Convoluted Gaussian Process (CGP): An Alternative to Facilitate Analysis and Predictions of Multiple DPMs under Several Driving Conditions Using Driving Simulators
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
Language
- English
Project
- Status: Completed
- Funding: $39500
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Contract Numbers:
69A3551747131
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Sponsor Organizations:
Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590 -
Managing Organizations:
University of Iowa, Iowa City
National Advanced Driving Simulator, 2401 Oakdale Blvd
Iowa City, IA United States 52242-5003 -
Performing Organizations:
University of Iowa, Iowa City
National Advanced Driving Simulator, 2401 Oakdale Blvd
Iowa City, IA United States 52242-5003 -
Principal Investigators:
Wang, Chao
McGehee, Daniel
Brown, Timothy
- Start Date: 20200901
- Expected Completion Date: 20210831
- Actual Completion Date: 0
- USDOT Program: University Transportation Centers
Subject/Index Terms
- TRT Terms: Accuracy; Driving simulators; Lane occupancy; Operating speed; Performance measurement; Validation
- Subject Areas: Highways; Planning and Forecasting; Safety and Human Factors;
Filing Info
- Accession Number: 01738852
- Record Type: Research project
- Source Agency: Safety Research Using Simulation University Transportation Center (SaferSim)
- Contract Numbers: 69A3551747131
- Files: UTC, RIP
- Created Date: May 7 2020 8:11AM