Convoluted Gaussian Process (CGP): An Alternative to Facilitate Analysis and Predictions of Multiple DPMs under Several Driving Conditions Using Driving Simulators
| 项目名称： | Convoluted Gaussian Process (CGP): An Alternative to Facilitate Analysis and Predictions of Multiple DPMs under Several Driving Conditions Using Driving Simulators |
| 摘要： | 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 |
| 状态： | Active |
| 资金： | 39500 |
| 资助组织： | Office of the Assistant Secretary for Research and Technology |
| 执行机构： | University of Iowa, Iowa City |
| 开始时间： | 20200901 |
| 预计完成日期： | 20210831 |
| 主题领域： | Highways；Planning and Forecasting；Safety and Human Factors |
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