INHA University
Published on 4 March 2026
▲Schematic diagram illustrating the overall processing flow in which two input data sources—machine learning models and computer vision—are fused through a Bayesian filter.
Graduate students Lee Dong-min and Lee Seung-yong, master’s candidates in the Department of Mechanical Engineering at Inha University, have received international recognition for their research on improving road surface condition classification performance for autonomous vehicles.
Through their paper titled “Multimodal Bayesian Filtering for Enhanced Road Surface Condition Classification in Intelligent Vehicles Safety Systems,” the researchers proposed a novel algorithm for road surface condition classification.
The core contribution of the study lies in significantly improving classification accuracy by fusing data-driven machine learning models with computer vision–based road surface classification results using a real-time Bayesian filtering approach.
This research has been accepted for publication at the IEEE Intelligent Vehicles Symposium (IV) 2026 International Conference, scheduled to be held this June in Detroit, USA. IEEE IV is one of the most prestigious international conferences in the fields of autonomous driving and intelligent vehicles, known for its rigorous review process and strong emphasis on academic originality and impact.
Professor Kiwoo Kim of the Department of Mechanical Engineering at Inha University commented, “I hope this achievement serves as an opportunity for master’s students in mechanical engineering to gain confidence that they, too, can present research at leading conferences in the electrical and electronics engineering field, and that it will support their future career development.”
Meanwhile, this research was conducted with support from a national R&D project funded by the Ministry of Trade, Industry and Energy, titled “Development of an Embedded AI Controller for Road Surface Condition Recognition” (lead company: HL Mando), under the supervision of Professor Kiwoo Kim as principal investigator. Follow-up studies are planned.
