Technical Paper
Accuracy of a Driver Model with Nonlinear AutoregRessive with eXogeous Inputs (NARX)
2018-04-03
2018-01-0504
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.
