Vehicle Intelligence

This course introduces the fundamental theories and algorithms related to perception, decision-making, and control, which are core components of autonomous driving systems. In the perception module, students will explore data acquisition using camera and LiDAR sensors, and learn about AI-based object detection technologies. The course also covers sensor fusion techniques, focusing on the integration of radar, camera, and LiDAR data. For decision-making, both physics-based approaches and AI-based methods will be introduced and compared, providing students with insight into traditional and modern strategies for motion planning. In the control section, the course presents classical PID control techniques as well as recent advancements using reinforcement learning to demonstrate the evolving landscape of autonomous vehicle control systems.

Basic Design in Mobility Engineering

This course aims to improve capability to solve introductory engineering problems with creative problem solving procedure. Through individual home works, labs, and team projects, students will experience how effectively the creative problem solving procedure can be applied to the mobility engineering problem. This course will be a first step to lead the students to become an engineer who has a full of creativity as well as professional knowledge.

Experiments on Mobility Basics

This lab provides hands-on experience in data generation, processing, and visualization for applying artificial intelligence to autonomous driving. Students will learn importance of data preprocessing by comparing time-series datasets from both simulations and real vehicles. They will also practice implemening data generation and processing techniques using major open-source autonomous driving simulators. The course builds foundational skills for integrating AI technologies.

Automatic Control

This course covers the fundamental theories necessary for the analysis and design of automatic control systems. Topics include mathematical modeling of control systems, analysis of system response characteristics, configuration and behavior of feedback control systems, and stability analysis of closed-loop systems. Based on these foundations, students will learn to interpret the behavior of control systems. The course also introduces various controller design techniques in the time and frequency domains, such as root locus and Bode plots. Through design assignments using MATLAB, students will develop practical skills in controller design.