Our research aims to develop autonomous systems that are designed with humans in mind. We investigate how to incorporate human preferences, behavior, psychology and decision-making processes into the design of intelligent transportation systems, including autonomous vehicles and traffic management systems to improve safety, performance, and reliability.

LACITS investigates the design of distributed control and estimation algorithms for large-scale modular systems. Our focus is on developing systems that can adapt to uncertain and changing environments while maintaining safety and performance and ensuring resilience.

Our research focuses on developing secure and resilient control systems for connected autonomous vehicles. We address challenges such as system failures, cybersecurity threats, and uncertainties related to system characteristics. Our goal is to design intelligent control and decision-making systems that prioritize human-vehicle interaction, improving active safety and collision avoidance while maintaining efficiency. We strive to create autonomous transportation systems that are secure, reliable, and provide a seamless user experience.

Our lab's research aims to improve the decision-making, motion planning, and control of unmanned vehicles and mobile robots in harsh and complex environments. We apply machine learning techniques to achieve optimal control and develop intelligent control systems that can handle uncertainties related to sensor fusion, object classification, and rough-terrain navigation. Our focus is on developing autonomous systems that can learn from experience and adapt to changing environments through reinforcement learning. Additionally, we explore cooperative planning and multi-agent control to improve overall system performance and efficiency.