Accuracy of a Driver Model with Nonlinear AutoregRessive with eXogeous Inputs (NARX) Shibaura Institute of Technology

Format:

Conference/Event

Author/Creator:

Miyata, Miyata, author.

Contributor:

Gokan, Masato

Hirose, Toshiya

Conference Name:

WCX World Congress Experience (2018-04-10 : Detroit, Michigan, United States)

Language:

English

Physical Description:

1 online resource

Place of Publication:

Warrendale, PA SAE International 2018

Summary:

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 130km/h made at intervals of 10km/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 130km/h at intervals of 10km/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

Notes:

Vendor supplied data

Publisher Number:

2018-01-0504

Access Restriction:

Restricted for use by site license