Many automated driving technologies have been developed and are continuing to be
implemented for practical use. Among them a driver model is used in automated
driving and driver assistance systems to control the longitudinal and lateral
directions of the vehicles that reflect the characteristics of individual
drivers. To this end, personalized driver models are constructed in this study
using long short-term memory (LSTM). The driver models include individual
driving characteristics and adapt system control to help minimize discomfort and
nuisance to drivers. LSTM is used to construct the driver model, which includes
time-series data processing. LSTM models have been used to investigate
pedestrian behaviors and develop driver behavior models in previous studies. We
measure the driving operation data of the driver using a driving simulator (DS).
The road geometry of an actual section of the Tomei Expressway, which comprises
straight and curved roads, between Tokyo and Nagoya in Japan was simulated in
the DS. Personalized driver models were constructed using LSTM based on the data
of driving maneuvers on the expressway. Simulation results indicate that model
accuracy decreases for the entire experimental road compared to that for each
curved road; the model accuracy of each curved road was improved. In order to
improve the accuracy, it is effective to build a model for each curve or
section, and the accuracy is lower at the exit than at the entrance of the
curve, and highest at the middle. And then it is necessary to consider both the
time required to improve accuracy and the change in curvature when considering
the construction of personalized models on curved roads.
