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This project aims at modeling the interactions among different driving performance measures (DPMs), e.g., standard deviation of lateral position (SDLP) and driving speed, under various driving conditions. The hypothesis is that different DPMs interact with each other and the successful modeling of such interactions could greatly improve the prediction accuracy and reduce the variability of DPMs at untried driving conditions. The project would use driving simulators data to train and test the proposed DPM interaction model, where the DPM prediction accuracy would be evaluated and compared with various alternatives, e.g., generalized linear model, to validate the effectiveness of the proposed model