School of Engineering Department of Civil and Environmental Engineering 86 Analysis of Compound Extreme Using Cmip6 Multi-Model Ensemble Supervisor: IM Eun Soon / CIVL Student: ZHOU Zixuan / EVMT Course: UROP1100, Spring The demand for accurate climate projections has grown significantly because people realized the importance of projecting climate change impacts for societal adaptation. However, climate models have systematic bias, hindering people from directly using them for impact analysis. Bias correction is widely used to produce unbiased climate projections, including component-wise and direct bias correction. This project focuses on bias correction for heat-stress indicators in South Korea using multiple regional climate model simulations. We will compare five bias correction methods in this study, including four univariate (direct correction) and one multivariate (component-wise bias correction) approach, for correcting two heat-stress indices, Wetbulb Globe Temperature (WBGT) and Apparent Temperature (AT). These two indices have different intervariable dependencies between temperature and relative humidity. Specifically, I was responsible for producing the Quantile Delta Mapping (QDM) for Wet Bulb Temperature. I went through two stages. Firstly, I remapped the historical simulation (1979-2014) and future simulation (2015-2100) based on time interpolation to resolve non-standard calendar and spatial interpolation to the ERA5 grid. After that, I applied QDM to future simulations based on the error calculated between ERA5 and historical simulation. Characterizing Driving Behaviors of Human Drivers When Following Automated Vehicles Using The Realworld Dataset Supervisor: JIAN Sisi / CIVL Student: CHANG Sin Tong / CIVL Course: UROP1100, Spring It is apparent that the usage of automated vehicles on road are increasing from the trend of market growth. Before the replacement of human-driven vehicles (HVs) by automated vehicles (AVs), there exists a transition period where there are mixed traffic streams composing of both AVs and HVs. This presents a research opportunity as the interactions between AVs and HVs, primarily the changes in behavior of HVs when following AVs have not been fully established and understood. This is particularly important as comprehensive understanding will be required to ensure the design of AVs driving algorithms are adequate in creating a safe driving environment for road users. A primary goal is to increase safety by the introduction of AVs on the road.