•   When: Tuesday, November 29, 2022 from 05:00 PM to 07:00 PM
•   Speakers: Yongxin Wang
•   Location: Virtual - Zoom
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The safety assurance is one of the basic issues in modern transportation. According to the World Health Organization(WHO), there were 1.35 million deaths as a result of road traffic crashes worldwide each year. A recent vehicle crash causation study by NHTSA found that human errors are the critical reasons in 94% of the crashes. The emergence of autonomous driving systems and Vehicle-to-Everything (V2X) communication could drastically reduce the number of crashes and fatalities that occur on the roads today.

This dissertation focuses on improving the safety in transportation systems, like connected and autonomous vehicles. The first part examines how to predict the failure cases of the perception systems in autonomous vehicles. Knowing when the perception system will fail is a necessity to find alternate control methods so that crashes can be mitigated for both self-driving and human driven vehicles. The second part of this work addresses the viability of using collaborative perception to mitigate potential road risks by enabling Non-Line-Of-Sight (NLOS) obstacle detection. While the majority of previous research is based on simulation, I have conducted experiments using on-board devices in real traffic scenarios that have communication latency and uncertainty.  In addition, I have created large corpus of multi-modal multi-weather driving data to facilitate research in autonomous and connected vehicles perception.