Abstract

Each year, more than one million people around the world lose their lives due to road accidents, making them a significant contributor to non-natural deaths worldwide. To tackle this issue, intelligent driving systems (IDS) have made considerable progress. However, evaluating IDS in real-world scenarios is challenging due to the complexity and rarity of safety-critical situations. To address this challenge, I will present our simulation-base efforts, which involves safety-critical scenario generation and risk identification benchmark. Finally, I will discuss the open questions surrounding the use of simulations in the development and evaluation of IDS.

Bio.

Yi-Ting Chen is an assistant professor in the Department of Computer Science at National Yang Ming Chiao Tung University. He received his Ph.D. degree from the Department of Electrical and Computer Engineering at Purdue University in 2015 and a B.S. degree in Electronics Engineering from National Chiao Tung University in 2009. He was a senior scientist at Honda Research Institute USA from 2015 to 2020. He was visiting researcher at Google ATAP in 2015 and a computer vision researcher at the University of California at Merced in 2014. His research interests lie in computer vision, machine learning, robotics, and behavior science, and their applications to intelligent systems. He is a recipient of Junior Faculty Award (豐達科技青年講座) from NYCU in 2021.

All faculty and students are welcome to join.