Keynote Speech Information-Dong Seog HAN

1. Dean, College of IT Engineering, Kyungpook National University (KNU), South Korea
2. Professor, School of Electronic and Electrical Engineering, KNU, South Korea
3. Director, Center for ICT and Automotive Convergence

Biography: DONG SEOG HAN (Senior Member, IEEE) received his B.S. degree in electronic engineering from Kyungpook National University (KNU), Daegu, South Korea, in 1987 and M.S. and Ph.D. degrees in electrical engineering from the Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea, in 1989 and 1993, respectively. From 1987 to 1996, he was with Samsung Electronics Co., Ltd., where he developed the transmission system for the ATSC HDTV receiver. Since 1996, he has been a Professor at the School of Electronic and Electrical Engineering, KNU. He was a Courtesy Associate Professor at the Department of Electrical and Computer Engineering, University of Florida, in 2004. He was a Director at the Center of Digital TV and Broadcasting, Institute for Information Technology Advancement (IITA), from 2006 to 2008. He is currently the Director of the Center for ICT and Automotive Convergence, KNU. He is also the Dean of the College of IT Engineering, KNU. His main research interests include intelligent signal processing and autonomous vehicles.

Title: AI Technologies for Autonomous Driving

Abstract: Artificial intelligence technologies for autonomous driving refer to the core technologies that enable vehicles to drive autonomously, perceive their surroundings, and reach their destinations safely and efficiently. Artificial intelligence technologies for autonomous driving involve the convergence of various research and technologies from fields such as machine learning, computer vision, sensor technology, and natural language processing. In this talk, we will cover the following main AI component technologies for autonomous driving:

1. Driving Environment Awareness Technology: Autonomous vehicles use a variety of sensors to detect their surroundings. It utilizes LiDAR, radar, cameras, and ultrasonic sensors to determine the driving route and recognize surrounding conditions in real-time. It covers the detection of surrounding vehicles, obstacles, and roads, as well as predicting the movement of pedestrians.
2. Occupant Monitoring: For safe driving, autonomous vehicles monitor the emotions and behaviors of passengers through camera and voice analysis. We will cover emotion analysis technology that combines image processing and natural language understanding to monitor the emotions of the occupants.
3. Path Planning and SLAM (Simultaneous Localization and Mapping): Autonomous driving path planning involves determining the optimal driving path from the starting point to the destination, considering environmental information and the vehicle's status. SLAM is crucial for path planning, as it enables the vehicle or robot to simultaneously detect the surroundings, estimate its position, and construct a map of the environment.

Through this talk, you will be able to quickly learn the development trends and element technologies of artificial intelligence algorithms and autonomous driving systems.