題目：Research on Vehicle Automation and Artificial Intelligence at Berkeley DeepDrive, UC Berkeley – Challenges and Opportunities
報(bào)告人： Ching-Yao Chan (Research Professor, Associate Director, Berkeley DeepDrive, University of California at Berkeley, USA)
報(bào)告時(shí)間：2018 年 5 月 30 日，上午 10:00-11:30
報(bào)告地點(diǎn)：車(chē)輛重點(diǎn)實(shí)驗(yàn)樓 2 層報(bào)告廳
報(bào)告語(yǔ)言：英文/中文

報(bào)告內(nèi)容：

In this talk, the following topics will be covered:
?A brief introduction of connected and automated vehicles activities at California PATH (Partners of Advanced Transportation Technology) at UC Berkeley
?An overview of the Berkeley DeepDrive research center at UC Berkeley and its research activities
?Machine learning in automated driving systems
?Safety challenges of automated driving systems
?Opportunities for future research

The talk begins with a highlight of historical research activity as well as a review of recent and ongoing studies at California PATH, a world-renowned institution on intelligent transportation systems. The speaker will then provide an overview of the Berkeley DeepDrive consortium, which currently has more than 20 industrial partners and is focused on the application of deep learning technologies for automotive applications. The talk will then lead to the descriptions of several current research projects that address different aspects of automated driving. The speaker will then use some recent incidents of automated driving systems to illustrate the safety issues and challenges of automated driving in real-world driving. An interactive discussion with the audience will be held. As a conclusion of the talk, we will cover the future industrial trends and research topics that will help synergize the potential of artificial intelligence and autonomous driving.

報(bào)告人背景資料：

Ching-Yao Chan is a Research Professor at University of California, Berkeley. He serves as the Program Leader for Safety Research at California PATH (Partners for Advanced Transportation Technology) of Institute of Transportation Studies (ITS). He is also serving as Associate Director of Berkeley Deep Drive (BDD). BDD, which currently has more than 20 industrial partners, is a research center focusing on the application of deep learning technologies for intelligent dynamic systems, including autonomous driving. He obtained his doctoral degree from Berkeley in 1988 and worked in the private sectors before joining PATH in 1994. Since then, he has been involved in a variety of research projects.
He has 30 years of research experience spans from vehicle automation, driver-assistance systems, sensing and wireless communication technologies, to driver behaviors, vehicular safety, highway network safety assessment, machine learning technologies and their applications on automated driving systems. He has published more than 130 papers in various journals and conferences. With his nationally recognized expertise, he was invited by Society of Automotive Engineers (SAE) to provide tutorials in an SAE seminar series to more than 500 automotive professionals over a number of years. He also lectured extensively for various famous organizations. He was the recipient of the SAE Forest R. MacFarland Award for his outstanding contributions to engineering education. His project has also won the prestigious award of the Best of ITS Research Award from the ITS America Annual Meeting.

主辦單位：“新能源車(chē)輛及運(yùn)用”引智基地
                      特種車(chē)輛研究所
車(chē)輛傳動(dòng)國(guó)家重點(diǎn)實(shí)驗(yàn)室