Journal article
Modeling and prediction of driving performance measures based on multi-output convolutional Gaussian process
Accident analysis and prevention, Vol.161, pp.106360-106360
10/2021
DOI: 10.1016/j.aap.2021.106360
Abstract
•The modeling and prediction of the relationship between driving performance measures and driving conditions are studied under baseline (controlled) driving scenarios.•The proposed framework facilitates the modeling of interactions and uncertainties among multiple driving performance measures; thus the prediction accuracy is significantly improved.•The framework has a rigorous statistics structure, at the same time provides clear interpretations about the features in each driving performance measure and their interactions.•The method is compared with various benchmark methods and validated through case studies using baseline driving data in two driving simulation platforms.
Driving performance measures (DPMs) are important indices for driving and personal safety in vehicle operation. The DPMs are collected under various controlled driving conditions to demonstrate different driving behaviors so that mitigating technology interventions can be studied and designed. However, significant costs are involved in the DPM acquisition, and there are a very limited number of controlled driving condition data. Thus, the modeling and prediction of the DPMs under unobserved driving conditions are critical, and many methods have been developed. However, existing literature in this area suffer a common limitation: The interactions among different DPMs are not fully considered (each DPM is modeled individually), although the existence of such interactions is widely reported. This paper proposes a novel DPM modeling and prediction method, i.e., multi-output convolutional Gaussian process (MCGP), that incorporates the interactions among different DPMs. The method features the modeling flexibility for different DPMs and the interpretable modeling structure for integrating the DPM interactions. The method is compared with three benchmark methods on the DPM data set under four different settings, and the results demonstrate the superiorities of the method. Discussions and interpretations of the results are also provided.
Details
- Title: Subtitle
- Modeling and prediction of driving performance measures based on multi-output convolutional Gaussian process
- Creators
- Pranaykumar Kasarla - University of IowaChao Wang - University of IowaTimothy L Brown - University of IowaDaniel McGehee - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Accident analysis and prevention, Vol.161, pp.106360-106360
- DOI
- 10.1016/j.aap.2021.106360
- ISSN
- 0001-4575
- eISSN
- 1879-2057
- Publisher
- Elsevier Ltd
- Language
- English
- Date published
- 10/2021
- Academic Unit
- Occupational and Environmental Health; Emergency Medicine; Pharmaceutical Sciences and Experimental Therapeutics; Driving Safety Research Institute; Industrial and Systems Engineering; Center for Social Science Innovation; Injury Prevention Research Center; Public Policy Center (Archive)
- Record Identifier
- 9984186586502771
Metrics
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