and Control, focusing on state estimation and model identification for autonomous vehicles using GNSS, IMU, and odometry technologies. His current role involves developing control systems for autonomous racecars, enhancing their performance and automation capabilities. Educationally, Máté is pursuing a PhD in Robotics at Budapest University of Technology and Economics, exploring model calibration for autonomous vehicles with machine learning integration. He holds a master’s degree in Electrical Engineering and Informatics, where his research focused on state and parameter estimation for robotic systems, and a bachelor’s degree in Mechatronics Engineering, emphasizing automated parking systems. His technical skills include proficiency in Matlab, Python, C/C++, and LabVIEW, as well as CAD and FEM tools like SolidWorks, Inventor, and Ansys.

Research skills

Mate Fazekas is proficient in multiple languages, with Hungarian as his mother tongue and English at an intermediate B2 level. His computer skills include intermediate proficiency in Microsoft Office tools such as Word, Excel, PowerPoint, Access, and Project. He also possesses intermediate skills in Computer-Aided Design (CAD) and Finite Element Method (FEM) software like SolidWorks, Inventor, and Ansys. In programming, Mate demonstrates intermediate proficiency in languages such as C/C++ and LabVIEW, along with advanced skills in MATLAB and intermediate proficiency in Python. These skills equip him well for his research and development roles, particularly in robotics and autonomous vehicle technologies.

Publications

  1. Wheel odometry model calibration with neural network-based weighting
    • Authors: Fazekas, M., Gáspár, P.
    • Journal/Conference: Engineering Applications of Artificial Intelligence, 2024
    • Citations: 0
  2. LPV-Based Control Design with Guarantees: a Case Study for Automated Steering of Road Vehicles
    • Authors: Nemeth, B., Fazekas, M., Bagoly, Z., Gaspar, P., Sename, O.
    • Conference: European Control Conference, ECC 2023, 2023
    • Citations: 0
  3. Calibration of the Nonlinear Wheel Odometry Model with an Improved Genetic Algorithm Architecture
    • Authors: Fazekas, M., Németh, B., Gáspár, P.
    • Conference: Proceedings of the International Conference on Informatics in Control, Automation and Robotics, 2022
    • Citations: 0
  4. Vehicle Control with Cloud-aided Learning Feature: an Implementation on Indoor Platform
    • Authors: Németh, B., Antal, Z., Marosi, A.C., Fazekas, M., Gáspár, P.
    • Journal: IFAC-PapersOnLine, 2022
    • Citations: 1
  5. Wheel Odometry Model Calibration with Input Compensation by Optimal Control
    • Authors: Fazekas, M., Gáspár, P., Németh, B.
    • Journal: IFAC-PapersOnLine, 2022
    • Citations: 0
  6. Calibration of Front Wheel Odometry Model
    • Authors: Fazekas, M., Gáspár, P., Németh, B.
    • Book: Lecture Notes in Mechanical Engineering, 2022
    • Citations: 0
  7. Implementation of a variable-geometry suspension-based steering control system
    • Authors: Fényes, D., Fazekas, M., Németh, B., Gáspár, P.
    • Journal: Vehicle System Dynamics, 2022
    • Citations: 6
  8. Parameter Identification of the Nonlinear Wheel Odometry Model with Batch Least Squares Method
    • Authors: Fazekas, M., Gáspár, P., Németh, B.
    • Conference: Conference on Control and Fault-Tolerant Systems, SysTol, 2021
    • Citations: 1
  9. Velocity estimation via wheel circumference identification
    • Authors: Fazekas, M., Gáspár, P., Németh, B.
    • Journal: Periodica Polytechnica Transportation Engineering, 2021
    • Citations: 1
  10. Improving the wheel odometry calibration of self-driving vehicles via detection of faulty segments
    • Authors: Fazekas, M., Gaspar, P., Nemeth, B.
    • Conference: IEEE International Conference on Automation Science and Engineering, 2021
    • Citations: 0