Research

Safe driving is a core technology in autonomous vehicles. We focus on trajectory planning and tracking control algorithms to optimize vehicle behavior. These algorithms are designed with considerations for ride comfort, safety, and cost, contributing to the advancement of autonomous driving systems and the realization of smart mobility.

With technological advancements, research on smart mobility beyond traditional transportation is essential. We study intelligent transportation systems to create more convenient, safe, and sustainable mobility solutions.

At SMOC Lab, we research advanced control theories that are applicable not only to mobility but also to various industrial fields. We focus on low-cost sensor-based control, high-precision control performance, and robust control systems, contributing to advancements across multiple industries.

We research modeling techniques for new systems, including control-oriented modeling, to enable mathematical and physical analysis. Our work not only contributes to academia but also serves as the foundation for developing innovative control strategies and functionalities.

Due to cost and hardware limitations, certain states or parameters in systems cannot be directly measured. We design estimation algorithms using mathematical techniques and learning-based approaches to infer such information. Additionally, we research sensor fusion and system fusion algorithms to address the needs of intelligent systems.