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Accuracy of a Driver Model with Nonlinear AutoregRessive with eXogeous Inputs (NARX)
Technical Paper
2018-01-0504
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Most driving assist systems are uniformly controlled without considering differences in characteristics of individual drivers. Drivers may feel discomfort, nuisance, and stress if the system functions differently from their characteristics. The present study reduced these side effects for systems with a highly accurate driver model. The model was constructed using Nonlinear AutoregRessive with eXogeous inputs (NARX), which has a learning function and estimates the driving action of a driver. The model was constructed for one driving condition yet can be applied to other driving conditions. If one model can be applied to many driving conditions, a system can construct as minimum requirements. The driver decelerated while approaching the target at the tail of a traffic jam on a highway. A driver model was constructed for the driver’s braking action. The experimental condition was 11 data measurements from 50 to 130 km/h made at intervals of 10 km/h. A model was constructed with 1-10 data. Analysis clarifies the number of data points needed to construct the model. The accuracy of the model was confirmed from 50 to 130 km/h at intervals of 10 km/h. Analysis clarifies on model accuracy when there is velocity difference. The accuracy of the model improved as the volume of learning data increased. The relationship between the volume of data and the model accuracy was clarified. The accuracy decreased as the difference in velocity increased, and this tendency was more obvious when the subject vehicle travelled at low velocity.
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Citation
Miyata, A., Gokan, M., and Hirose, T., "Accuracy of a Driver Model with Nonlinear AutoregRessive with eXogeous Inputs (NARX)," SAE Technical Paper 2018-01-0504, 2018, https://doi.org/10.4271/2018-01-0504.Data Sets - Support Documents
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References
- Hirose , T. , Sawada , T. , and Oguchi , Y. 2004
- Sawada , N. , Hirose , T. , and Kasuga , N. A Study on Modeling of driver’s Braking Action with NARX Neural Networks 22nd ITS World Congress 2015 2015
- Sawada , N. , Hirose , T. , and Kasuga , N.
- Institute for Traffic Accident Research and Data Analysis (ITARDA) http://www.itarda.or.jp/
- Hirose , T. , Gokan , M. , Kasuga , N. , and Sawada , T. A Study on Modeling of Driver’s Braking Action to Avoid Rear-End Collision with Time Delay Neural Network SAE Technical Paper 2014-01-0201 2014 10.427/2014-01-0201
- Xie , H. , Tang , H. , and Liao , Y.-h. Time Series Prediction Based on NARX Neural Networks: An Advanced Approach IEEE Proceedings of the Eighth International Conference on Machine Learning and Cybernetics 1275 1279 2009 10.1109/ICMLC.2009.5212326